Population Based Algorithm

The Artificial Bee Colony (ABC) algorithm is widely used to achieve optimum solution in a short time in integer-based optimization problems. However, the complexity of integer-based problems such as Knapsack Problems (KP) requires robust algorithms to avoid excessive solution search time. ABC algorithm that provides both the exploitation and the exploration approach is used as an alternative approach for various KP problems in the literature. However, it is rarely used for the Single Container Loading problem (SCLP) which is an important part of the transportation systems. In this study, the exploitation and exploration aspects of the ABC algorithm are improved by using memory mechanisms and genetic operators to develop three different hybrid ABC algorithms. The developed algorithms and the basic ABC algorithm are applied to a SCLP dataset from the literature to observe the effects of the memory mechanism and the genetic operators separately. Besides, a joint hybrid ABC algorithm using both reinforcement approaches is proposed to solve the SCLP. The results show that the joint hybrid ABC algorithm has emerged as a promising approach to solving SCLP with an average performance, and the genetic operators are more effective than the memory mechanism to develop a hybrid ABC algorithm.

The Artificial Bee Colony (ABC) algorithm is widely used to achieve optimum solution in a short time in integer-based optimization problems. However, the complexity of integer-based problems such as Knapsack Problems (KP) requires robust algorithms to avoid excessive solution search time. ABC algorithm that provides both the exploitation and the exploration approach is used as an alternative approach for various KP problems in the literature. However, it is rarely used for the Single Container Loading problem (SCLP) which is an important part of the transportation systems. In this study, the exploitation and exploration aspects of the ABC algorithm are improved by using memory mechanisms and genetic operators to develop three different hybrid ABC algorithms. The developed algorithms and the basic ABC algorithm are applied to a SCLP dataset from the literature to observe the effects of the memory mechanism and the genetic operators separately. Besides, a joint hybrid ABC algorithm using both reinforcement approaches is proposed to solve the SCLP. The results show that the joint hybrid ABC algorithm has emerged as a promising approach to solving SCLP with an average performance, and the genetic operators are more effective than the memory mechanism to develop a hybrid ABC algorithm.

There is a trend in the scientific community to create and solve optimization models for complex problems based on the life in nature, one of which is Artificial Bee Colony Algorithm (Algorithm ABC). This paper discussed the ABC Algorithm to solve CBT (Curriculum-Based Course Timetabling) problems in the ITC-2007 (International timetabling Competition -2007). The composition of the ABC algorithm to solve the problems is demonstrated in this paper. The proposed ABC algorithm consisting of four phases: initialization, Employed Bee phase, Onlooker Bee phase and phase Scout Bee. Initialization phase generating solution candidates which subjects were randomly placed in the slot and the space available. In Employed bee phase and Onlooker Bee phase, a search of existing slots and room around the subjects that violate the constraint with the aim of minimizing constraint violation is conducted. Based on the experimental results show that the ABC algorithm can solve problems of CBT on the ITC-2007 with fewer number of soft constraint violations compared to the results of Asaju et. Al, who proposed similar approach to solve CBT problems in the ICT-2007. However, the proposed ABC Algorithm is still not able to achieve results comparable with the best approach reported in the ITC-2007.

Swarm intelligence (SI) based algorithms are performing very well in the field of optimization over the past few decades. A lot of new SI based algorithms are being developed. The existing algorithms are also modified, mostly, either by hybridizing them with some other algorithms or by incorporating local search techniques. This research presents a new local search strategy based on grasshopper jumping mechanism. The proposed local search strategy is termed as grasshopper local search strategy (GHLS). Further, the proposed strategy is incorporated into an efficient SI based algorithm, artificial bee colony (ABC) algorithm. The proposed hybridized algorithm is termed as grasshopper inspired artificial bee colony (GHABC) algorithm. The proposed GHABC is tested on 37 numerical benchmark optimization functions. The results indicate that the proposed GHABC algorithm is a competent approach for solving numerical optimization problems.

Artificial bee colony (ABC) optimisation algorithm is a recent, fast and easy-to-implement population-based meta heuristic for optimisation. ABC has been proved a rival algorithm with some popular swarm intelligence-based algorithms such as particle swarm optimisation, firefly algorithm and ant colony optimisation. The solution search equation of ABC is influenced by a random quantity which helps its search process in exploration at the cost of exploitation. In order to find a fast convergent behaviour of ABC while exploitation capability is maintained, in this paper basic ABC is modified in two ways. First, to improve exploitation capability, two local search strategies, namely classical unidimensional local search and levy flight random walk-based local search are incorporated with ABC. Furthermore, a new solution search strategy, namely stochastic diffusion scout search is proposed and incorporated into the scout bee phase to provide more chance to abandon solution to improve itself. Efficiency of the proposed algorithm is tested on 20 benchmark test functions of different complexities and characteristics. Results are very promising and they prove it to be a competitive algorithm in the field of swarm intelligence-based algorithms.

Artificial Bee Colony (ABC) is a well known optimization approach to solve nonlinear and complex problems. It is relatively a simple and recent population based probabilistic approach for global optimization. Similar to other population based algorithms, ABC is also computationally expensive due to its slow nature of search process. The solution search equation of ABC is significantly influenced by a random quantity which helps in exploration at the cost of exploitation of the search space. In the solution search equation of ABC due to the large step size the chance of skipping the true solution is high. Therefore, in this paper, to balance the diversity and convergence capability of the ABC, Lévy Flight random walk based local search strategy is proposed and incorporated with ABC along with opposition based learning strategy. The proposed algorithm is named as Opposition Based Lévy Flight ABC. The experiments over 14 un-biased test problems of different complexities and five well known engineering optimization problems show that the proposed algorithm outperforms the basic ABC and its recent variants namely Gbest guided ABC, Best-So-Far ABC, and Modified ABC in most of the experiments.

Differential Evolution (DE) is a renowned optimization stratagem that can easily solve nonlinear and comprehensive problems. DE is a well known and uncomplicated population based probabilistic approach for comprehensive optimization. It has apparently outperformed a number of Evolutionary Algorithms and further search heuristics in the vein of Particle Swarm Optimization at what time of testing over both yardstick and actual world problems. Nevertheless, DE, like other probabilistic optimization algorithms, from time to time exhibits precipitate convergence and stagnates at suboptimal position. In order to stay away from stagnation behavior while maintaining an excellent convergence speed, an innovative search strategy is introduced, named memetic search in DE. In the planned strategy, positions update equation customized as per a memetic search stratagem. In this strategy a better solution participates more times in the position modernize procedure. The position update equation is inspired from the memetic search in artificial bee colony algorithm. The proposed strategy is named as Memetic Search in Differential Evolution (MSDE). To prove efficiency and efficacy of MSDE, it is tested over 8 benchmark optimization problems and three real world optimization problems. A comparative analysis has also been carried out among proposed MSDE and

The multiple-choice multidimensional knapsack problem (MMKP) is a well-known NP-hard problem that has many real-time applications. However, owing to its complexity, finding computationally efficient solutions for the MMKP remains a challenging task. In this study, we propose a Modified Artificial Bee Colony algorithm (MABC) to solve the MMKP. The MABC employs surrogate relaxation, Hamming distance, and a tabu list to enhance the local search process and exploit neighborhood information. We evaluated the performance of the MABC on standard benchmark instances and compared it with several stateof-the-art algorithms, including RLS, ALMMKP, ACO, PEGF-PERC, TIKS-TIKS 2 and D-QPSO. The experimental results reveal that MABC produces highly competitive solutions in terms of the best solutions found, achieving approximately 2% of the optimal solutions with trivial (milliseconds) CPU time. The Kruskal-Wallis test revealed that there was no statistically significant difference in the objective function values between the MABC algorithm and other state-of-the-art algorithms (H = 0.31506, p = 0.98882). However, for CPU efficiency, the test showed a statistically significant difference (H = 84.90850, p = 0), indicating that the MABC algorithm exhibited significantly better CPU efficiency (with shorter execution times) than the other algorithms did. Along with these findings, the ease of implementation of the algorithm and the small number of control parameters make our approach highly adaptive for large-scale real-time systems. INDEX TERMS Artificial bee colony algorithm, multiple-choice multidimensional knapsack problem, hamming distance, surrogate relaxation.

The multiple-choice multidimensional knapsack problem (MMKP) is a well-known NP-hard problem that has many real-time applications. However, owing to its complexity, finding computationally efficient solutions for the MMKP remains a challenging task. In this study, we propose a Modified Artificial Bee Colony algorithm (MABC) to solve the MMKP. The MABC employs surrogate relaxation, Hamming distance, and a tabu list to enhance the local search process and exploit neighborhood information. We evaluated the performance of the MABC on standard benchmark instances and compared it with several state-of-the-art algorithms, including RLS, ALMMKP, ACO, PEGF-PERC, TIKS-TIKS 2 and D-QPSO. The experimental results reveal that MABC produces highly competitive solutions in terms of the best solutions found, achieving approximately 2% of the optimal solutions with trivial (milliseconds) CPU time. The Kruskal-Wallis test revealed that there was no statistically significant difference in the objective function values between the MABC algorithm and other state-of-the-art algorithms (H = 0.31506, p = 0.98882). However, for CPU efficiency, the test showed a statistically significant difference (H = 84.90850, p = 0), indicating that the MABC algorithm exhibited significantly better CPU efficiency (with shorter execution times) than the other algorithms did. Along with these findings, the ease of implementation of the algorithm and the small number of control parameters make our approach highly adaptive for large-scale real-time systems. INDEX TERMS Artificial bee colony algorithm, multiple-choice multidimensional knapsack problem, hamming distance, surrogate relaxation.

most provincial governments are considering or introducing changes to hospital funding. Ten years of rapidly increasing expenditures have left them still facing complaints of waiting lists and waiting times. Activity-based funding (ABf) would supplement traditional negotiated global budgets, reimbursing a predetermined amount for each case treated-essentially, a "fee schedule"-thus providing incentives and resources to increase throughput of certain "hot button" procedures and services and to improve efficiency. maybe. ABf-type systems in other countries date back over 20 years; the results are very mixed. What is clear is that information and reporting requirements are substantial. A host of perverse incentives lurk in ABf. most Canadian hospitals and provincial governments do not now have the necessary data systems, so are wise to proceed cautiously.

The nature-inspired algorithms (NIAs) have shown efficiency to solve many complex real-world optimization problems. The efficiency of NIAs is measured by their ability to find adequate results within a reasonable amount of time, rather than an ability to guarantee the optimal solution. This paper presents a solution for lower order system modeling using Spider monkey optimization (SMO) algorithm to obtain a better approximation for lower order systems and reflects almost original higher order system's characteristics. Further, a local search strategy, namely power law based local search is incorporated with SMO. The proposed strategy is named as power law based local search in SMO (PLSMO). The efficiency, accuracy, and reliability of the proposed algorithm is tested over 20 well known benchmark functions. Then, the PLSMO algorithm is applied to solve the lower order system modeling problem.

Curriculum-Based university Course Time-Tabling, CB-CTT, a known scheduling problem. We adapted a new swarm intelligence approach, identified as MABC based on the Artificial Bee Colony (ABC) to solve the CB-CTT. The approach consists of two steps: first, a feasible solution of the problem is constructed, which satisfies only the hard constraints; and then, the soft constraints are attempted to be satisfied. MABC could satisfy the hard constraints of the problem for all datasets of the ITC-2007 track 3, a benchmark dataset for the CB-CTT. The penalty of the achieved solutions by MABC is comparable to the related work in the literature that used the ABC for solving the CB-CTT.

Artificial Bee Colony (ABC) optimization algorithm is a swarm intelligence based nature inspired algorithm which has been proved a competitive algorithm with some popular nature-inspired algorithms. However, it is found that the ABC algorithm prefers exploration at the cost of the exploitation. Therefore, in this paper a self adaptive fitness based mutation strategy is presented in which the perturbation in the solution is based on fitness of the solution. The proposed strategy is self-adaptive in nature and therefore no manual parameter setting is required.

Abstract— Artificial Bee Colony (ABC) algorithm is very interesting population based swarm optimization technique. This technique is motivated by means of extraordinary nature of honey bees. ABC algorithm commonly used to get to the bottom of nonlinear and complex problems. Comparable to other population based strategies, ABC also has some negative aspects. It is computationally steep because of its sluggish temperament during search process. This paper planned a new approach so as to enhance performance of ABC strategy by combining positive characteristics two most popular modification of ABC algorithm named Gbest-guided ABC (GABC) and Memetic search in ABC (MeABC). It combines best part of both algorithm and balance process of exploration and exploitation in ABC. GABC improves exploitation and MeABC improves exploration. The proposed algorithm is named as Memetic search with Global best solution in ABC (MGABC). It is tested over some impartial test problems with diverse intricacy;...

Swarm intelligence (SI) based algorithms are performing very well in the field of optimization over the past few decades. A lot of new SI based algorithms are being developed. The existing algorithms are also modified, mostly, either by hybridizing them with some other algorithms or by incorporating local search techniques. This research presents a new local search strategy based on grasshopper jumping mechanism. The proposed local search strategy is termed as grasshopper local search strategy (GHLS). Further, the proposed strategy is incorporated into an efficient SI based algorithm, artificial bee colony (ABC) algorithm. The proposed hybridized algorithm is termed as grasshopper inspired artificial bee colony (GHABC) algorithm. The proposed GHABC is tested on 37 numerical benchmark optimization functions. The results indicate that the proposed GHABC algorithm is a competent approach for solving numerical optimization problems.

The most widespread motors in industries are induction motors nowadays. The design, performance evaluation and control of induction motors are based on circuit parameters. The accurate measurement of electrical parameters, like resistance (or reactance), is a tedious job. So, researchers have found induction motor parameter estimation as a significant optimisation aspect. Though conventional techniques produce good results, the swarm intelligencemotivated techniques produce still better results for real-world optimisation problems nowadays. In this paper, swarm intelligence-motivated artificial bee colony (ABC) algorithm is modified with physics's phenomena. The proposed algorithm is named as disrupted black hole ABC (DBHABC) algorithm. Further, this proposed algorithm is applied for optimising induction motor parameter estimation. The obtained outcomes reveal that DBHABC may be a good choice for induction motor parameter estimation.

Artificial Bee Colony (ABC) optimization algorithm is a swarm intelligencebased nature inspired algorithm, which has been proved a competitive algorithm with some popular nature-inspired algorithms. ABC has been found to be more efficient in exploration as compared to exploitation. With a motivation to balance exploration and exploitation capabilities of ABC, this paper presents an adaptive version of ABC. In this adaptive version, step size in solution modification and ABC parameter 'limit' are set adaptively based on current fitness values. In the present self-adaptive ABC, good solutions are appointed to exploit the search region in their neighbourhood, while worse solutions are appointed to explore the search region. The better solutions are given higher chances to update themselves with the help of parameter 'limit', which changes adaptively in the present study. The experiments on 16 unbiased test problems of different complexities show that the proposed strategy outperforms the basic ABC and some recent variants of ABC.

Differential Evolution (DE) is a well known and simple population based probabilistic approach for global optimization. It has reportedly outperformed a few Evolutionary Algorithms and other search heuristics like Particle Swarm Optimization when tested over both benchmark and real world problems. But, DE, like other probabilistic optimization algorithms, sometimes exhibits premature convergence and stagnates at suboptimal point. In order to avoid stagnation behavior while maintaining a good convergence speed, a new position update process is introduced, named fitness based position update process in DE. In the proposed strategy, position of the solutions are updated in two phases. In the first phase all the solutions update their positions using the basic DE and in the second phase, all the solutions update their positions based on their fitness. In this way, a better solution participates more times in the position update process. The position update equation is inspired from the Artificial Bee Colony algorithm. The proposed strategy is named as Fitness Based Differential Evolution (F B DE). To prove efficiency and efficacy of F B DE, it is tested over 22 benchmark optimization problems. A comparative analysis has also been carried out among proposed FBDE, basic DE,

Artificial Bee Colony (ABC) is a well known optimization approach to solve nonlinear and complex problems. It is relatively a simple and recent population based probabilistic approach for global optimization. Similar to other population based algorithms, ABC is also computationally expensive due to its slow nature of search process. The solution search equation of ABC is significantly influenced by a random quantity which helps in exploration at the cost of exploitation of the search space. In the solution search equation of ABC due to the large step size the chance of skipping the true solution is high. Therefore, in this paper, to balance the diversity and convergence capability of the ABC, Lévy Flight random walk based local search strategy is proposed and incorporated with ABC along with opposition based learning strategy. The proposed algorithm is named as Opposition Based Lévy Flight ABC. The experiments over 14 un-biased test problems of different complexities and five well known engineering optimization problems show that the proposed algorithm outperforms the basic ABC and its recent variants namely Gbest guided ABC, Best-So-Far ABC, and Modified ABC in most of the experiments.

Artificial bee colony (ABC) optimisation algorithm is relatively a recent and simple population-based probabilistic approach for global optimisation over continuous and discrete spaces. It has reportedly outperformed a few evolutionary algorithms (EAs) and other search heuristics when tested over both benchmark and real world problems. ABC, like other probabilistic optimisation algorithms, has inherent drawback of premature convergence or stagnation that leads to the loss of exploration and exploitation capability of ABC. Therefore, in order to find a trade-off between exploration and exploitation capability of ABC algorithm two modifications are proposed in this paper. First, a new control parameter namely, cognitive learning factor (CLF) is introduced in the employed bees phase and onlooker bees phase. Cognitive learning is a powerful mechanism that adjusts the current position of candidate solution by a means of some specified knowledge. Second, the range of ABC control parameter φ is modified. The proposed strategy named as balanced artificial bee colony (BABC) algorithm, balances the exploration and exploitation capability of the ABC. To prove efficiency of the algorithm, it is tested over 24 benchmark problems of different complexities and compared with the basic ABC.

Artificial bee colony (ABC) optimisation algorithm is relatively a simple and recent population-based probabilistic approach for global optimisation. ABC has been outperformed over some nature inspired algorithms (NIAs) when tested over benchmark as well as real world optimisation problems. The solution search equation of ABC is significantly influenced by a random quantity which helps in exploration at the cost of exploitation of the search space. In the solution search equation of ABC, there is an enough chance to skip the true solution due to large step sizes. In order to balance the diversity and convergence capability of the ABC, in this paper, a power law-based local search strategy is proposed and integrated with ABC. The proposed strategy is named as power law-based local search in ABC (PLABC). In the PLABC, new solutions are generated around the best solution and it helps to enhance the exploitation capability of ABC. Further, to improve the exploration capability, numbers of scout bees are increased. The experiments on 24 test problems of different complexities show that the proposed strategy outperforms the basic ABC and recent variants of ABC, namely, Gbest guided ABC (GABC), best-so-far ABC (BSFABC) and modified ABC in most of the experiments.

In recent years researchers have provided novel problem solving techniques based on swarm intelligence for solving difficult real world problems such as traffic, routing, networking, games, industries and economics. Artificial bee colony algorithm (ABC) was first developed by Dervis Karaboga [1]. When the robust performance is desired by means of searching something, the swarms does it better; by adaptation of greedy selection and random search. The ABC algorithm simulates the foraging behavior of honey bees. The local search in two stages in each step and global search are responsible for making this algorithm a robust search technique. The details of this algorithm are discussed here. Because of its very strong search process, computational simplicity and ease of modification according to the problem, the ABC algorithm is now finding more widespread applications in business, scientific and engineering circles. In this paper, we provide a thorough and extensive overview of most res...

Artificial Bee Colony (ABC) optimization algorithm is a powerful stochastic evolutionary algorithm that is used to find the global optimum solution in search space. In ABC each bee stores candidate solution; and stochastically modifies its candidate over time, based on the best solution found by neighboring bees,and based on the best solution found by the bee itself. When tested over various benchmark function and real life problems, it has performed better than a few evolutionary algorithms and other search heuristics. ABC, like other probabilistic optimization algorithms, has inherent drawback of premature convergence or stagnation that leads to loss of exploration and exploitation capability. Therefore, in order to balance between exploration and exploitation capability of ABC a new search strategy is proposed. In the proposed strategy, search process in ABC is performed by smaller group of independent swarms of bees. The experiments with 10 test functions of different complexities show that the proposed strategy has better diversity and faster convergence than the basic ABC.

Software reliability engineering has recently turned out to be an interesting research topic in the field of software engineering. For the purpose of reliability calculation of software, various software reliability models have been designed based on the application of software in particular fields. The ability of the particular model in estimating the failure rate, reliability, and cost of the software are the major requirement in reliability modeling since any sort of failure or fault in the software can make the entire system unreliable to perform the desired operation. This paper proposes an efficient software reliability growth model (SRGM) model selection for estimating the reliability of the software. The reliability model selection criteria are generally based on the improved computational time and better failure rates. The selection of the model is done by utilizing optimization techniques. Here we have used modified cuckoo search optimization and modified ABC in order to find the effectiveness of the reliability model. Using these optimization algorithms, we evaluate various measures of the reliability models and are compared with that of other models. Here two different optimization approaches are used since we can efficiently find the best model using these algorithms.

Artificial bee colony (ABC) optimization algorithm is relatively a simple and recent population based probabilistic approach for global optimization. ABC has been outperformed over some Nature Inspired Algorithms (NIAs) when tested over benchmark as well as real world optimization problems. The solution search equation of ABC is significantly influenced by a random quantity which helps in exploration at the cost of exploitation of the search space. In the solution search equation of ABC, there is a enough chance to skip the true solution due to large step size. In order to balance between diversity and convergence capability of the ABC, a new local search phase is integrated with the basic ABC to exploit the search space identified by the best individual in the swarm. In the proposed phase, ABC works as a local search algorithm in which, the step size that is required to update the best solution, is controlled by Golden Section Search approach. The proposed strategy is named as Memetic ABC (MeABC). In MeABC, new solutions are generated around the best solution and it helps to enhance the exploitation capability of ABC. MeABC is established as a modified ABC algorithm through experiments over 20 test problems of different complexities and 4 well known engineering optimization problems.

Abstract— Artificial Bee Colony (ABC) algorithm is very interesting population based swarm optimization technique. This technique is motivated by means of extraordinary nature of honey bees. ABC algorithm commonly used to get to the bottom of nonlinear and complex problems. Comparable to other population based strategies, ABC also has some negative aspects. It is computationally steep because of its sluggish temperament during search process. This paper planned a new approach so as to enhance performance of ABC strategy by combining positive characteristics two most popular modification of ABC algorithm named Gbest-guided ABC (GABC) and Memetic search in ABC (MeABC). It combines best part of both algorithm and balance process of exploration and exploitation in ABC. GABC improves exploitation and MeABC improves exploration. The proposed algorithm is named as Memetic search with Global best solution in ABC (MGABC). It is tested over some impartial test problems with diverse intricacy;...

Optimal power flow (OPF) is the most requisite tool in the power system analysis. The OPF is relatively a difficult constrained optimization problem and broadly solved by conventional as well as modern intelligent methods. In this paper the authors proposed a levy flight spider monkey optimization (LFSMO) algorithm to solve the standard OPF problem for IEEE 30-bus system, which is proposed to improve the exploitation capability of spider monkey optimization (SMO) algorithm. The performance of LFSMO is evaluated by testing it over 25 benchmark functions.

The nature-inspired algorithms (NIAs) have shown efficiency to solve many complex real-world optimization problems. The efficiency of NIAs is measured by their ability to find adequate results within a reasonable amount of time, rather than an ability to guarantee the optimal solution. This paper presents a solution for lower order system modeling using Spider monkey optimization (SMO) algorithm to obtain a better approximation for lower order systems and reflects almost original higher order system's characteristics. Further, a local search strategy, namely power law based local search is incorporated with SMO. The proposed strategy is named as power law based local search in SMO (PLSMO). The efficiency, accuracy, and reliability of the proposed algorithm is tested over 20 well known benchmark functions. Then, the PLSMO algorithm is applied to solve the lower order system modeling problem.

Web-based learning system helps to improve the style of learning environment that adapts the learning material to meet learners' needs and also can adapt the learning content according to the individuality of learners. Web learning system provides personalized learning environment that is based on user's cognition. Web Based Education-WBE encompasses all aspects and processes of education that use a World Wide Web as a communication medium and supporting technology. With the increasing popularity of web based learning, it is required to recognize the cognition of individual learners. Cognitive theory is widely used to predict the effectiveness of the web learning and multimedia learning. The cognition induced by instructional and multimedia modes are measured by indirect or subjective methods. Questionnaires are a common form of measuring cognition indirectly. In this paper, a questionnaire is prepared to identify the cognition of the student and his website preferences in t...

Scheduling academic staff timetable is important to avoid the classes clash or redundancy between teacher and student timetable. A good timetable allow the student and teacher time management with a good healthy and work-life balance. However, with the scheduling academic staff timetable may use many procedure to get efficiency result. Therefore, this paper is provides a gap of study for existing work on optimization timetable regarding their research purpose. This research report our findings from review and analysis of different studies. The strengths and shortcomings of the features and utility are also discussed in order to offer a better understanding of the research's gaps and weaknesses. This study concludes that these studies are still in their infancy and require additional improvement.

This work proposes a population based heuristic that can be embedded within any local search algorithm to solve university course timetabling problems. Population based Local Search (PB-LS) employs two main operators. The first is applied to a single solution to determine the force between that solution and the current solutions, whilst the second is applied to all solutions to determine the force in all directions. The progress of the search through the search space is governed by the forces either in a single direction or in all directions. The aim of our work is to produce an effective algorithm, which can increase the diversity of local search algorithms and overcome the limitations of other population based algorithms. In order to evaluate the effectiveness of PB-LS, we perform a comparison between the performance of PB-LS with other approaches in the scientific literature. We use university course timetabling benchmark datasets as a test domain. Results show that, PB-LS is able to produce statistically significant higher quality solutions that outperforms many other approaches (in some instances with regards to Socha dataset).

Differential Evolution (DE) is a renowned optimization stratagem that can easily solve nonlinear and comprehensive problems. DE is a well known and uncomplicated population based probabilistic approach for comprehensive optimization. It has apparently outperformed a number of Evolutionary Algorithms and further search heuristics in the vein of Particle Swarm Optimization at what time of testing over both yardstick and actual world problems. Nevertheless, DE, like other probabilistic optimization algorithms, from time to time exhibits precipitate convergence and stagnates at suboptimal position. In order to stay away from stagnation behavior while maintaining an excellent convergence speed, an innovative search strategy is introduced, named memetic search in DE. In the planned strategy, positions update equation customized as per a memetic search stratagem. In this strategy a better solution participates more times in the position modernize procedure. The position update equation is inspired from the memetic search in artificial bee colony algorithm. The proposed strategy is named as Memetic Search in Differential Evolution (MSDE). To prove efficiency and efficacy of MSDE, it is tested over 8 benchmark optimization problems and three real world optimization problems. A comparative analysis has also been carried out among proposed MSDE and

Recently, swarm intelligence-based algorithms gained attention of the researchers due to their wide applicability and ease of implementation. However, much research has been made on the development of swarm intelligence algorithms but theoretical analysis of these algorithms is still a less explored area of the research. Theoretical analyses of trajectory and convergence of potential solutions toward the equilibrium point in the search space can help the researchers to understand the iteration-wise behavior of the algorithms which can further help in making them efficient. Artificial bee colony (ABC) optimization algorithm is swarm intelligence-based algorithm. This paper presents the convergence analysis of ABC algorithm using theory of dynamical system. Convergent boundaries for the parameters of ABC update equation have also been proposed. Also, the trajectory of potential solutions in the search space is analyzed by obtaining a partial differential equation corresponding to the position update equation of ABC algorithm. The analysis reveals that the ABC algorithm performs better when parameters of the update equation are in the convergent region and potential solutions movement follows 1-D advection equation. INDEX TERMS Artificial bee colony (ABC) algorithm, advection equation, convergence analysis, finite difference scheme, swarm intelligence.

This work is a research in the field of Ontologies Integration from the point of view of Ontology Mining based on Services. Specifically, the work focuses on an automatic suggestion of ontological alignments for users. The Ontology Mining area (OM) is very recent, due to the current trend of using ontologies as a mechanism for representing knowledge, which has created a wide field to explore and extract knowledge. The problem lies in the comparison of existing ontologies, in order to use them together, that is, finding their semantic equivalences. There are different techniques for ontologies alignment as a form of ontological comparison based on the matching of the concepts, which is a fundamental process in the ontologies integration. Each alignment technique uses different strategies; based on specific principles, which make it more adequate for a particular context. This paper proposes an automatic approach for comparison and selection of alignment techniques, given a group of o...

Self-organisation is a distributed and asynchronous process in which global pattern or behaviour emerge from local components of the system. Neither central control nor external intervention is necessary during this process. Self-organising systems are adaptive and robust, which are appealing properties from a design and engineering point of view. In this paper, we present an innovative self-organisation approach for a dynamic vehicle routing problem, the Barclay Cycle Hire truck dispatch. In addition, we introduce an evolutionary algorithm capable of automatically configuring the "self-organising trucks". Experimental results show the evolutionary algorithm improves the overall fitness of the selforganising trucks; and we observe global emergent behaviour in the way trucks self-organise.

Artificial Bee Colony (ABC) optimization algorithm is a swarm intelligencebased nature inspired algorithm, which has been proved a competitive algorithm with some popular nature-inspired algorithms. ABC has been found to be more efficient in exploration as compared to exploitation. With a motivation to balance exploration and exploitation capabilities of ABC, this paper presents an adaptive version of ABC. In this adaptive version, step size in solution modification and ABC parameter 'limit' are set adaptively based on current fitness values. In the present self-adaptive ABC, good solutions are appointed to exploit the search region in their neighbourhood, while worse solutions are appointed to explore the search region. The better solutions are given higher chances to update themselves with the help of parameter 'limit', which changes adaptively in the present study. The experiments on 16 unbiased test problems of different complexities show that the proposed strategy outperforms the basic ABC and some recent variants of ABC.

The nature-inspired algorithms (NIAs) have shown efficiency to solve many complex real-world optimization problems. The efficiency of NIAs is measured by their ability to find adequate results within a reasonable amount of time, rather than an ability to guarantee the optimal solution. This paper presents a solution for lower order system modeling using Spider monkey optimization (SMO) algorithm to obtain a better approximation for lower order systems and reflects almost original higher order system's characteristics. Further, a local search strategy, namely power law based local search is incorporated with SMO. The proposed strategy is named as power law based local search in SMO (PLSMO). The efficiency, accuracy, and reliability of the proposed algorithm is tested over 20 well known benchmark functions. Then, the PLSMO algorithm is applied to solve the lower order system modeling problem.

Optimal power flow (OPF) is the most requisite tool in the power system analysis. The OPF is relatively a difficult constrained optimization problem and broadly solved by conventional as well as modern intelligent methods. In this paper the authors proposed a levy flight spider monkey optimization (LFSMO) algorithm to solve the standard OPF problem for IEEE 30-bus system, which is proposed to improve the exploitation capability of spider monkey optimization (SMO) algorithm. The performance of LFSMO is evaluated by testing it over 25 benchmark functions.

Segmentation of images is a very challenging problem due to the presence of noise in the images and its widespread usage and applications. In this paper we proposed the GABC-Genetic Artificial Bee Colony Algorithm which is a hybrid concept of Artificial Bee Colony and Genetic Algorithm used for solving segmentation problem. Here we add Genetic Algorithm with ABC Algorithm which improves the solution space. In GABC, threshold estimation is regarded as search procedure which finds an appropriate value in a continuous grey scale interval. In our proposed methodology, optimal threshold is searched with the help of ABC algorithm and the GA updates the solution space. For finding efficient fitness function for ABC algorithm the original image is decomposed using discrete wavelet transform after the definition of grey number in the Grey theory. Now the approximation image and gradient image is reconstructed with low frequency coefficients and high frequency coefficients respectively. Then ...

Optimal power flow (OPF) is the most requisite tool in the power system analysis. The OPF is relatively a difficult constrained optimization problem and broadly solved by conventional as well as modern intelligent methods. In this paper the authors proposed a levy flight spider monkey optimization (LFSMO) algorithm to solve the standard OPF problem for IEEE 30-bus system, which is proposed to improve the exploitation capability of spider monkey optimization (SMO) algorithm. The performance of LFSMO is evaluated by testing it over 25 benchmark functions.

Abstract— Artificial Bee Colony (ABC) algorithm is very interesting population based swarm optimization technique. This technique is motivated by means of extraordinary nature of honey bees. ABC algorithm commonly used to get to the bottom of nonlinear and complex problems. Comparable to other population based strategies, ABC also has some negative aspects. It is computationally steep because of its sluggish temperament during search process. This paper planned a new approach so as to enhance performance of ABC strategy by combining positive characteristics two most popular modification of ABC algorithm named Gbest-guided ABC (GABC) and Memetic search in ABC (MeABC). It combines best part of both algorithm and balance process of exploration and exploitation in ABC. GABC improves exploitation and MeABC improves exploration. The proposed algorithm is named as Memetic search with Global best solution in ABC (MGABC). It is tested over some impartial test problems with diverse intricacy;...

Web services are application software which can be remotely accessed through the Internet. Due to the proliferate the growth of web services of the same functionality, the user goes into a dilemma to select suitable service for him. In this paper, we study the web service selection problem (WSS) in a sequential composition model. We formulated the WSS as a constrained optimization problem. To solve the problem we suggest, a modified artificial bee colony (mABC) algorithm, which uses a chaotic based opposition learning method to generate a better initial population. To improve the exploration capability of mABC, a new search equation for employee bee phase is suggested. On the other hand, to improve the exploitation capacity of mABC, a new search strategy, inspired by differential evolution (DE), is adopted in the onlooker bee phase. We test mABC on synthetic web service selection problem taken from QWS dataset. To asses the relative performance of mABC, we compare it against five other state-of-the-art algorithms. The experimental results show that mABC is better than other existing approaches in terms of response time, latency, availability and reliability. INDEX TERMS Web service selection, Artificial bee colony algorithm, Chaotic map, QoS attributes.

Nature-inspired algorithms are very popular tools for solving optimization problems inspired by nature. However, there is no guarantee that optimal solution can be obtained using a randomly selected algorithm. As such, the problem can be addressed using trial and error via the use of different optimization algorithms. Therefore, the proposed study in this paper analyzes the time-complexity and efficacy of some nature-inspired algorithms which includes Artificial Bee Colony, Bat Algorithm and Particle Swarm Optimization. For each algorithm used, experiments were conducted several times with iterations and comparative analysis was made. The result obtained shows that Artificial Bee Colony outperformed other algorithms in terms of the quality of the solution, Particle Swarm Optimization is time efficient while Artificial Bee Colony yield a worst case scenario in terms of time complexity.

The Artificial Bee Colony (ABC) algorithm is a swarm intelligence based algorithm, which simulate the foraging behavior of honey bee colonies. It has been widely applied to solve the real-world problem. However, ABC has good exploration but poor exploitation abilities, and its convergence speed is also an issue in some cases. In order to overcome these issues, this paper presents a new metaheuristic algorithm called Quantum Artificial Bee Colony (QABC) algorithm for global optimization problems inspired by quantum physics concepts. Simulations are conducted on a suite of unimodal/multimodal continuous benchmark functions. The results demonstrate the good performance of the QABC algorithm in solving complex numerical optimization problems when compared with other popular algorithms.

In recent years researchers have provided novel problem solving techniques based on swarm intelligence for solving difficult real world problems such as traffic, routing, networking, games, industries and economics. Artificial bee colony algorithm (ABC) was first developed by Dervis Karaboga [1]. When the robust performance is desired by means of searching something, the swarms does it better; by adaptation of greedy selection and random search. The ABC algorithm simulates the foraging behavior of honey bees. The local search in two stages in each step and global search are responsible for making this algorithm a robust search technique. The details of this algorithm are discussed here. Because of its very strong search process, computational simplicity and ease of modification according to the problem, the ABC algorithm is now finding more widespread applications in business, scientific and engineering circles. In this paper, we provide a thorough and extensive overview of most res...

In this study a hybrid differential evolution-back-propagation algorithm to optimize the weights of feedforward neural network is proposed.The hybrid algorithm can achieve faster convergence speed with higher accuracy. The proposed hybrid algorithm combining differential evolution (DE) and back-propagation (BP) algorithm is referred to as DE-BP algorithm to train the weights of the feed-forward neural (FNN) network by exploiting global searching feature of the DE evolutionary algorithm and strong local searching ability of the BP algorithm. The DE has faster exploration property during initial stage of global search for the expense of convergence speed. On the contrary, the problem of random initialization of weights may lead to getting stuck at local minima of the gradient based BP algorithm. In the proposed hybrid algorithm, initially we use global searching ability of the DE to move towards global optimal solution in the search space for few generations by selecting good starting weights and then precise local gradient searching of the BP in that region to converge to the optimal solution with increased speed of convergence. The performance of proposed DE-BP is investigated on a couple of public domain datasets, the experimental results are compared with the BP algorithm, the DE evolutionary training algorithm and a hybrid real-coded GA with back-propagation (GA-BP) algorithm. The results show that the proposed hybrid DE-BP algorithm produce promising results in comparison with other training algorithms.

Artificial Bee Colony (ABC) and Differential Evolution (DE) are two very popular and efficient meta-heuristic algorithms. However, both algorithms have been applied to various science and engineering optimization problems, extensively, the algorithms suffer from premature convergence, unbalanced exploration-exploitation, and sometimes slow convergence speed. Hybridization of ABC and DE may provide a platform for developing a meta-heuristic algorithm with better convergence speed and a better balance between exploration and exploitation capabilities. This paper proposes a hybridization of ABC and DE algorithms to develop a more efficient meta-heuristic algorithm than ABC and DE. In the proposed hybrid algorithm, Hybrid Artificial Bee Colony with Differential Evolution (HABCDE), the onlooker bee phase of ABC is inspired from DE. Employed bee phase is modified by employing the concept of the best individual while scout bee phase has also been modified for higher exploration. The proposed HABCDE has been tested over 20 test problems and 4 real-world optimization problems. The performance of HABCDE is compared with the basic version of ABC and DE. The results are also compared with state-of-the-art algorithms, namely Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Particle Swarm Optimization (PSO), Biogeography Based Optimization (BBO) and Spider Monkey Optimization (SMO) to establish the superiority of the proposed algorithm. For further validation of the proposed hybridization, the experimental results are also compared with other hybrid versions of ABC and DE, namely ABC-DE, DE-BCO and HDABCA and with modified ABC algorithms, namely Best-So-Far ABC (BSFABC), Gbest guided ABC (GABC) and Modified ABC (MABC). Results indicate that HABCDE would be a competitive algorithm in the field of meta-heuristics.