Bat Algorithm

Modern metaheuristic algorithms such as bee algorithms and harmony search start to demonstrate their power in dealing with tough optimization problems and even NP-hard problems. This book reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including genetic algorithms, bee algorithms, particle swarm optimization, simulated annealing, ant colony optimization, harmony search, and firefly algorithms. We also briefly introduce the photosynthetic algorithm, the enzyme algorithm, and Tabu search. Worked examples with implementation have been used to show how each algorithm works. This book is thus an ideal textbook for an undergraduate and/or graduate course. As some of the algorithms such as the harmony search and firefly algorithms are at the forefront of current research, this book can also serve as a reference book for researchers.

Bat algorithm (BA) is a bio-inspired algorithm developed by Yang in 2010 and BA has been found to be very efficient. As a result, the literature has expanded significantly in the last 3 years. This paper provides a timely review of the bat algorithm and its new variants. A wide range of diverse applications and case studies are also reviewed and summarized briefly here. Further research topics are also discussed.

Many optimization algorithms have been developed by drawing inspiration from swarm intelligence (SI). These SI-based algorithms can have some advantages over traditional algorithms. In this paper, we carry out a critical analysis of these SI-based algorithms by analyzing their ways to mimic evolutionary operators. We also analyze the ways of achieving exploration and exploitation in algorithms by using mutation, crossover and selection. In addition, we also look at algorithms using dynamic systems , self-organization and Markov chain framework. Finally, we provide some discussions and topics for further research.

Bat algorithm (BA) is a recent metaheuristic optimization algorithm proposed by Yang. In the present study, we have introduced chaos into BA so as to increase its global search mobility for robust global optimization. Detailed studies have been carried out on benchmark problems with different chaotic maps. Here, four different variants of chaotic BA are introduced and thirteen different chaotic maps are utilized for validating each of these four variants. The results show that some variants of chaotic BAs can clearly outperform the standard BA for these benchmarks.

Bat algorithm (BA) is a recent optimization algorithm based on swarm intelligence and inspiration from the echolocation behavior of bats. One of the issues in the standard bat algorithm is the premature convergence that can occur due to the low exploration ability of the algorithm under some conditions. To overcome this deficiency, directional echolocation is introduced to the standard bat algorithm to enhance its exploration and exploitation capabilities. In addition to such directional echolocation, three other improvements have been embedded into the standard bat algorithm to enhance its performance. The new proposed approach, namely the directional Bat Algorithm (dBA), has been then tested using several standard and non-standard benchmarks from the CEC'2005 benchmark suite. The performance of dBA has been compared with ten other algorithms and BA variants using non-parametric statistical tests. The statistical test results show the superiority of the directional bat algorithm.

Cardiopulmonary resuscitation method is used to save more number of peoples from the neurological problem. In this case, the neurological problem denotes brain death. This brain death is mainly caused due to cardiac arrest and happen within 4 to 5 minutes. To avoid this problem we go for cardiopulmonary resuscitation method. It will be helpful to relieve the patient from cardiac arrest. When a cardiac arrest occurs, it is necessary to perform cardiopulmonary resuscitation as soon as possible. This requires maintaining the pressure depth at 5 cm at a rate of 100 cpm. For CPR machines, which are frequently used in ambulances, the return of spontaneous circulation is not superior to that of manual CPR, although CPR machines can maintain the compression rate more accurately. The manual CPR is not that much efficient when compared to automatic CPR because, the experts who are giving CPR to the patient are cannot able to give the continuous CPR to the patient but, the automatic CPR machine is able to give the continuous CPR to the patient. The article reviews such kind of automatic devices. The automatic CPR machine is already exist. But, the cost of that machine is high. So we intended to design the low cost CPR machine. This is achieved by replacing the component like Arduino microcontroller and solenoid lock. The replaced component also do the same work like in the high cost CPR machine.

Generally, Metaheuristic algorithms such as ant colony optimization, Elephant herding algorithm, particle swarm optimization, bat algorithms becomes a powerful methods for solving optimization problems. This paper provides a timely review of the bat algorithm and its new variants. Bat algorithm (BA) is a Swarm based metaheuristic algorithm developed in 2010 by Xin-She Yang, BA has been inspired by the foraging behavior of micro bats, algorithm carries out the search process using artificial bats as search agents mimicking the natural pulse loudness and emission rate of real bats. It has become a powerful swarm intelligence method for solving optimization problems over continuous and discrete spaces. Nowadays, it has been successfully applied to solve problems in almost all areas of optimization , and it found to be very efficient. As a result, the literature has expanded significantly, a wide range of diverse applications and case studies has been made base on the bat algorithm.

The performance of an artificial neural network (ANN) depends on the connection weights and network structure. Many optimization algorithms have been applied for ANN model selection. This paper presents an optimization algorithm based on the cooperative batinspired algorithm. The advantage of the bat algorithm lies in the combination of a population-based algorithm and local search; however, it is more powerful in local search. Therefore to better balance exploration and exploitation in the population some modifications to the velocity equation of the standard bat algorithm are applied. In addition, we propose two new topologies for cooperation between subpopulations to further improve the algorithm's capability to maintain the diversity of bats in the population. The first is a combination of two known mechanisms (Ring and Master-Slave), and the second inserts a Coevolving strategy of slave subpopulations in the Master-Slave strategy. The proposed methods are applied for the selection of an ANN model, where both the structure of the ANN and its weights are optimized by the method. Six classification and two time series prediction benchmark datasets are tested and the performance of the proposed algorithms is evaluated and compared with other methods in the literature. Statistical analysis shows that for the classification problem there is a significant improvement in the bat algorithm with Ring and Master-Slave strategies cooperation compared to the other methods in the literature in terms of classification error for three cases out of five and a significant enhancement in the number of connection weights in the network. The analysis also shows that for time series prediction there is a significant improvement in the prediction error for all the cases.

Optimization techniques are stimulated by Swarm Intelligence wherever the target is to get a decent competency of a problem. The knowledge of the behavior of animals or insects has a variety of models in Swarm Intelligence. Swarm Intelligence has become a potential technique for evolving many robust optimization problems. Researchers have developed various algorithms by modeling the behaviors of the different swarm of animals or insects. This paper explores three existing meta-heuristic methods named as Ant Colony Optimization (ACO), Bee Colony Optimization (BCO) and Bat Algorithm (BA). Ant Colony Optimization was stimulated by the nature of ants. Bee Colony Optimization was inspired by the plundering behavior of honey bees. Bat Algorithm was emerged on the echolocation characteristics of micro bats. This study analyzes the problem-solving behavior of groups of relatively simple agents wherein local interactions among agents, are either directly or indirectly through the environment. The scope of this paper is to explore the characteristics of swarm intelligence as well as its advantages, limitations and application areas, and subsequently, to explore the behavior of ants, bees and micro bats along with its most popular variants. Furthermore, the behavioral comparison of these three techniques has been analyzed and tried to point out which technique is better for optimization among them in Swarm Intelligence. From this, the paper can help to understand the most appropriate technique for optimization according to their behavior.

This study investigated reservoir operation under climate change for a base period (1981–2000) and future period (2011–2030). Different climate change models, based on A2 scenario, were used and the HAD-CM3 model, considering uncertainty, among other climate change models was found to be the best model. For the Dez basin in Iran, considered as a case study, the climate change models predicted increasing temperature from 1.16 to 2.5 C and decreasing precipitation for the future period. Also, runoff volume for the basin would decrease and irrigation demand for the downstream consumption would increase for the future period. A hybrid framework (optimization-climate change) was used for reservoir operation and the bat algorithm was used for minimization of irrigation deficit. A genetic algorithm and a particle swarm algorithm were selected for comparison with the bat algorithm. The reliability, resiliency, and vulnerability indices, based on a multi-criteria model, were used to select the base method for reservoir operation. Results showed the volume of water to be released for the future period, based on all evolutionary algorithms used, was less than for the base period, and the bat algorithm with high-reliability index and low vulnerability index performed better among other evolutionary algorithms.

This book exemplifies how algorithms are developed by mimicking nature. Classical techniques for solving day-to-day problems is time-consuming and cannot address complex problems. Metaheuristic algorithms are nature-inspired optimization techniques for solving real-life complex problems. This book emphasizes the social behaviour of insects, animals and other natural entities, in terms of converging power and benefits. Major nature-inspired algorithms discussed in this book include the bee colony algorithm, ant colony algorithm, grey wolf optimization algorithm, whale optimization algorithm, firefly algorithm, bat algorithm, ant lion optimization algorithm, grasshopper optimization algorithm, butterfly optimization algorithm and others. The algorithms have been arranged in chapters to help readers gain better insight into nature-inspired systems and swarm intelligence. All the MATLAB codes have been provided in the appendices of the book to enable readers practice how to solve examples included in all sections. This book is for experts in Engineering and Applied Sciences, Natural and Formal Sciences, Economics, Humanities and Social Sciences.

An improved Bat algorithm with Gaussian distribution random walk (BAGD) is introduced in this paper. The original Bat algorithm has a problem of random large step length that leads to sub-optimal solutions in the search space and it cannot solve higher dimensional problems. To solve higher dimensional problems and to decrease the step length size, this research focuses on using a Gaussian distribution in Bat algorithm which provide shorter step lengths during the search. The proposed BAGD was compared with six popular metaheuristic algorithms on ten benchmark functions. Comparative results indicated that the proposed BAGD perform better than the state-of-the-art algorithms in most cases. The proposed BAGD solution used small step lengths in the search space and it was able to solve high dimensional problems.

In our information societies, we increasingly delegate tasks and decisions to automated systems, devices and agents that mediate human relationships, by taking decisions and acting on the basis of algorithms. Their increased intelligence, autonomous behavior and connectivity are changing crucially the life conditions of human beings as well as altering traditional concepts and ways of understanding reality. Algorithms are directed to solve problems that are not always detectable in their own relevance and timeliness. They are also meant to solve those problems through procedures that are not always visible and assessable in their own. In addition, technologies based on algorithmic procedures more and more infer personal information from aggregated data, thus profiling human beings and anticipating their expectations, views and behaviors. This may have normative, if not discriminatory, consequences. While algorithmic procedures and applications are meant to serve human needs, they risk to create an environment in which human beings tend to develop adaptive strategies by conforming their behaviour to the expected output of the procedures, with serious distortive effects. Against this backdrop, little room is often left for a process of rational argumentation able to challenge the results of algorithmic procedures by putting into question some of their hidden assumptions or by taking into account some neglected aspects of the problems under consideration. At the same time, it is widely recognized that scientific and social advances crucially depend on such an open and free critical discussion.

Optimizing reservoir operation rule is considered as a complex engineering problem which requires an efficient algorithm to solve. During the past decade, several optimization algorithms have been applied to solve complex engineering problems, which water resource decision-makers can employ to optimize reservoir operation. This study investigates one of the new optimization algorithms, namely, Bat Algorithm (BA). The BA is incorporated with different rule curves, including first-, second-, and third-order rule curves. Two case studies, Aydoughmoush dam and Karoun 4 dam in Iran, are considered to evaluate the performance of the algorithm. The main purpose of the Aydoughmoush dam is to supply water for irrigation. Hence, the objective function for the optimization model is to minimize irrigation deficit. On the other hand, Karoun 4 dam is designed for hydropower generation. Three different evaluation indices, namely, reliability, resilience, and vulnerability were considered to examine the performance of the algorithm. Results showed that the bat algorithm with third-order rule curve converged to the minimum objective function for both case studies and achieved the highest values of reliability index and resiliency index and the lowest value of the vulnerability index. Hence, the bat algorithm with third-order rule curve can be considered as an appropriate optimization model for reservoir operation.

Many designs and inverse problems can be formulated as optimization problems. Due to the cost of evaluating real-world objective functions, optimization algorithms must be both fast and robust. While Particle Swarm (PS) is one of the fastest and most robust optimization algorithms ever developed, it cannot solve all problems and is poorly suited in many cases. This paper proposes a modification to PS and investigates five algorithms similar to PS: Quantum Particle Swarm (QPS), Modified Quantum Particle Swarm (MQP), Firefly Algorithm (FA), Cuckoo Search (CKO), and Bat-Inspired Metaheuristic Algorithm (BAT). Their performance is compared to standard PS using a subset of the Schittkowski & Hock's test cases. The modified PS is observed to outperform standard PS in twenty-seven percent of the test cases. QPS and MQP similarly outperform PS in roughly twenty-nine percent of test cases, while BAT and FA outperform PS in twenty-six percent of test cases.

The topology of SEPIC "Single-ended primary inductance converter" is considered as a suitable option for automotive power system where the value of the output voltage should be ranging between the low and high values of the input value of voltage. The main feature of the DC-DC converter is the stability of output voltage. This paper shows the SEPIC converter employing PID converter to be used in industrial applications. A SEPIC DC-DC converter can manage either step-down mode or step-up mode. The benefit of using this circuit is to supply a stable and controlled output voltage no matter how much the input voltage is. The simulated behaviors of the uncontrolled and controlled SEPIC converter are presented in this paper. The PID controller based on bat algorithm (BA) optimization method is used for searching of the best Proportional-Integral-Derivative (PID) gains. The solution has given very good performance and whatever the output reference voltage variation and load disturbance of the system.

Reliability based design optimization (RBDO) problems are important in engineering applications, but it is challenging to solve such problems. In this study, a new resolution method based on the directional Bat Algorithm (dBA) is presented. To overcome the difficulties in the evaluations of probabilistic constraints, the reliable design space concept has been applied to convert the yielded stochastic constrained optimization problem from the RBDO formulation into a deterministic constrained optimization problem. In addition, the constraint handling technique has also been introduced to the dBA so that the algorithm can solve constrained optimization problem effectively. The new method has been applied to several engineering problems and the results show that the new method can solve different varieties of RBDO problems efficiently. In fact, the obtained solutions are consistent with the best results in the literature.

The bat algorithm (BA) has been shown to be effective to solve a wider range of optimization problems. However, there is not much theoretical analysis concerning its convergence and stability. In order to prove the convergence of the bat algorithm, we have built a Markov model for the algorithm and proved that the state sequence of the bat population forms a finite homogeneous Markov chain, satisfying the global convergence criteria. Then, we prove that the bat algorithm can have global convergence. In addition, in order to enhance the convergence performance of the algorithm and to identify the possible effect of parameter settings on convergence, we have designed an updated model in terms of a dynamic matrix. Subsequently, we have used the stability theory of discrete-time dynamical systems to obtain the stable parameter ranges for the algorithm. Furthermore, we use some benchmark functions to demonstrate that BA can indeed achieve global optimality efficiently for these functions.

— Amongst the multiple advantages and applications of remote sensing, one of the most important use is to solve the problem of crop classification, i.e., differentiating between various crop types. Satellite images are a reliable source for investigating the temporal changes in crop cultivated areas. In this work, we propose a novel Bat Algorithm (BA) based clustering approach for solving crop type classification problems using a multi-spectral satellite image. The proposed partitional clustering algorithm is used to extract information in the form of optimal cluster centers from training samples. The extracted cluster centers are then validated on test samples. A real-time multi-spectral satellite image and one benchmark dataset from the UCI repository are used to demonstrate robustness of the proposed algorithm. The performance of the Bat Algorithm is compared with the traditional K-means and two other nature-inspired metaheuristic techniques, namely, Genetic Algorithm and Particle Swarm Optimization. From the results obtained, we can conclude that BA can be successfully applied to solve crop type classification problems.

Rule-based classification in the field of health care using artificial intelligence provides solutions in decision-making problems involving different domains. An important challenge is providing access to good and fast health facilities. Cervical cancer is one of the most frequent causes of death in females. The diagnostic methods for cervical cancer used in health centers are costly and time-consuming. In this paper, bat algorithm for feature selection and ant colony optimization-based classification algorithm were applied on cervical cancer data set obtained from the repository of the University of California, Irvine to analyze the disease based on optimal features. The proposed algorithm outperforms other methods in terms of comprehensibility and obtains better results in terms of classification accuracy.

Bat algorithm is a population metaheuristic proposed in 2010 which is based on the echolocation or bio-sonar characteristics of microbats. Since its first implementation, the bat algorithm has been used in a wide range of fields. In this paper, we present a discrete version of the bat algorithm to solve the well-known symmetric and asymmetric traveling salesman problems. In addition, we propose an improvement in the basic structure of the classic bat algorithm. To prove that our proposal is a promising approximation method, we have compared its performance in 37 instances with the results obtained by five different techniques: evolutionary simulated annealing, genetic algorithm, an island based distributed genetic algorithm, a discrete firefly algorithm and an imperialist competitive algorithm. In order to obtain fair and rigorous comparisons, we have conducted three different statistical tests along the paper: the Student's t-test, the Holm's test, and the Friedman test. We have also compared the convergence behaviour shown by our proposal with the ones shown by the evolutionary simulated annealing, and the discrete firefly algorithm. The experimentation carried out in this study has shown that the presented improved bat algorithm outperforms significantly all the other alternatives in most of the cases.

Electricity, today, has not only become a necessity but also a tool for determining the economic standing and growth of a nation. The exponential growth in demand over the past two decades and the widening gap between demand and supply is a growing concern. This paper proposes a strategy for placing capacitors to allow deeper levels of substation voltage reduction for peak load reduction and power loss reduction. By reducing peak demand, a utility can avoid paying high prices and by minimizing system losses, savings are obtained through reduced demand and energy charges. In this paper, the application of Bat Algorithm (BA) for loss reduction is discussed.

This paper presents a two stage approach that determines the optimal location and size of capacitors on radial distribution systems to improve voltage profile and to reduce the active power loss. In first stage, the capacitor locations can be found by using loss sensitivity method. Bat algorithm is used for finding the optimal capacitor sizes in radial distribution systems. The sizes of the capacitors corresponding to maximum annual savings are determined by considering the cost of the capacitors. The proposed method is tested on 15-bus, 33 bus, 34-bus, 69-bus and 85-bus test systems and the results are presented.

— Although hyperspectral images acquired by on-board satellites provide information from a wide range of wavelengths in the spectrum, the obtained information is usually highly correlated. This paper proposes a novel framework to reduce the computation cost for large amounts of data based on the efficiency of the optimum-path forest (OPF) classifier and the power of metaheuristic algorithms to solve combinatorial optimizations. Simulations on two public data sets have shown that the proposed framework can indeed improve the effectiveness of the OPF and considerably reduce data storage costs.

The work presented in this paper is focused on the resolution of a real-world drugs distribution problem with pharmacological waste collection. With the aim of properly meeting all the real-world restrictions that comprise this complex problem, we have modeled it as a multi-attribute or rich vehicle routing problem (RVRP). The problem has been modeled as a Clustered Vehicle Routing Problem with Pickups and Deliveries, Asymmetric Variable Costs, Forbidden Roads and Cost Constraints. To the best of authors knowledge, this is the first time that such a RVRP problem is tackled in the literature. For this reason, a benchmark composed of 24 datasets, from 60 to 1000 customers, has also been designed. For the developing of this benchmark, we have used real geographical positions located in Bizkaia, Spain. Furthermore, for the proper dealing of the proposed RVRP, we have developed a Discrete and Improved Bat Algorithm (DaIBA). The main feature of this adaptation is the use of the well-known Hamming Distance to calculate the differences between the bats. An effective improvement has been also contemplated for the proposed DaIBA, which consists on the existence of two different neighborhood structures, which are explored depending on the bat's distance regarding the best individual of the swarm. For the experimentation, we have compared the performance of our presented DaIBA with three additional approaches: an evolutionary algorithm, an evolutionary simulated annealing and a firefly algorithm. Additionally, with the intention of obtaining rigorous conclusions, two different statistical tests have been conducted: the Friedman's non-parametric test and the Holm's post-hoc test. Furthermore, an additional experimentation has been performed in terms of convergence. Finally, the obtained outcomes conclude that the proposed DaIBA is a promising technique for addressing the designed problem.

A new metaheuristic based back-propagation algorithm known as Bat-BP is presented in this paper. The proposed Bat-BP algorithm successfully solves the problems like slow convergence to global minima and network stagnancy in back-propagation neural network (BPNN) algorithm. In this paper, the bat population is increased from 10 to 500 bats to detect the performance decline or incline in the Bat-BP algorithm by performing simulations on XOR and OR datasets. The simulation results show that the convergence rate to global minimum in Bat-BP is directly proportional with an increase in bats on 2-bit XOR dataset. In case of 3-bit XOR and 4-bit OR datasets, the results deteriorated with an increase in the bat population.

The cross-ambiguity function (CAF) relates to the correlation processing of signals in radar, sonar, and communication systems in the presence of delays and Doppler shifts. It is a commonly used tool in the analysis of signals in these systems when both delay and Doppler shifts are present. In this chapter, we aim to tackle the CAF synthesization problem such that the synthesized CAF approximates a desired CAF. A CAF synthesization problem is addressed by jointly designing a pair of waveforms using a metaheuristic approach based on the echolocation of bats. Through four examples, it is shown that such an approach can be used as an effective tool in synthesizing different types of CAFs.

For the optimal performance of wireless sensor networks in different areas of applications needs to maximize the coverage area of sensor nodes. The coverage of sensor nodes in monitoring region can be improved by using efficient node deployment algorithms. In this paper node deployment based on bat algorithm (BA) is proposed to enhance the coverage rate of nodes. Each bat describes solution for deployment of sensor nodes individually. In bat algorithm based node deployment grid points covered by one sensor node are excluded for remaining sensor nodes. The benefit of eliminating the grid points is that the load on remaining nodes is decreased and there is no chance of overlapping i.e. grid point is covered by only one sensor node. The simulations of node deployment based on BA and fruit fly optimization algorithm (FOA) are also demonstrated. In this paper to further increase the coverage rate of sensor nodes the performance of various parameters of bat algorithm such as loudness, pulse emission rate, maximum frequency, grid points and sensing radius has been optimized. The simulation results of node deployment based on optimized bat algorithm are also compared with BA and FOA based node deployment in terms of mean coverage rate, computation time and standard deviation. The coverage rate curve for various numbers of iterations and sensor nodes are also presented for optimized bat algorithm, BA and FOA. The results demonstrate the effectiveness of optimized bat algorithm as it achieved more coverage rate than BA and FOA.

Nature-inspired metaheuristic algorithms, especially those based on swarm intelligence, have attracted much attention in the last ten years. Firefly algorithm appeared in about five years ago, its literature has expanded dramatically with diverse applications. In this paper, we will briefly review the fundamentals of firefly algorithm together with a selection of recent publications. Then, we discuss the optimality associated with balancing exploration and exploitation, which is essential for all metaheuristic algorithms. By comparing with intermittent search strategy, we conclude that metaheuristics such as firefly algorithm are better than the optimal intermittent search strategy. We also analyse algorithms and their implications for higher-dimensional optimization problems.

A design of an optimal backstepping fractional order proportional integral derivative (FOPID) controller for handling the trajectory tracking problem of wheeled mobile robots (WMR) is examined in this study. Tuning parameters is a challenging task, to overcome this issue a hybrid meta-heuristic optimization algorithm has been utilized. This evolutionary technique is known as the hybrid whale grey wolf optimizer (HWGO), which benefits from the performances of the two traditional algorithms, the whale optimizer algorithm (WOA) and the grey wolf optimizer (GWO), to obtain the most suitable solution. The efficiency of the HWGO algorithm is compared against those of the original algorithms WOA, GWO, the particle swarm optimizer (PSO), and the hybrid particle swarm grey wolf optimizer (HPSOGWO). The simulation results in MATLAB–Simulink environment revealed the highest efficiency of the suggested HWGO technique compared to the other methods in terms of settling and rise time, overshoot, a...

The following study compares the performance of different heuristic methods to solve the problem of optimal location of transformers in power distribution networks.
The work aims to find or techniques which have the best performance for the problem at hand. In principle the solution of the problem of location of transformers is analyzed as turn restrictions of the problem themselves. Then, the optimization algorithms covered in the course are compared to later verify the performance of each of these.

In this paper, weighted differential evolution algorithm (WDE) has been proposed for solving real-valued numerical optimization problems. When all parameters of WDE are determined randomly, in practice, WDE has no control parameter but the pattern size. WDE can solve unimodal, multimodal, separable, scalable, and hybrid problems. WDE has a very fast and quite simple structure, in addition, it can be parallelized due to its non-recursive nature. WDE has a strong exploration and exploitation capability. In this paper, WDE's success in solving CEC' 2013 problems was compared to 4 different EAs (i.e., CS, ABC, JADE, and BSA) statistically. One 3D geometric optimization problem (i.e., GPS network adjustment problem) and 4 constrained engineering design problems were used to examine the WDE's ability to solve real-world problems. Results obtained from the performed tests showed that, in general, problem-solving success of WDE is statistically better than the comparison algorithms that have been used in this paper.

An optimization algorithm based on the echolocation behavior of bats is proposed for improving the load-frequency control (LFC) of interconnected power systems. A maiden attempt is made to highlight the effectiveness of Bat Algorithm in optimizing the controller parameters in load frequency control. The proposed algorithm is applied to a two area interconnected power system with Superconducting Magnetic Energy Storage (SMES) unit. The Superconducting Magnetic Energy Storage unit improves the transients of frequency and tieline power deviations against small load changes by favorable damping effect. The performance of the Bat algorithm is analyzed and simulation study is presented. The simulation results confirm the effectiveness of the proposed algorithm through fast damping steady state deviations in power and frequency in the presence of step load disturbance.

The uniqueness, firmness, public recognition, and its minimum risk of intrusion made fingerprint is an expansively used personal authentication metrics. Fingerprint technology is a biometric technique used to distinguish persons based on their physical traits. Fingerprint based authentication schemes are becoming increasingly common and usage of these in fingerprint security schemes, made an objective to the attackers. The repute of the fingerprint image controls the sturdiness of a fingerprint authentication system. We intend for an effective method for fingerprint classification with the help of soft computing methods. The proposed classification scheme is classified into three phases. The first phase is preprocessing in which the fingerprint images are enhanced by employing median filters. After noise removal histogram equalization is achieved for augmenting the images. The second stage is the feature Extraction phase in which numerous image features such as Area, SURF, holo entr...

Bat Algorithm (BA) is a nature-inspired swarm algorithm which has been applied to solve multiple real-world optimization problems. Due to a lack of balance between exploitation and exploration, multiple researchers have proposed different hybrids of BA. This paper proposes Shuffled Multi-Population Bat algorithm (SMPBat), a hybrid between two recently proposed variants of BA:-EShBAT and BatRM-S. BatRM-S – Bat algorithm with Ring Master-Slave strategy, is a multi-population variant of BA which partitions it's population according to a combination of the ring and master-slave strategies. EShBAT – Enhanced Shuffled Bat algorithm, incorporates shuffling into BA. SMPBat combines the population partitioning strategies of these two algorithms to enhance the exploitation and exploitation capabilities of BA. The evolution strategy of SMPBat also strives to retain the improved solutions. The standard BA replaces a solution with a new solution around the best. However, n this process, the information gained by that solution so far is completely lost. SMPBat changes this exploitation technique used by BA. SMPBat is compared to BatRM-S, EShBAT and BA, over 30 well-known optimisation functions. Results establish SMPBat as a significant improvement over BA, EShBAT and BatRM-S.

Heuristic optimization techniques have became very general techniques and have diffuse know areas. Main purpose of these techniques is to achieve good performance on problem. One of these techniques is Bat Algorithm (BA). BA is an optimization algorithm based on echolocation usually of bats and promoted by simulate of bats' behavior. In this study, exploration and exploitation mechanisms of bat algorithm are improved by Using descent directions(Powell). Performance of proposed and standard version of algorithm is compared on eight basic criterion test problems. Results indicate that proposed version is preferable than standard version in terms of standard deviation and other parameters.

Swarm intelligence in a bat algorithm (BA) provides social learning. Genetic operations for reproducing individuals in a genetic algorithm (GA) offer global search ability in solving complex optimization problems. Their integration provides an opportunity for improved search performance. However, existing studies adopt only one genetic operation of GA, or design hybrid algorithms that divide the overall population into multiple subpopulations that evolve in parallel with limited interactions only. Differing from them, this work proposes an improved selfadaptive bat algorithm with genetic operations (SBAGO) where GA and BA are combined in a highly integrated way. Specifically, SBAGO performs their genetic operations of GA on previous search information of BA solutions to produce new exemplars that are of high-diversity and high-quality. Guided by these exemplars, SBAGO improves both BA's efficiency and global search capability. We evaluate this approach by using 29 widelyadopted problems from four test suites. SBAGO is also evaluated by a real-life optimization problem in mobile edge computing systems. Experimental results show that SBAGO outperforms its widely-used and recently proposed peers in terms of effectiveness, search accuracy, local optima avoidance, and robustness.

Bat Algorithm (BA) is a simple and effective global optimization algorithm which has been applied to a wide range of real-world optimisation problems. Various extensions to Bat algorithm have been proposed in the past; prominent amongst them being ShBAT. ShBAT is a hybrid between BA and Shuffled Frog Leaping Algorithm-SFLA; a memetic algorithm based on food search behavior of frogs. ShBAT integrates the shuffling and reorganization technique of SFLA to enhance the exploitation capabilities of BAT. This paper proposes Enhanced Shuffled Bat algorithm (EShBAT) an extension to ShBAT. In ShBAT, different memeplexes evolve independently, with different cultures. EShBAT improves the exploitation capabilities of ShBAT by grouping together the best of each memeplex to form a super-memeplex. This super-memeplex evolves independently to further exploit the best solutions. The performance of EShBAT is verified over 30 well-known benchmark functions. Experimental results indicate a significant improvement of EShBAT over BA and ShBAT.

Wireless sensor network (WSN) has been widely used due to its vast range of applications. The energy problem is one of the important problems influencing the complete application. Sensor nodes use very small batteries as a power source and replacing them is not an easy task. With this restriction, the sensor nodes must conserve their energy and extend the network lifetime as long as possible. Also, these limits motivate much of the research to suggest solutions in all layers of the protocol stack to save energy. So, energy management efficiency becomes a key requirement in WSN design. The efficiency of these networks is highly dependent on routing protocols directly affecting the network lifetime. Clustering is one of the most popular techniques preferred in routing operations. In this work we propose a novel energy-efficient protocol for WSN based on a bat algorithm called ECO-BAT (Energy Consumption Optimization with BAT algorithm for WSN) to prolong the network lifetime. We use an objective function that generates an optimal number of sensor clusters with cluster heads (CH) to minimize energy consumption. The performance of the proposed approach is compared with Low-Energy Adaptive Clustering Hierarchy (LEACH) and Energy Efficient cluster formation in wireless sensor networks based on the Multi-Objective Bat algorithm (EEMOB) protocols. The results obtained are interesting in terms of energy-saving and prolongation of the network lifetime.

This paper proposes an effective beamformer for uniform linear arrays of half-wave dipole antennas based on binary bat algorithm (BBA) by controlling complex weights (both amplitudes and phases) excited at elements in an array. The proposed beamformer can impose adaptive nulls at interferences without knowing directions in the sidelobe region by minimizing the total output power of an array, whereas the main lobe and sidelobe levels are maintained. To demonstrate this capability, the proposal will be evaluated in several scenarios, compared to a beamformer based on binary particle swarm optimization (BPSO).

Design optimization of steel space frames is a very popular topic in structural engineering due to economy saved
in cost of the structures by optimization process. Although the final cost of a steel frame is affected by many
factors, such as material, manufacturing, erection and transportation costs, the material cost of steel comprises a
great deal of overall cost of the structure. Hence, the design optimization of steel frames is focused on weight
minimization in the literature based on the assumption that the use of least material leads to an economical
design as well in terms of final cost of a structure. This study focuses on design optimization of steel space
frames that are sized for minimum weight subject to stress, stability and nodal displacement and drift constraints
according to Allowable Stress Design-American Institute of Steel Construction (ASD-AISC) specification. Bat
inspired optimization (BIO) algorithm, which is a recently developed metaheuristic technique that exploits
echolocation behavior of bats in searching a design space, is employed to deal with the optimization problem at
hand. It is shown that BIO produces improved results with respect to other methods of metaheuristics.

Metaheuristic techniques have been recently used to counter the problems like slow convergence to global minima and network stagnancy in backpropagation neural network (BPNN) algorithm. Previously, a meta-heuristic search algorithm called Bat was proposed to train BPNN to achieve fast convergence in the neural network. Although, Bat-BP algorithm achieved fast convergence but it had a problem of oscillations in the gradient path, which can lead to sub-optimal solutions. In-order to remove oscillations in the BAT-BP algorithm, this paper proposed the addition of momentum coefficient to the weights update in the Bat-BP algorithm. The performance of the modified Bat-BP algorithm is compared with simple Bat-BP algorithm on XOR and OR datasets. The simulation results show that the convergence rate to global minimum in modified Bat-BP is highly enhanced and the oscillations are greatly reduced in the gradient path.

Image thresholding is a well known image segmentation procedure extensively attempted to obtain binary image from the gray level image. In this article, histogram based bi-level and multi-level segmentation is proposed for gray scale images using Bat Algorithm (BA). The optimal thresholds are attained by maximizing Otsu's between class variance function. The performance of BA is demonstrated by considering five benchmark (512 x 512) images and compared it with the existing algorithms such as Particle Swarm Optimization (PSO), and Bacterial Foraging Optimization (BFO) existing in the literature. The performance assessment between algorithms is carried out using prevailing parameters such as objective function, Peak Signal to Noise Ratio (PSNR), and Structural Dissimilarity (SSIM) index. The results evident that BA provides better objective function, PSNR and SSIM compared to PSO, and BFO considered in this study.

In this paper, a comparative and comprehensive study of synthesizing linear antenna array (LAA) designs, is presented. Different desired objectives are considered in this paper; reducing the maximum sidelobe radiation pattern (i.e., pencil-beam pattern), controlling the first null beamwidth (FNBW), and imposing nulls at specific angles in some designs, which are accomplished by optimizing different array parameters (feed current amplitudes, feed current phase, and array elements positions). Three different optimization algorithms are proposed in order to achieve the wanted goals; grasshopper optimization algorithms (GOA), ant lion optimization (ALO), and a new hybrid optimization algorithm based on GOA and ALO. The obtained results show the effectiveness and robustness of the proposed algorithms to achieve the wanted targets. In most experiments, the proposed algorithms outperform other well-known optimization methods, such as; Biogeography based optimization (BBO), particle swarm optimization (PSO), firefly algorithm (FA), cuckoo search (CS) algorithm, genetic algorithm (GA), Taguchi method, self-adaptive differential evolution (SADE), modified spider monkey optimization (MSMO), symbiotic organisms search (SOS), enhanced firefly algorithm (EFA), bat flower pollination (BFP) and tabu search (TS) algorithm

In this paper, a day-ahead profit-maximizing energy management scheme for a grid-tied microgrid operation is proposed. The microgrid contains various types of distributed energy resources (DERs) and an inverter-interfaced battery-bank storage system. The average of day-ahead hourly forecasted data for loads, wind speed, and solar radiation are inputted into the framework of energy management (EMF). To optimize the microgrid performance, EMF determines the hourly dispatch of reactive and active power for each DER. Also, it specifies the discharging and charging times of the energy storage system and the on-load tap changer position setting of the transformer connected to the main grid. The main aim is to maximize the revenue of microgrid meeting all technical limitations. The main grid can sell/buy reactive and active powers to/from the microgrid with a variable daily energy price of the market. A collective rule base-BAT algorithm is implemented as a solver of the energy management optimization problem for a grid-tided microgrid. Furthermore, the ability of the suggested EMF is proved in comparison with recent approaches.