Non-dominated Sorting Genetic Algorithm-II (NSGA-II)

In this article, the multi-objective optimization of cylindrical aluminum tubes under axial impact load is presented. Absorbed energy and specific absorbed energy are considered as objective functions while the mean crush load should not exceed allowable limit. The geometric dimensions of tubes including diameter, length and thickness are chosen as design variables. The Non-dominated Sorting Genetic Algorithm -II (NSGAII) is applied to obtain the Pareto optimal solutions. A back-propagation neural network (ANN) is constructed as the surrogate model to formulate the mapping between the variables and the objectives. The finite element software ABAQUS/Explicit is used to generate the training and test sets for the ANNs. Validating the results of finite element model, several impact tests are carried out using drop hammer.

We address the problem of optimizing a spacecraft trajectory by using three different multi-objective evolutionary algorithms: i) Non-dominated sorting genetic algorithm, ii) Pareto-based ranking genetic algorithm, and iii) Strength Pareto genetic algorithm. The trajectory of interest is an orbit transfer around a central body when the spacecraft uses a lowthrust propulsion system. We use a Lyapunov feedback control law called the Q-law to create an eligible trajectory, while the Q-law control parameters are selected with the multiobjective algorithms. The optimization goal is to minimize flight time and consumed propellant mass simultaneously. The Pareto fronts (trade-off surface between flight time and propellant mass) produced by these algorithms are evaluated by means of two quantitative metrics: 1) size of the dominated space and 2) coverage of two Pareto fronts. With the two metrics, a hierarchy of algorithms emerged. The nondominated sorting genetic algorithm and the strength Pareto genetic algorithm are equally effective, and they outperform the Pareto-based ranking genetic algorithm.

Multi-objective genetic algorithms (MOGA) are used to optimize a low-thrust spacecraft control law for orbit transfers around a central body. A Lyapunov feedback control law called the Q-law is used to create a feasible orbit transfer. Then, the parameters in the Q-law are optimized with MOGAs. The optimization goal is to minimize both the flight time and the consumed propellant mass of the trajectory created by the Qlaw, and consequently to find Pareto-optimal trajectories. To improve the qualities of the obtained Pareto-optimal solutions, elitism and a diversity-preservation mechanism are incorporated into MOGA. The MOGA performance with and without the new mechanisms are systematically compared and evaluated with quantitative metrics. The new mechanisms significantly improve the convergence and distribution of the resulting Pareto front for the low-thrust orbit-transfer optimization problem. The new mechanisms also improve the statistical stability and the computational efficiency of the algorithm performance.

UK electricity demand is expected to increase by about 50-100% over the next 30 years, driven primarily by the UK’s net zero policy. The addition of renewables, EV’s and other low carbon technologies introduces more uncertainty into the system driving the need for flexibility services. These flexibility services can be provided by traditional generators, but EV’s, storage, and demand side response are expected to provide a larger share of this. It is evident that domestic customers can provide flexibility services to the system, but modelling their responses, behaviours will be difficult. They are social and emotive actors who exhibit non-stationary behaviours that are difficult to represent and are often ignored or oversimplified. There is therefore a need for an extensible tool to simulate customer interactions with aggregators and system operators. This thesis presents an Agent Based Modelling (ABM) framework that incorporates these behaviours. Customers are represented using behavioural rules using a modified “Agent_Zero” framework. Aggregators are represented as commercial entities selecting appropriate business models for the markets they operate in. Aggregators compete against other aggregators and compete for customers in this simulation. Aggregators also face new risks in this evolving market that will require valuation and risk control. A real option model of the risk associated with aggregator operation is developed and incorporated into a case study using 55,000 customers, representative of a UK city the size of Dundee or York. These aspects provide complex behavioural interactions that provides a rich set of outputs. Analysis of the results show that aggregator business models evolve through time and that business model choice is a complex one. Price impacts of emotions/networked social influences are significant (~30-50%). Simulation output can be used as an input into future market designs and provides benefits to different stakeholders including regulators, generators, customers and future aggregators.

This paper outlined the basic concepts of two selected types of hybrid multi-level inverters that were capacitor voltage-source dependent. A natural capacitor voltage balancing involving hysteresis with redundant switching states was analyzed in this paper. Simulations using MATLAB 7.14 were carried out for an unbalanced and a well-balanced capacitor voltage supply under 0V, 100V and 200V pre-charged capacitor voltage conditions. A comparative analysis of the two types of hybrid multi-level inverters (Type A and Type B) with pertinent to %THD values of [35.77% and 23.79%] for Type A and [92.10% and 84.94%] for type B during fault occurrence were evaluated in this work. These harmonic values can lead to thermal run away and total malfunction of the semi-conductor switches. An experimental vali-dation of Type A hybrid multi-level inverter with a three level flying capacitor input stage and H-bridge output stage using a three phase RL-Load of 15Ω and 45mH was accomplished in the labora...

Vehicular fog computing (VFC) offers a promising paradigm for reducing latency and energy consumption by utilizing nearby edge resources. However, efficient and scalable resource management remains a significant challenge, particularly due to dynamic network topologies, resource requirements, and high quality of service (QoS). Traditional metaheuristics, such as genetic algorithm (GA) and PSO, are limited in convergence speed and solution quality under these constraints. This paper presents the improved NSGA-II+, a state-of-the-art multi-objective evolutionary model that enhances NSGA-II and NSGA-III through dynamic population adaptation, Pareto front-based selection, and premature convergence prevention. Experimental comparisons in both common and very dense vehicle environments, comprising up to 1,000 vehicles and 2,000 tasks, show that NSGA-II+ significantly outperforms the baseline algorithms, reducing average delay by 72.55% (compared to NSGA-II) and 71.75% (compared to NSGA-III), and reducing energy costs by 70.96% (compared to NSGA-II) and 70.75% (compared to NSGA-III). This enhances NSGA-II+'s ability to handle both dynamic terrain and resource diversity. Its flexibility and strong balance between exploration and exploitation also make it an attractive solution for immediate, energy-efficient deployment in intelligent transportation systems.

Evolutionary approach based meta-heuristics have gained prominence in recent years for solving multi objective optimization problems (MOP). Multi Objective Evolutionary Approaches (MOEA) has substantial success across a variety of real-world engineering applications. Master Production Scheduling (MPS) is a plan that determines optimal values of products to be produced. As an MPS can effectively and efficiently synchronize the operations in any organization, it can be posed as one of multi objective parameter optimization problems. The present selective survey attempts to provide a general overview of the work that has been done in the last two decades in MOEAs in line with Master Production Scheduling problems.

Single Ended Primary Inductance Converter (SEPIC) which is commonly devoted as a switched power supply in many applications is presented in this work to conclude the effect of switching frequency on the output voltage time response, elements values, spectral analysis of output voltage and efficiency. For this goal, three circuits of SEPIC converters were designed at switching frequencies of 100KHZ, 400KHZ and 600KHZ. The operating conditions of the three circuits which include input voltage range, output voltage value, output voltage ripple and inductor ripple current are kept the same while the inductors and capacitors of the converters are calculated according to each switching frequency. The LTC1871 controller is utilized to produce a required duty cycle (D) to drive the converter. The implementation of the circuits was carried out using LTspice XVII. The results show that the higher switching leads to better time response of the output voltage with small values of passive components of the converter. However, the efficiency of the converter is decreased and the noise due to harmonics is increased. Therefore, the selection of switching frequency must be set accurately according to converter functions.

This paper presents the design and implementation of a Modular Multilevel Converter (MMC) integrated with a motor drive system. The MMC topology offers numerous advantages such as improved voltage waveform quality, reduced harmonics, and enhanced reliability, making it an attractive choice for high-power motor drive applications. The proposed system architecture is modular in nature, facilitating scalability and flexibility to meet varying power requirements. Design considerations including sub module configuration, control strategies, and modulation techniques are discussed in detail. Furthermore, the practical implementation aspects of the MMC with the motor drive system are presented, addressing challenges related to hardware realization, control algorithm implementation, and performance evaluation. Experimental results validate the effectiveness of the proposed system in achieving high-quality output voltage waveforms and efficient motor operation across a range of operating conditions. Overall, the presented design and implementation offer a promising solution for high-power motor drive applications, leveraging the benefits of modular multilevel converter technology.

This research paper presents an innovative approach to maximizing power extraction from solar photovoltaic (PV) arrays under partial shading conditions by employing the Hippopotamus Optimization Algorithm (HOA). Partial shading is a common issue that significantly reduces the efficiency of PV systems by creating multiple local maxima on the P-V curve, thereby challenging conventional Maximum Power Point Tracking (MPPT) methods. To address this, we propose an adaptive reconfiguration strategy for the PV array, optimized using HOA, which successfully moderates the impacts of shading and enhances overall energy yield. The Hippopotamus Optimization Algorithm, inspired by the foraging behavior of hippopotamuses, is utilized for its robust global search capabilities and fast convergence. The algorithm dynamically adjusts the arrangement of the PV module to locate and maintain operation at the global maximum power point. Our methodology involves simulating various shading scenarios and evaluating the performance of HOA-based reconfiguration against traditional MPPT techniques. Simulation results demonstrate a significant improvement in power extraction efficiency, with the HOA-based reconfiguration strategy consistently achieving higher power output compared to conventional methods. Additionally, the proposed system exhibits enhanced adaptability to changing shading patterns, ensuring reliable performance in diverse environmental conditions. The findings highlight the potential of the Hippopotamus Optimization Algorithm as a powerful tool for optimizing PV schemes, particularly in scenarios where shading is inevitable. This study contributes to the advancement of renewable energy technologies by offering a novel solution for improving the efficiency and reliability of solar PV arrays.

This paper presents energy management in DC Microgrid. Microgrids are a growing power generating source in remote areas than the utility grid. It can be operated as a standalone & grid-connected to serve the entities. This paper concentrates on the DC Microgrid and manages the energy within the system when the grid suffers from an outage of service, and unbalanced load conditions in the grid. During grid conditions, the buses connected to it should synchronize with the grid. The voltage in the buses is same, that is verified in this paper work through controllers to the converter are also discussed. From the outcome of simulation, THD satisfies the IEC 61000-3-2 standards. The energy management in DC Microgrid is achieved by connecting hybrid source is also implemented to prevent the grid outage conditions.

While the increased adoption of electric vehicles (EVs) is a promising alternative to reduce CO2 emissions, it creates new challenges for the power grid due to increased energy demand and power quality (PQ) issues. These impacts vary depending on several factors such as the level of EV adoption, charging technology, network voltage level, charging patterns, charging station location, battery condition, and driving habits. Analyzing these impacts and developing solutions, such as characterizing the demand curve for charging stations and understanding EV charging patterns, is crucial to ensure a sustainable transition to an electrified EV future. A study using the ZIP load model that represents voltage dependence by combining constant impedance (“Z”), constant current (“I”), constant power (“P”) components, and phasor measurement units (PMUs) demonstrates the effectiveness of EV demand characterization. The importance of this aspect for grid stability and charging management is highlighted.

The growing popularity of direct current (DC) power sources, energy storage systems, and DC loads has recently shifted the focus away from alternating current (AC) microgrids and towards DC-only systems. However, smart and energy-efficient building integration and effective microgrid administration are prerequisites. Direct current microgrids, which include solar modules as their principal power source, an energy storage device (battery), and an essential DC load, may have their energy consumption managed with the help of our study. Within the microgrid (MG) architecture, the DC-DC boost converter enables the PV module to operate in many modes, one of which is Maximum Power Point Tracking (MPPT). In order to link the battery and supercapacitor to the DC bus, the system also makes use of a DC-DC bidirectional converter. By restricting the charging/discharging currents and state of charge (SoC) of the batteries and supercapacitors, the proposed control approach seeks to manage power flow within the microgrid while considering their lifespan. This method simplifies and improves upon previous approaches to DC load supply by doing away with complicated energy management or maximum power point tracking methods. We conceived and simulated the DC microgrid under investigation thanks to Matlab/Simulink. We fulfil the load demand while maintaining system stability and performance. We provide a presentation and discussion of findings from controller design, analysis, and simulation validation for several operating modes.

Once clean, renewable energy sources are used to charge the batteries in electric vehicles (EVs), the vehicles can produce zero gas emissions, greatly improving the environment. EVs and other distributed energy storage devices can be used in a smart microgrid to deliver energy to the loads throughout highest times, reducing the impact of load shading and improving the quality of the electricity. To achieve these goals of energy balance between EVs, the grid, and renewable energy sources, an isolated hybrid multiport converter is required. This paper develops an optimized isolated multi-port DC-DC converter for controlling power flow in multiple directions in an EV. This converter contains a dc-dc unidirectional converter, a bidirectional dc-dc converter, a triple active bridge (TAB), and a multi-port dual active bridge converter. Additionally, the Optimal Controller (OC) is developed to manage the power between the EV and the battery. The power flow is achieved by using the Modified Coati Optimization Algorithm (MCOA). In the MCOA, the optimal gain parameter of the converter is selected. In the planned technique, a bidirectional DC-DC converter can be considered to interconnect the EV battery towards deliver bidirectional power flow ability with the battery. The proposed approach is executed in MATLAB, and presentations are assessed by considering performance measures. Moreover, it is contrasted with the traditional approaches of particle swarm optimization (PSO) and grey wolf optimization (GWO).

The primary focus of this study is to develop an energy management system that regulates the energy transfers between the hybrid microgrid system and the loads connected to it, and the grid via MATLAB/Simulink so as to model the flow of energy. The secondary aim is to make recommendations aimed at the charging and discharging of what is referred to as the hybrid energy storage system (HESS). The results indicate that the proposed algorithm successfully carried out the required task of bridging the HESS charging to discharging ratio in relation to the different operating conditions as well as power management between the microgrid and the network. In this application, a stronger charging power might be employed on the HESS. It has been seen that the HESS is more likely to complete charging within a short time than the greater charging power. A more advanced and efficient energy management system is critical to the microgrid system so that the generation can keep pace with the requirements of the load profile. It is important to take account of load forecasting with regard to power planning and executing so as to know the most suitable action that should be taken. To achieve a general reduction in the cost of operation, in this paper, we propose the use of an advanced Energy Management System (EMS), Model Predictive Control (MPC), to effectively manage the allocation of power in the microgrid.

Increasing use of electrical energy at domestic consumer level due to increased usage of electrical appliances, compounded with increased electric vehicle usage will cause severe stress on existing distribution networks necessitating exp ensive infrastructural changes. An alternative would be to increase consumer level energy production through renewable sources so that dependence on the grid is eliminated altogether, or minimized. This would also help reduce overall carbon footprint. This pap er discusses a viable implementation of a DC micro-grid with battery storage that is compatible with the majority of existing loads and also suggests modifications in certain loads to make them compatible. The micro-grid also allows for DC charging of electric vehicles at different voltage levels. The compatibility with the proposed Microgrid, of loads that can function without modifications and those that require minor changes is analyzed and verified experimentally.

Abstract. We dwell in largely non-technical terms on the essential differences between single-objective optimization and multiple-objective optimization. We argue in particular that single-objective approaches to real-world problems are almost invariably simplifications of the real-problem which make many ideal solutions unreachable to the optimization method. We promote the use of multi-objective optimization methods, particularly those arising from the evolutionary computation community. We point out that the state of the art in the field of ...

This dissertation presents a value-driven approach to optimising SME supply chain performance during the transition from Industry 4.0 to Industry 5.0, integrating multi-objective optimisation, evolutionary computation, and strategic transformation frameworks. Using the NSGA-II genetic algorithm, the research models trade-offs between cost, delivery time, and system resilience, contextualised within SME resource constraints and operational realities. A tailored simulation environment replicates SME manufacturing workflows, enabling phased digital transformation testing. The study further analyses technical, organisational, and workforce barriers to Industry 4.0 adoption and outlines requirements for the shift toward human-centric, sustainable Industry 5.0 principles. Hybrid performance metrics are proposed, combining lean efficiency goals with value-based indicators, and a modular implementation roadmap is provided to support automation adoption, digital integration, and resilience-building. The findings offer actionable insights for engineers, consultants, and SME leaders balancing operational performance with long-term value creation in the face of technological transition.

Electro-hydraulic power steering (EHPS) systems are widely used in commercial vehicles due to their adjustable power assist and energy-saving advantages. In this paper, a dynamic model of the EHPS system is developed, and quantitative expressions for three evaluation indexes, steering road feel, steering sensibility and steering energy loss, are derived for the first time. A multi-objective collaborative optimization model of the EHPS system is then established, which consists of one total system and three parallel subsystems, based on collaborative optimization theory. Considering the coupled variables of each subsystem, the total system is optimized by a multi-objective algorithm, while the subsystems are optimized by a single-objective algorithm. The optimization results demonstrate that the average frequency domain energy of the steering road feel is increased by 69.1%, the average frequency domain energy of steering sensitivity is reduced by 19.2%, and steering energy consumption is reduced by 10.8% compared to the initial value. The non-dominated sorting genetic algorithm-II (NSGA-II) shows superior comprehensive performance compared to the other two multi-objective algorithms, and the optimization performance can be further improved by setting appropriate algorithm parameters.

In this study a multi-objective formulation is proposed for designing a supply chain of perishable products including suppliers, plants, distributors, and customers under sustainable development. In addition to the studies of the literature, direct shipment between producers and customers and also alternative products possibility are allowed. In this problem the objectives like facilities establishment costs, transportation costs, negative environmental impacts, and social impact (fixed and variable employment rates) are optimized simultaneously. As in real situations, most of the transportation activities of such supply chain are performed by hiring transportation devices, the open routing logic is applied to form the travelling path of each hired transportation device. Furthermore, the possibility of direct shipment from the plants to the customers is considered in order to increase profitability of the plants. Because of the NP-hard nature of the supply chain design problems, some meta-heuristic solution approaches of the literature are modified to multi-objective form and applied to solve the problem. Several test problems from small to large sizes are generated randomly to evaluate the meta-heuristic algorithms. As a result, among the proposed algorithms, the multi-objective grey wolf optimizer (MGWO) perform better than others by considering four wellknown evaluation metrics. At the end, a case study from perishable products supply chain of Iran is solved and analyzed to show the applicability of the proposed problem.

Computational grid (CG) provides a wide distributed platform for high end compute intensive applications. Job scheduling in a computational grid is an activity in which the submitted jobs are assigned on the nodes of the grid with the objective to optimise some characteristic parameter. One of such important characteristic parameters is makespan. Makespan depends on the number of jobs, number of nodes and job execution time. As such, scheduling problem is NP-Hard therefore soft computing techniques are often applied to get an optimal schedule. Genetic algorithm (GA), a search procedure based on the evolutionary computation, is often applied to solve a large class of complex optimisation problems. Immune genetic algorithm (IGA) is a hybrid approach which incorporates the features of immune algorithm in GA. GA takes longer to converge and also has the possibility to get trapped in local optima nearing convergence. IGA is more efficient as it explores the solution space based on the affinity, an entropy based property. This work applies IGA to schedule the submitted jobs on the grid nodes for the optimal makespan. Simulation of the proposed model is done to evaluate its performance and it exhibit effective result.

Scheduling a job on the grid is an NP Hard problem, and hence a number of models on optimizing one or other characteristic parameters have been proposed in the literature. It is expected from a computational grid to complete the job quickly in most reliable grid environment owing to the number of participants in the grid and the scarcity of the resources available. Genetic algorithm is an effective tool in solving problems that requires sub-optimal solutions and finds uses in multi-objective optimization problems. This paper addresses a multi-objective optimization problem by introducing a scheduling model for a modular job on a computational grid with a dual objective, minimizing the turnaround time and maximizing the reliability of the job execution using NSGA -II, a GA variant. The cost of execution on a node is measured on the basis of the node characteristics, the job attributes and the network properties. Simulation study and a comparison of the results with other similar models reveal the effectiveness of the model.

Three-dimensional heat transfer characteristics and pressure drop of water flow in a set of rectangular microchannels are numerically investigated using Fluent and compared with those of experimental results. Two metamodels based on the evolved group method of data handling (GMDH) type neural networks are then obtained for modelling of both pressure drop (DP) and Nusselt number (Nu) with respect to design variables such as geometrical parameters of microchannels, the amount of heat flux and the Reynolds number. Using such obtained polynomial neural networks, multi-objective genetic algorithms (GAs) (non-dominated sorting genetic algorithm, NSGA-II) with a new diversity preserving mechanism is then used for Pareto based optimization of microchannels considering two conflicting objectives such as (DP) and (Nu). It is shown that some interesting and important relationships as useful optimal design principles involved in the performance of microchannels can be discovered by Pareto based multi-objective optimization of the obtained polynomial metamodels representing their heat transfer and flow characteristics. Such important optimal principles would not have been obtained without the use of both GMDH type neural network modelling and the Pareto optimization approach.

Outranking based models as one of the most important multicriteria decision methods need the definition of large amount of preferential information called "parameters" from decision maker. Because of the multiplicity of parameters, their confusing interpretation in problem context and the imprecise nature of data, Obtaining all these parameters simultaneously specially in large scale realistic credit problems which requires real time decision making is very complex and time-consuming. Preference Disaggregation approach infers these parameters from the holistic judgements provided by decision maker. This approach within multicriteria decision methods is equivalent to machine learning in artificial intelligence discipline. Under this approach this paper proposes a new learning method in which Genetic Algorithm(GA) in an evolutionary process induces all , ELECTRE TRI model parameters from training set then at the end of this process, classification is done on testing set by i...

The time-cost trade-off problem (TCTP) presents a significant challenge in construction management, requiring a balance between project duration and associated costs for successful completion. This study evaluates the performance of an arithmetic optimization algorithm (AOA) for solving the TCTP. AOA integrates non-dominated sorting (NDS) to generate Pareto-optimal solutions that address both time and cost objectives. The methodology involves testing the AOA on three case studies representing small-and medium-scale construction projects with 18, 29, and 63 activities. Comparative analyses with traditional metaheuristic algorithms, such as ant colony optimization, genetic algorithms, and particle optimization algorithms, reveal that NDS-AOA delivers competitive results, particularly in smaller projects, that achieve lower costs and faster computation times. However, its effectiveness decreases in medium-scale projects, indicating scalability limitations. Numerical tests suggest that while AOA is wellsuited for small to medium projects, it requires further enhancements, such as hybridization with other techniques, to effectively handle larger-scale problems.

Trade-off problem requires a balance between the project objectives taken as time and cost, known as the NP-hard optimization problem. Due to this, any metaheuristic algorithm like the arithmetic optimization algorithm (AOA) gaining popularity for its simplicity and fast convergence might suffer from finding the optimal solution(s) when the construction project scale is increasing. To improve the overall optimization ability and overcome the drawbacks of the plain AOA in solving the timecost trade-off optimization problems, in this study, the generation jumping phase of the opposition-based learning strategy is proposed and integrated with AOA. This enhancement realizes complementary advantages of the opposition jumping rate to avoid falling into the local optimum and premature convergence. Construction engineering projects involving 63, 81, and 146 activities are applied to verify the effectiveness and feasibility of the enhanced AOA. The experimental results reveal that the proposed model is more effective than the plain AOA and other emerging algorithms for simultaneously optimizing the trade-off problems in construction management.

Disruption on a supply chain provokes lost that should be minimized looking for alternative suppliers. This solution involves a strategy to manage the impact of the disruption and thus to recuperate the supply chain. Difficulty of the management is the diversity of involved factors such that turns complex to provide or choice a solution among the possible ones. Depending on the objective(s) to optimize are the strategy to follow and the solution to choice. In this work the Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization NSGA-II is used as the strategy to generate and optimize (minimize) solutions (lost) in front of a disruption. The included objectives are cost, risk and the place of facilities supporting the supply chain recuperation. These objectives are combined to generate possible solutions and to choice one that provides a proposal to minimize the disruption impact on a delimited period of time. Advantage of NSGA-II utilization is the provision of a practical formal and computational tool to analyze different scenarios without simplifies the complexity of a standard real supply chain. The illustrative exercise presents recovery scenarios for a crude oil refinery supply chain.

The design of offshore wind farms is computationally challenging, requiring the simultaneous optimisation of many conflicting objectives. Solving this problem is of paramount importance if society is to meet ambitious net zero goals. The problem is solved by identifying an optimal arrangement of individual turbines such that all objectives are optimised. However, a single solution does not exist due to the inherent conflict between objectives, and a set of solutions must be identified. As well as the challenge in generating optimal solution sets, there exists a decision support task if the solutions are to be effectively presented to a decision maker. This study focuses on six key objectives: wind farm efficiency, annual energy production, electric cable length, number of wind turbines, levelised cost of energy, and total area. Two evolutionary algorithms, NSGA-II and NSGA-III, were employed to explore the solution space efficiently. Performance evaluation was performed using spacing, generational distance, and hypervolume metrics. The aforementioned algorithms and metrics were applied to three wind farm layouts: a discrete layout and two continuous layouts. The NSGA-III algorithm was shown to perform better than its predecessor (NSGA-II). The difference was small, albeit significant. Previous works (e.g., Rodrigues et al. (2016); Mytilinou and Kolios ( )) in which many-objective optimisations were discussed provided little insight into the visualisation and interpretation of the results. While the mentioned work used parallel coordinate plots, this work provides a deeper insight by presenting the results via Principal Component Analysis (PCA) and Multi Dimensional Scaling (MDS) plots. The best solution, containing 6188 wind farm layouts, was found by the NSGA-III algorithm on a continuous wind farm layout with repair mechanism. From the best solution, the wind farms containing 27, 102 and 160 wind turbines were selected and compared with the real wind farms located around the UK. It was demonstrated that the optimiser could identify better wind farm layouts concerning annual energy production, efficiency, and LCOE than the real wind farm layouts of Rhyl Flats and Greater Gabbard.

This paper presents a comparative analysis of the results obtained with two different genetic algorithms, NSGA-II and SPEA-II, in the framework of load management activities in electric power systems. The multiobjective problem deals with the identification and the selection of suitable control strategies to be applied to groups of electric loads aimed at reducing maximum power demand at the sub-station level, maximizing profits with selling of electricity and minimizing the discomfort caused to the end-users. The comparative analysis of the algorithms’ performance is done based on the attainment surface approach. Besides, it is shown that this approach can be used as a vehicle to introduce the decision maker’s preferences in the evaluation process.

Aggregation functions largely determine the convergence and diversity performance of multi-objective evolutionary algorithms in decomposition methods. Nevertheless, the traditional Tchebycheff function does not consider the matching relationship between the weight vectors and candidate solutions. In this paper, the concept of matching degree is proposed which employs vectorial angles between weight vectors and candidate solutions. Based on the matching degree, a new modified Tchebycheff aggregation function is proposed, which integrates matching degree into the Tchebycheff aggregation function. Moreover, the proposed decomposition method has the same functionality with the Tchebycheff aggregation function. Based on the proposed decomposition approach, a new multiobjective optimization algorithm named decomposition based multi-objective state transition algorithm is proposed. Relevant experimental results show that the proposed algorithm is highly competitive in comparison with other state-of-the-art multiobjetive optimization algorithms.

Cloud computing provides computing resources with elasticity following a pay-as-you-go model. This raises Multi-Objective Optimization Problems (MOOP), in particular to find Query Execution Plans (QEPs) with respect to users' preferences being for example response time, money, quality, etc. In such a context, MOOP may generate Pareto-optimal front with high complexity. Pareto-dominated based Multi-objective Evolutionary Algorithms (MOEA) are often used as an alternative solution, like Non-dominated Sorting Genetic Algorithms (NSGAs) that provide better computational complexity. This paper presents NSGA-G, a NSGA based on Grid Partitioning for improving complexity and quality of current NSGAs. Experiments on DTLZ test problems using Generational Distance (GD), Inverted Generational Distance (IGD) and Maximum Pareto Front Error prove the relevance of our solution.

Data sharing is important in the medical domain. Sharing data allows large-scale analysis with many data sources to provide more accurate results (especially in the case of rare diseases with small local datasets). Cloud federations consist in a major progress in sharing medical data stored within different cloud platforms, such as Amazon, Microsoft, Google Cloud, etc. It also enables to access distributed data of mobile patients. The pay-as-you-go model in cloud federations raises an important issue in terms of Multi-Objective Query Processing (MOQP) to find a Query Execution Plan according to users preferences, such as response time, money, quality, etc. However, optimizing a query in a cloud federation is complex with increasing heterogeneity and additional variance, especially due to a wide range of communications and pricing models. Indeed, in such a context, it is difficult to provide accurate estimation to make relevant decision. To address this problem, we present Dynamic Regression Algorithm (DREAM), which can provide accurate estimation in a cloud federation with limited historical data. DREAM focuses on reducing the size of historical data while maintaining the estimation accuracy. The proposed algorithm is integrated in Intelligent Resource Scheduler, a solution for heterogeneous databases, to solve MOQP in cloud federations and validate with preliminary experiments on a decision support benchmark (TPC-H benchmark).

In this paper, we propose a genetic algorithm-based method for evaluation of operational plans within effects-based planning. We formulate the effects-based planning problem as a bi-objective optimization problem, in which the distance from the initial state to the current state (𝑔) and the distance from the current state to the desired end state (ℎ) are minimized. To solve the problem, we adopt Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Considering an expeditionary operation scenario, we simulate a subset of possible plans and present the decision maker with a set of promising plans which are capable of approaching the desired end state efficiently. In order to discuss the efficiency and effectiveness of the algorithm, we compare the results of NSGA-II with the results of A*. The computational results show that NSGA-II is much more efficient than A* with regard to 𝑔. On the other hand A* is a little more effective with regard to ℎ.

The spreading of advanced constituive models, needed to model complex phenomena, makes necessary to solve difficult parameter identification problems. The need of multiple tests to fully characterize the experimental behaviour makes the parameter identification problem a multi objective one. Unlike conventional techniques, based on the formulation of an aggregate scalar objective function, in the present work the problem is addressed using a new multi objective algorithm obtained extending the continuous Ant Colony Optimization algorithm. Mathematical tests and application to a real world problem are performed and different performance measures are used to asses the performance of the approach.

This paper presents a hierarchical and easy configurable framework for the implementation of distributed evolutionary algorithms for multiobjective optimization problems. The proposed approach is based on a layered structure corresponding to different execution environments like single computers, computing clusters and grid infrastructures. Two case studies, one based on a classical test suite in multiobjective optimization and one based on a data mining task, are presented and the results obtained both on a local cluster of computers and in a grid environment illustrates the characteristics of the proposed implementation framework.

This paper focuses on the building envelope design in a public space of healthcare facility. The aim of the study is to achieve feasible envelope design alternatives by maximizing daylight factor and minimizing the envelope cost, which are conflicting objectives. To deal with this problem, we used Non-dominated Sorting Genetic Algorithm-II and Self-adaptive Multi-Objective Ensemble Differential Evolution algorithms. According to comparisons, NSGA-II algorithm presented slightly better results for the building envelope design of a healthcare public space.

In this paper, the optimization of multi-pass milling has been investigated in terms of two objectives: machining time and production cost. An advanced search algorithm-parallel genetic simulated annealing (PGSA)was used to obtain the optimal cutting parameters. In the implementation of PGSA, the fitness assignment is based on the concept of a non-dominated sorting genetic algorithm (NSGA). An application example is given using PGSA, which has been used to find the optimal solutions under four different axial depths of cut on a 37 SUN workstation network simultaneously. In a single run, PGSA can find a Pareto-optimal front which is composed of many Pareto-optimal solutions. A weighted average strategy is then used to find the optimal cutting parameters along the Pareto-optimal front. Finally, based on the concept of dynamic programming, the optimal cutting strategy has been obtained.

Data sharing is important in the medical domain. Sharing data allows large-scale analysis with many data sources to provide more accurate results. Cloud federations can leverage sharing medical data stored in different cloud platforms, such as Amazon, Microsoft, etc. The pay-as-you-go model in cloud federations raises important issues of Multi-Objective Optimization Problems (MOOP) related to users' preferences, such as response time, money, etc. However, optimizing a query in a cloud federation is complex with increasing the variety, especially due to a wide range of communications and pricing models. The variety of virtual machines configuration also leverages the high complexity in generating the space of candidate solutions. Indeed, in such a context, it is difficult to provide accurate estimations and optimal solutions to make relevant decisions. The first challenge is how to estimate accurate parameter values for MOOPs in a cloud federation consisting of different sites. To address the accurate estimation of parameter values problem, we present the Dynamic Regression Algorithm (DREAM). DREAM focuses on reducing the size of historical data while maintaining the estimation accuracy. The second challenge is how to find an approximate optimal solution in MOOPs using an efficient Multi-Objective Optimization algorithm. To address the problem of finding an approximate optimal solution, we present Non-dominated Sorting Genetic Algorithms based on Grid partitioning (NSGA-G) for MOOPs. The proposed algorithm is integrated into the Intelligent Resource Scheduler, a solution for heterogeneous databases, to solve MOOP in cloud federations. We validate our algorithms with experiments on a decision support benchmark.

Purpose:
Due to the increase in energy demand and the effects of global warming, energy-efficient buildings have gained significant importance in the modern construction industry. To create a suitable framework with the aim of reducing energy consumption in the building sector, the external walls of a residential building were considered with two criteria of global warming potential and energy consumption.

Design/methodology/approach:
In the first stage, to achieve a nearly zero-energy building, energy analysis was performed for 37 different states of thermal insulation. Then, the insulation materials' life cycle assessment was performed. These results were used to find a set of optimal modes in the Pareto front by using non-dominated sorting genetic algorithm II multi-objective genetic algorithm. Thus, based on the data obtained from this method, it was possible to compare and choose different thermal insulation materials based on the distance from the Pareto front, reducing the environmental effects.

Findings:
The results showed that replacing the windows was possible to save 3.24 % in energy consumption. Also, selecting the proper insulation reduced energy consumption value by 6.3.13%. Finally, this building can save 69.31 % of energy consumption compared to the base building by following the zero-energy building standard. As a result, the Pareto curve was introduced as a guide for the optimal design of the building's wall insulation.

Originality/value:
The proposed method provides designers with a framework for latent carbon analysis to access quickly and select optimal scenarios. It can also be used without restrictions for other decisions with different goals and criteria.

Quicksort has been described as the best practical choice for sorting. It is faster than many algorithms for sorting on most inputs and remarkably efficient on the average. However, it is not efficient in the worst case scenarios as it takes O(n 2). Research efforts have been made to enhance this algorithm for the worst case scenarios by improving the way the algorithm chooses its pivot element for partitioning, but these approaches have the disadvantage of increasing the algorithm's average computing time. Introsort was, however, developed to overcome this limitation. This paper presents an approach that uses Bidirectional Bubble Sort to improve the performance of Introsort. Instead of using Insertion Sort as the last step of the sorting algorithm for small lists, the approach uses Bidirectional Bubble Sort. The results of the implementation and experimentation of this algorithm compared with Introsort shows its better performance in the worst case scenario as the size of the list increases.

Hollow fiber membrane separation modules are used extensively in industry for a variety of separation processes. In most cases, conflicting requirements and constraints govern the optimal choice of decision (or design) variables. In fact, these optimization problems may involve several objectives, some of which must be maximized, while the others minimized simultaneously. Often, a set of equally good (non-dominated or Pareto optimal) solutions exist. In this study, a membrane separation module for the dialysis of beer has been taken as an example system to illustrate the multi-objective optimization of any membrane module. A mathematical model is first developed and 'tuned' using some experimental results available in the literature. The model is then used to study a few simple multi-objective optimization problems using the non-dominated sorting genetic algorithm (NSGA). Two objective functions are used: the alcohol removal (%) from the beer is maximized, while simultaneously minimizing the removal of the 'extract' (taste chemicals). Pareto optimal solutions are obtained for this module. It was found that the inner radius of the hollow fiber is the most important decision variable for most cases. Another optimization problem using the cost as the third objective function is also solved, using a combination of the ε-constraint method and NSGA. It is found that the Pareto solutions lie on a curve in the three-dimensional objective function space, and do not form a surface.

In this study a multi-objective optimization model is developed for water sensor network design in water distribution systems. In this model the three criteria used for evaluating the performance of the water sensor placement designed are directly used as the objectives of the optimization problem. These include minimizing the expected water volume contaminated, minimizing the expected time of detection and maximizing the detection likelihood. Due to the difficulty of determining sensor placement locations within thousands of junction combinations in the system, the sub-domain concept is introduced, which identifies a subset of junctions for candidate sensor locations. The sub-domains are determined using the roulette wheel method based on junction water demand values. The junctions with larger water demand have higher probabilities to be selected to the candidate sensor subset. For solution of the model an improved approach that is based on the non-dominated sorting genetic algorithm (NSGA-II) is used. The approach works over the sub-domain and the final Pareto optimal front is obtained through the sub-domain iteration process. The two water distribution systems provided in BWSN 2006 are chosen as examples to demonstrate the performance of the model and algorithm proposed. The impact of the non-detected scenarios in calculating objectives on the Pareto optimal front is also addressed in this study. The results show that the proposed model and the algorithm are effective in solving this problem.