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Purpose: The purpose of this study is to decrease the computation time that the large number of simulations involved in a parametric sweep when the model is in a three-dimensional environment. Design/methodology/approach: In this paper, a new methodology combining the PCE and a controlled, elitist genetic algorithm is proposed to design IPT systems. The relationship between the quantities of interest (mutual inductance and ferrite volume) and structural parameters (ferrite dimensions) is expressed by a PCE metamodel. Then, two objective functions corresponding to mutual inductance and ferrite volume are defined. These are combined together to obtain optimal parameters with a trade-off between these outputs. Findings: According to the number of individuals and the generations defined in the optimization algorithm in this paper, it needs to calculate 20,000 times in a 3D environment, which is quite time-consuming. But for PCE metamodel of mutual inductance M, it requires at least 100 times of calculations. Afterward, the evaluation of M based on the PCE metamodel requires 1 or 2 s. So compared to a conventional optimization based on the 3D model, it is easier to get optimized results with this approach and it saves a lot of computation time. Originality/value: The multiobjective optimization based on PCEs could be helpful to perform the optimization when considering the system in a realistic 3D environment involving many parameters with low computation time. [ABSTRACT FROM AUTHOR]

[Display omitted] • A framework is proposed for population-based optimization algorithms' convergence. • DIRECT's partition and population evolutions are repeated in this framework. • The global convergence of the framework is proved. • Framework is applied successfully on three population-based optimization algorithms. Population-based optimization algorithms, such as genetic algorithm and particle swarm optimization, have become a class of important algorithms for solving global optimization problems. However, there is an issue that the global convergence is often absent for most of them. This paper proposes a partition-based convergence framework for population-based optimization algorithms to solve this troubling problem. In this framework, regular partitions and evolutions of populations are implemented alternatively. Specifically, the initial population is generated from a regular partition on the search space; after several generations of evolution of the population, the evolution result is returned to join in the regular partition again, and a new population is generated. Repeat such progress until some stop condition is satisfied. Global convergence is guaranteed for the framework. Then this convergence framework is applied to particle swarm optimization, differential evolution, and genetic algorithm. The modified algorithms are globally convergent and perform better than the original version. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to investigate optimal control the non-linear fractional-order chaotic system of cancer cells by use of particle swarm optimization and genetic algorithms. The chaotic behavior of cancer cell growth and the tumor growth model were expressed as a system of differential equations. Then, optimal control of cancer cells using drugs was presented. Particle swarm optimization method and genetic algorithms were used to solve the problem of optimal control of cancer cell growth. The results of the control applied to the model can control the cancer cell growth. The application of control results in decreasing the number of cancer cells to zero. When the controller is applied from the beginning, the Results of genetic algorithm method are excellent. All the results obtained for the particle swarm optimization method show that this method has also been very successful and have results very close to the genetic algorithm method. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to apply grey wolf optimizer (GWO) algorithm for steady state analysis of self-excited induction generators (SEIGs) supplying isolated loads. Design/methodology/approach: Taking the equivalent circuit of SEIG, the impedances representing the stator, rotor and the connected load are reduced to a single loop impedance in terms of the unknown frequency, magnetizing reactance and core loss resistance for the given rotor speed. This loop impedance is taken as the objective function and minimized using GWO to solve for the unknown parameters. By including the value of the desired voltage as a constraint, the formulated objective function is also extended for estimating the required excitation capacitance. Findings: The experimental results obtained on a three phase 415 V, 3.5 kW SEIG and the corresponding predetermined performance characteristics agree closely, thereby validating the proposed GWO method. Moreover, a comparative study of GWO with genetic algorithm and particle swarm optimization techniques reveals that GWO exhibits much quicker convergence of the objective function. Originality/value: The important contributions of this paper are as follows: for the first time, GWO has been introduced for the SEIG performance predetermination and computation of the excitation capacitance for attaining the desired terminal voltage for the given load and speed; the predicted performance accuracy is improved by considering the variable core loss of the SEIG; and GWO does not require derivations of lengthy equations for calculating the SEIG performance. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to execute the efficiency analysis of the selected metaheuristic algorithms (MAs) based on the investigation of analytical functions and investigation optimization processes for permanent magnet motor. Design/methodology/approach: A comparative performance analysis was conducted for selected MAs. Optimization calculations were performed for as follows: genetic algorithm (GA), particle swarm optimization algorithm (PSO), bat algorithm, cuckoo search algorithm (CS) and only best individual algorithm (OBI). All of the optimization algorithms were developed as computer scripts. Next, all optimization procedures were applied to search the optimal of the line-start permanent magnet synchronous by the use of the multi-objective objective function. Findings: The research results show, that the best statistical efficiency (mean objective function and standard deviation [SD]) is obtained for PSO and CS algorithms. While the best results for several runs are obtained for PSO and GA. The type of the optimization algorithm should be selected taking into account the duration of the single optimization process. In the case of time-consuming processes, algorithms with low SD should be used. Originality/value: The new proposed simple nondeterministic algorithm can be also applied for simple optimization calculations. On the basis of the presented simulation results, it is possible to determine the quality of the compared MAs. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to present a novel approach to the determination of the maximum power point (MPP) in the photovoltaic system using genetic algorithm (GA). The optimization is realised on two types of photovoltaic (PV) modules: monocrystalline and polycrystalline solar modules, with the same rated peak power (400 Wp) but different electrical output data. Design/methodology/approach: The proposed algorithm is a nature-based algorithm that uses genetic operators such as reproduction, crossover and mutation to realise the search through the investigated area of solutions. To determine the MPP of the PV modules, a two-diode model of a PV cell is used. Based on the input electrical data for the analysed PV module, as well as the mathematical model of the PV, the algorithm can estimate the current and voltage at the MPP for given solar irradiation and cell temperature. The analysis is made for several different irradiations, but in work, the results are presented for irradiations of: 100, 500 and 1,000 W/m2 and cell temperatures of 0, 25 and 40 °C. Findings: From the presented results and performed analysis, it can be concluded that GA gives adequate results for both modules and for all working conditions. From the obtained results, it can be concluded that the optimization algorithm performs better when applied to the monocrystalline module works better especially in conditions with larger cell temperature, in comparison with the performance of the optimization algorithm applied to the polycrystalline module. On the other hand, the optimization algorithm applied to the polycrystalline module works better for the other working scenarios with smaller cell temperatures. Practical implications: From the performed analysis, it can be concluded that the use GA as an optimization tool for the determination of the MPP can be successfully implemented. In addition, to improve the overall performance of the PV system, it is also necessary to forecast the weather conditions of the location where the PV system would be installed to forecast the cell temperature and the solar irradiation. This is necessary to choose the right PV module and inverter for the given location. Originality/value: An optimization technique using GA as an optimization tool has been developed and successfully applied in the determination of the MPP for a PV system. The results are compared with the analytically determined values as well as with the values given by the producer, and they show good agreement. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to investigate different multi-objective optimization (MOO) approaches for design and control of electromagnetic devices. The main goal of MOO is to find the set of design variables or control parameters which will provide the best possible values of typical conflicting objective functions. Design/methodology/approach: In the research studies, standard genetic algorithm (GA), non-dominated sorting GA (NSGA-II), migration NSGA algorithm and alternance method of optimal control theory are discussed and compared. Findings: The test practical problems of multi-criteria optimization of induction heating processes with respect to chosen quality criteria confirm the effectiveness of application of considered MOO approaches both for the problems of design and control. Originality/value: This paper represents and investigates different MOO approaches for design and control of electrotechnological systems. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to calculate the back scattering response from single, finite and infinite arrays of nonlinear antennas like the case where the antennas are exposed to high-value signals such as lightning strokes. Design/methodology/approach: In this paper, the authors have used a recently introduced optimization technique called intelligent water drop. Findings: The results exhibit that the method used by the authors is faster and more accurate than other conventional optimization algorithms, i.e. particle swarm optimization and genetic algorithm. Originality/value: A new optimization algorithm is used to solve nonlinear problem accurately and sufficiently. Although the technique is not confined to the mentioned examples in the paper, it can be applied to other nonlinear circuits. [ABSTRACT FROM AUTHOR]

• A comprehensive dynamic model of PV power plant has been developed to study small signal stability. • The optimal probabilistically robust P PIλDμ controller is proposed for damping of oscillations. • The NSGA-II is employed to search for PIλDμ controller parameters in the presence of PV connected to single-machine system. • The uncertainty of probabilistically solar irradiation and temperature modeled by Monte Carlo simulation. With the rapidly increasing penetration level of power generated by large scale photovoltaic (PV) units into the power systems, the effect of the variable output power of the PV unit on the stability of the system cannot be ignored. This paper presents a mathematical approach to study the effect of high infiltration of PV power plant on the small signal stability of a power network and design of optimal fractional order PID (PIλDμ) controller for improving the probabilistic small signal stability of the power systems, taking into consideration the uncertainty of system operating conditions. Due to the probabilistic characteristics of large scale PV power generation, deterministic analysis approaches are not able to fully reveal the impact of high-level PV penetration. At first, this work introduces the main module and mathematical modeling of the large scale PV generation jointly with the single-machine infinite-bus power system. In the following, the paper proposes an efficient method that tunes power system stabilizer (PSS) to have the robustness for damping electro-mechanical oscillations in power systems with incorporated random PV power. For this reason, a robust PSS based on hybridization of PIλDμ controller and Non-dominated Sorting Genetic Algorithm (NSGAII) is designed. This paper targets at finding the optimal gain scheduling of the PIλDμ through the use of the advanced heuristic optimization technique with two objective functions in PV-grid connected systems. The performance of the proposed NSGAII-based PIλDμ controller (NSGAII- PIλDμ) under different solar irradiation, temperature conditions and disturbances is tested. Simulation results illustrate that the model presented can be used in designing of essential controllers for large scale PV power plant. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to apply two optimization methods to the issue of sensible energy store design. Design/methodology/approach: This paper is a comparison of topology optimization and genetic algorithms. Findings: Genetic algorithms are prone to converge to local maxima while requiring significantly longer convergence times compared to topology optimization. Topology optimization resulted in structures representing parallel sheets, which are as thin as the grid allows. These configurations can maintain the maximum surface area between the low and high conductivity materials at high refinement, resulting in the best performance. Practical implications: Time required for 99 per cent store discharge is decreased by 70 per cent using a 50 × 50 optimization grid at a loading of 10 Vol.%. Originality/value: These approaches have not been compared nor applied to this specific problem before. Value is in the key finding that maximization of surface area is only possible with fins/sheets and not tree structures. This dictates the optimal solution for dynamic behaviour. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this paper is to investigate the optimal geometry parameters in a dimple/protrusion-pin finned channel with high thermal performance.Design/methodology/approach The BSL turbulence model is used to calculate the flow structure and heat transfer in a dimple/protrusion-pin finned channel. The optimization algorithm is set as Non-dominated Sorting Genetic Algorithm II (NSGA-II). The high Nusselt number and low friction factor are chosen as the optimization objectives. The pin fin diameter, dimple/protrusion diameter, dimple/protrusion location and dimple/protrusion depth are applied as the optimization variables. An in-house code is used to generate the geometry model and mesh. The commercial software Isight is used to perform the optimization process.Findings The results show that the Nusselt number and friction factor are sensitive to the geometry parameters. In a pin finned channel with a dimple, the Nusselt number is high at the rear part of the dimple, while it is low at the upstream of the dimple. A high dissipative function is found near the pin fin. In the protrusion channel, the Nusselt number is high at the leading edge of the protrusion. In addition, the protrusion induces a high pressure drop compared to the dimpled channel.Originality/value The originality of this paper is to optimize the geometry parameters in a pin finned channel with dimple/protrusion. This is good application for the heat transfer enhancement at the trailing side for the gas turbine. [ABSTRACT FROM AUTHOR]

In this paper, we consider a realistic and meaningful scenario in the context of smart grids where an electricity retailer serves three different types of customers, i.e., customers with an optimal home energy management system embedded in their smart meters (C-HEMS), customers with only smart meters (C-SM), and customers without smart meters (C-NONE). The main objective of this paper is to support the retailer to make optimal day-ahead dynamic pricing decisions in such a mixed customer pool. To this end, we propose a two-level decision-making framework where the retailer acting as upper-level agent firstly announces its electricity prices of next 24 h and customers acting as lower-level agents subsequently schedule their energy usages accordingly. For the lower level problem, we model the price responsiveness of different customers according to their unique characteristics. For the upper level problem, we optimize the dynamic prices for the retailer to maximize its profit subject to realistic market constraints. The above two-level model is tackled by genetic algorithms (GA) based distributed optimization methods while its feasibility and effectiveness are confirmed via simulation results. [ABSTRACT FROM AUTHOR]

Purpose: An inductor for the uniform heating of the extremity of a ferromagnetic steel tube for stress relieving is considered. The main goal of the study is to investigate the possibility to achieve a reasonable design of the inductor when dealing with many design variables. Design/methodology/approach: Genetic optimization algorithms are used for this purpose, demonstrating the applicability of these techniques to the design of induction heating inductors. Genetic algorithms provide to the designer several optimal solutions belonging to Pareto Front, and this way they allow choosing the solution that better fits the technological requirements. In any case, the designer has to adapt the chosen solution to fit in with the real possibilities in industrial application. Findings: The study demonstrates that automatic optimization methods may help the designer of the induction heating system to solve complex problems with very conflicting technological requirements. Originality/value: In the paper, a problem with a high number of design variables is solved. Moreover, the goals of the optimization process are strongly conflicting, and the proposed problem is a challenging one. [ABSTRACT FROM AUTHOR]

This paper develops a multi-population evolutionary algorithm with single-objective guide to tackle many-objective optimization problems. It exploits the merits of both multiple populations and single-objective optimization to balance diversity and convergence of the evolution process. Specifically, the single-objective guide process helps to construct the better ideal point and reference points. A novel objective space partitioning mechanism is developed to transform a many-objective optimization problem into multiple subproblems, each of which is tackled by a subpopulation. A novel information sharing mechanism between subpopulations is proposed to balance diversity and convergence. Finally the subpopulations are merged together and deal with many-objective optimization problems to further enhance the convergence. We have compared the performance of the proposed algorithm with nine state-of-the-art algorithms on 85 test instances of 21 benchmark problems with up to 15 objectives. Experimental results show that the proposed algorithm has the superior performance in solving multi- and many-objective optimization problems. [ABSTRACT FROM AUTHOR]

With the development of tourism, the number of tourists is rapidly increasing. However, too many concentrated tourists at peak-tour have resulted in the distribution imbalance of tourists in the scenic area. Now the imbalance diminishes tourist satisfaction and destructs the ecological environment. Thus the contradictions between economic development and ecological environment protection of the scenic area are becoming increasingly acute. Tourists shunt and scheduling, which is a hot topic of tourism management, will help realize the load balance of the scenic area and ease the contradictions between economic development and environmental protection. In this paper, we build a simulation model of tourist shunt in real-time for scenic area, and search an optimal shunt scheme of various time-periods through genetic algorithm. Finally we take the scenic area of Jiuzhai Valley as an example and verify the effectiveness of the proposed scheme in this paper. [ABSTRACT FROM AUTHOR]

Abstract Environmental changes are traditionally considered intrinsic in evolutionary dynamic optimization. However, by ignoring that changes can instead be induced, we are ignoring that environmental changes can be eventually beneficial. To investigate the impact of artificial changes on the optimization speed up, we propose a framework for inducing artificial changes in any pseudo-Boolean or continuous optimization in this paper. Seven types of changes can be induced. Knowing when and how the changes occur allows us to design new strategies for evolutionary algorithms. Through computational experiments and illustrative examples, the impact of introducing changes in the optimization process is investigated. Experimental results indicate that changing the environments according to the proposed framework can lead to higher speed up, but not for all problems and change types. The best performance was obtained by change types that introduce plateaus and/or modify the gradient of regions of the fitness landscape around the current best solution. By doing this, the evolutionary dynamics is modified, eventually allowing the population to escape faster from local optima and reach new zones of the fitness landscape. Given a pseudo-Boolean or continuous optimization static problem, the proposed framework can be used to dynamically change the problem to speed up the optimization. [ABSTRACT FROM AUTHOR]

Highlights • The robustness against malicious attacks and cascading failures are considered simultaneously. • An effective normalized robustness measure has been proposed. • Optimization algorithm based on multi-agent genetic algorithm has been devised and validated. • The obtained results may provide candidates to solve realistic dilemmas as reported in references. Abstract Networked systems exist broadly in daily life and have been greatly emphasized in recent studies. Since these networks are always exposed in complicated environments and may become damaged from different kinds of attacks and failures, including intentional attacks and cascading failures, many researchers have studied the models and mitigation strategies towards these two issues separately. As has been indicated by some reports and materials, the design of comprehensively robust networks against both intentional attacks and cascading failures is of significance in daily life but existing optimization methods seem to be helpless when dealing with this problem. Therefore, this paper focuses on designing such networks. A normalized measure is first designed to evaluate the comprehensive robustness of networks. Then, a multi-agent genetic algorithm is devised to search for robust structures initialized by an input network, termed MAGA-Net R. Combining local and global optima information, the proposed algorithm is validated to be effective on synthetic and real-world networks and to outperform existing optimization methods. The optimal network designed by the proposed algorithm could facilitate theoretical analyses on networked systems and may provide potential candidates to solve some realistic dilemmas. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this paper is to elaborate algorithm and software for the optimization of the actuator–capacitor system taking the dynamics parameters into account. The system is applied for driving the valve of plasma gun. Two optimization strategies are proposed and pondered. The penalty function approach has been expanded in detail.Design/methodology/approach The field-circuit mathematical model of the dynamics operation consists of the strongly coupled equations of the transient electromagnetic fields and the equations of the electric circuit. The numerical implementation is based on finite element method and step-by-step Cranck–Nicholson procedure. The genetic algorithm has been used in the optimization procedure. The sigmoidal transformation has been applied to adjust the classical external penalty function method to the genetic algorithm.Findings The modification consists in adaptation of the penalty function to the genetic algorithm. In the proposed approach, operations involving successive iterations of increasing penalty function and operations containing genetic iterations are intertwined with each other. The differences between these two procedures are getting blurred. The proposed approach is very effective. It is possible to achieve optimal solution even more than ten times faster than using the classical method.Originality/value The proposed approach can be successfully applied to designing and optimization of different electromagnetic devices, including functional constraints. [ABSTRACT FROM AUTHOR]

A new method for Modular Neural Network optimization based on a Multi-objective Hierarchical Genetic Algorithm is proposed in this paper. The modular neural network using a granular approach and its optimization using a multi-objective hierarchical genetic algorithm provides better results than when the modular neural network is applied without a granular approach and optimization of parameters. The optimization of different parameters of the modular granular neural network architecture, such as the number of modules (sub-granules), size of the dataset for the training phase, goal error, learning algorithm, number of hidden layers and their respective number of neurons are performed in the proposed method. The fitness functions aim at minimizing the size of the dataset for the training phase and the error using a multi-objective approach. This method can be used in different areas of application, such as human recognition, classification problems or time series prediction. In this case the proposed method is tested with human recognition based on the face and ear biometric measures, where the proposed method aims at finding non-dominated solutions based on the number of data points for training and the recognition error. Benchmark face and ear databases are used to illustrate the advantages of the proposed approach. [ABSTRACT FROM AUTHOR]

Purpose - The purpose of this paper is to develop an effective, yet simple analytical framework for optimization of permanent-magnet synchronous machines. Also, single/multi-objective optimizations are performed for a case-study machine with surface-mounted permanent magnets. Design/methodology/approach - First, an accurate magnetic equivalent circuit is developed which takes all the material such as iron saturation and PM parameters into account. Then, through a Fourier analysis, it is combined with the d-q model of PM synchronous machines to achieve an optimization framework including the developed torque, back-EMF and a number of design considerations. Finally, a genetic algorithm (GA) is employed in the single/multi-objective design optimizations, which offers several design characteristics upon different desired outcomes. Findings - An analytical design framework for the optimization of permanent-magnet synchronous machines is developed in this paper that can effectively account for all material properties such as iron saturation and PM characteristics, and take into account the design considerations, all of which are shown as superiorities of the proposed approach over the existing method. In addition, the proposed framework is relatively simpler in terms of implementing. The model is verified by employing finite element method. Moreover, sensitivity analysis is carried out to investigate the influence of the design parameters on the machine performance, which provides valuable information for the designer of such devices. Finally, a GA is utilized to perform single/multi-objective optimization schemes whose objectives are minimizing the torque ripples, back-EMF total harmonic distortion and PM volume. Originality/value - The proposed framework is new approach that could be employed in the design optimization of PM synchronous machines. Contrary to existing method, it is simpler and more effective in taking the material properties such as iron saturation and PM characteristics into account. [ABSTRACT FROM AUTHOR]

Purpose -- Reducing eddy current losses in magnets of electrical machines can be obtained by means of several techniques. The magnet segmentation is the most popular one. It imposes the least restrictions on machine performances. This paper investigates the effectiveness of the magnet circumferential segmentation technique to reduce these undesirable losses. The full and partial magnet segmentation are both studied for a frequency range from few Hz to a dozen of kHz. To increase the efficiency of these techniques to reduce losses for any working frequency, an optimization strategy based on coupling of finite elements analysis and genetic algorithm is applied. The purpose of this paper is to define the parameters of the total and partial segmentation that can ensure the best reduction of eddy current losses. Design/methodology/approach -- First, a model to analyze eddy current losses is presented. Second, the effectiveness of full and partial magnet circumferential segmentation to reduce eddy loss is studied for a range of frequencies from few Hz to a dozen of kHz. To achieve these purposes a 2-D finite element model is developed under MATLAB environment. In a third step of the work, an optimization process is applied to adjust the segmentation design parameters for best reduction of eddy current losses in case of surface mounted permanent magnets synchronous machine. Findings -- In case of the skin effect operating, both full and partial magnet segmentations can lead to eddy current losses increases. Such deviations of magnet segmentation techniques can be avoided by an appropriate choice of their design parameters. Originality/value -- Few works are dedicated to investigate partial magnet segmentation for eddy current losses reduction. This paper studied the effectiveness and behaviour of partial segmentation for different frequency ranges. To avoid eventual anomalies related to the skin effect an optimization process based on the association of the finite elements analysis to genetic algorithm method is adopted. [ABSTRACT FROM AUTHOR]

Purpose -- The purpose of this paper is to introduce a new design optimization technique for a surface mounted permanent magnet (SMPM) machine to increase sensorless performance at high loadings by compromising with torque capability. Design/methodology/approach -- An SMPM parametric machine model was created and analysed by finite element analysis (FEA) software by means of the Matlab environment. Eight geometric parameters of the machine were optimized using genetic algorithms (GAs). The outer volume of the machine, namely copper loss per volume, was kept constant. In order to prevent sensorless performance loss at high loading, an optimization process was realized using two loading stages: maximum torque with minimum ripple at nominal load and maximum self-sensing capability at twice load. In order to show the effectiveness of the proposed technique, the obtained results were compared with the classical one-stage optimization realized for each loading condition separately. Findings -- With the proposed technique, fairly good performance results of the optimization were obtained when compared with the one-stage optimizations. Using the proposed technique, sensorless performance of the motor was highly increased by compromising torque capability for high loading. Additionally, this paper shows that the self-sensing properties of a SMPM machine should be considered at the design stage of the machine. Originality/value -- In related literature, design optimization studies for the sensorless capability of SMPM motor are very few. By increasing optimization performance, new proposed technique provides to achieve good result at high load for sensorless performance compromising torque capability. [ABSTRACT FROM AUTHOR]

Abstract: A review of the applications of interval type-2 fuzzy logic in intelligent control has been considered in this paper. The fundamental focus of the paper is based on the basic reasons for using type-2 fuzzy controllers for different areas of application. Recently, bio-inspired methods have emerged as powerful optimization algorithms for solving complex problems. In the case of designing type-2 fuzzy controllers for particular applications, the use of bio-inspired optimization methods have helped in the complex task of finding the appropriate parameter values and structure of the fuzzy systems. In this review, we consider the application of genetic algorithms, particle swarm optimization and ant colony optimization as three different paradigms that help in the design of optimal type-2 fuzzy controllers. We also mention alternative approaches to designing type-2 fuzzy controllers without optimization techniques. [Copyright &y& Elsevier]

This paper investigates the task assignment problem for a team of autonomous aerial/marine vehicles driven by constant thrust and maneuvering in a planar lateral drift field. The aim is to minimize the total traveling time in order to guide the vehicles to deliver a number of customized sensors to a set of target points with different sensor demands in the drift field. To solve the problem, we consider together navigation strategies and target assignment algorithms; the former minimizes the traveling time between two given locations in the drift field and the latter allocates a sequence of target locations to each vehicle. We first consider the effect of the weight of the carried sensors on the speed of each vehicle, and construct a sufficient condition to guarantee that the whole operation environment is reachable for the vehicles. Then from optimal control principles, time-optimal path planning is carried out to navigate each vehicle from an initial position to its given target location. Most importantly, to assign the targets to the vehicles, we combine the virtual coding strategy, multiple offspring method, intermarriage crossover strategy, and the tabu search mechanism to obtain a co-evolutionary multi-population genetic algorithm, short-named CMGA. Simulations on sensor delivery scenarios in both fixed and time-varying drift fields are shown to highlight the satisfying performances of the proposed approach against popular greedy algorithms. [ABSTRACT FROM AUTHOR]

Purpose – As arc suppression coils (ASCs), magnetically controlled reactors (MCRs) are usually operated in the single-phase mode. Due to the lack of a third order harmonic compensation circuit, the current harmonics are high. The purpose of this paper is to propose a novel structure of MCR and a genetic algorithm (GA) to determine the parameters which will result in minimum total harmonics. Design/methodology/approach – This paper proposes the structure and the working principle of the multi-valve controlled saturable reactor (MCSR). There are several sorts of magnetic valves in the iron cores of the MCSR. The saturation degree of each magnetic valve is different when the same direct component of the magnetic flux is generated in the iron core, therefore current harmonics of different phases emerging, i.e. the total harmonics can be reduced. The magnetization characteristics and the mathematical model of the current harmonics of the MCSR are presented by introducing three parameters. The optimal values of the parameters that result in the smallest total harmonic distortion in the output current are calculated by a GA. Findings – The simulation and experimental results are coincident with the theoretical analyses, which prove the effectiveness of the proposed method on harmonic suppression. Practical implications – The method proposed in this paper can successfully reduce the current harmonics of the conventional MCR, including but not limited to the ASC. A prototype MCSR (540 kVA/10 kV) has been designed and constructed. Originality/value – In this paper, a MCSR is proposed. The mathematical model of the MCSR for harmonic analysis is developed. The optimal parameters that result in the smallest THD in the output current are calculated. The mathematical model can be also used for the harmonic analysis of conventional MCRs. [ABSTRACT FROM AUTHOR]

Abstract: Designing User Interfaces (UIs) is considered a creative and human-intensive task, and this prevents from adopting computer-aided tools to explore alternative solutions. A number of decisions are to be taken, such as optimizing the displacement of UI items on the available screen space or selecting an adequate palette of colors. Therefore, the UI design process is iterative in nature, time consuming, and costly. Generative design, while keeping the centrality of human creativity, introduces a novel approach to assist humans in finding effective and esthetically convincing solutions. Meta-heuristics are a means to apply generative design in practice. We advocate the use of meta-heuristics to supporting different aspects of the UI design process. In this paper we discuss the application of genetic algorithms to solve two different problems: building hierarchical menu layouts and selecting color palettes based on a set of constraints. The paper discusses the results of two experimental applications developed in Poste Italiane, namely the money ordering by Automated Teller Machine (ATM) interfaces and the provision of financial information by kiosks. [Copyright &y& Elsevier]

Purpose – The purpose of the paper is to show that how a new magnetic circuit, which the paper could not imagined beforehand, may be obtained using a newly developed ON/OFF topology optimization method by applying it to the design of magnetic head and motor. Design/methodology/approach – ON/OFF optimization technique combined with finite element method. Findings – It is shown that 3-D topology optimization of SPT head, in which the recording head is increased and the leakage flux is decreased, is possible using the proposed method. The optimization of IPM motor with the minimum torque ripple and the maximum toque is also examined. Originality/value – 3-D ON/OFF optimization method applied to practical problems considering the nonlinearity and rotation of rotor, etc. [ABSTRACT FROM AUTHOR]

Purpose – A design procedure for multi-layer absorbers based on carbon nanotubes (CNT) frequency selective surfaces (FSS) sheets is here developed. The paper aims to discuss there issues. Design/methodology/approach – Single layer FSS are first analyzed via finite element (FE). Then equivalent sheets admittances are extracted in a transmission line model. Neural networks (NNs) interpolation over this data and subsequent multi-objective genetic algorithm (GA) based optimizations are then performed to design multiple layers absorbing structures. Designs are finally validated via full wave FEM simulations. Findings – In this paper, some absorbing structures made of three or four FSS sheets with total thicknesses around 6 mm are synthesized. Research limitations/implications – NNs' accuracy used in the equivalent model can be refined with further training. Practical implications – Low profile absorbing materials are of relevant industrial interest both for radar cloaking and anechoic chambers. Originality/value – The transmission line model combined with NNs and GA optimization is capable of speeding up the design procedure with respect to a conventional full-wave FEM approach. [ABSTRACT FROM AUTHOR]

Automated calibration is a crucial stage when validating non-linear dynamic systems. The modeler must control the calibration results and analyze parameter values in an iterative way. In many non-linear models, it is usual to find sets of configuration parameters that may obtain the same model fitting. In these cases, the modeler needs to understand the results’ implications and run a sensitivity analysis to check the model validity. This paper presents a framework based on niching genetic algorithms to provide modeler with a set of alternative calibration solutions which also ease the analysis of their parameters, model’s response, and sensitivity analysis. The framework is called MOMCA, an integral and interactive solution for model validation which facilitates the implication of decision makers. The core component of MOMCA is its niching genetic algorithm, able to reach various optima in multimodal optimization problems by keeping the necessary diversity. The proposed framework is applied to two different case studies. The first case study is a biological growth model and the second one is a managerial model to improve brand equity. Both applications show the benefits of the framework when providing a set of calibrated models and a way to analyze and perform sensitivity analysis based on the set of solutions. [ABSTRACT FROM AUTHOR]

Purpose – For many optimization problems such as optimal techniques, compositions, producing process, the optimizing objectives in systems are complex relations with respect to a great deal of parameters. Generally, the objective function is hardly obtained, even the searching objective is unquantifiable. So it is difficult to model and optimize the complex systems to some extent. Design/methodology/approach – To the above purpose, a novel approach is presented in this paper. It firstly utilizes the excellent fitting performance of neural network (NN) combined with expert knowledge (EK) to obtain the objective function, and secondly searches the optimal influential parameters with genetic algorithm (GA). Findings – Peaks function inside Matlab and the acural application of comprehensive performance optimization in analog PID control system are studied, respectively. The results of simulation and the actual experiment both show that the modeling and optimizing method presented in the paper are effective. Research limitations/implications – Expert knowledge is needed for the fuzzy/unquantifiable objective. Practical implications – The paper presents a useful way to model and optimize complex systems. Originality/value – The combined approach based on NN, EK and GA is firstly presented and is effectively used in modeling and optimizing complex systems. [ABSTRACT FROM AUTHOR]

The purpose of this work is to numerically find the optimum location of constant potential anodes to ensure complete structure surface protection using a cathodic protection technique. The existence of sacrificial anodes is originally introduced through the boundary conditions of the corresponding boundary value problem (BVP). However, if constant potential galvanic regions are introduced through its boundaries, then finding their optimal location is not an easy task due to the necessity of redefining boundary geometric nodes and the arrangement of virtual sources for the standard method of fundamental solutions (MFS) formulation. Therefore, in this work, the galvanic anodes are introduced as source terms using a Gaussian function. Hence, the boundary remains the same for different anode positions. The optimization process includes the identification of the following parameters characterizing the Gaussian function: the optimum coordinates of the centre of the anode, a factor that involves the inherent potential of the electrode and a proportionality factor for the electrode diameter. The MFS methodology coupled with a genetic algorithm presented good results for this multiobjective optimization procedure. This fact can be seen in the several results of applications that are discussed in this paper, considering numerical simulations in finite regions in R 2 . [ABSTRACT FROM AUTHOR]

The power to solve intractable optimisation problems is often found through population based evolutionary methods. These include, but are not limited to, genetic algorithms, particle swarm optimisation, differential evolution and ant colony optimisation. While showing much promise as an effective optimiser, extremal optimisation uses only a single solution in its canonical form – and there are no standard population mechanics. In this paper, two population models for extremal optimisation are proposed and applied to a multi-objective version of the generalised assignment problem. These models use novel intervention/interaction strategies as well as collective memory in order to allow individual population members to work together. Additionally, a general non-dominated local search algorithm is developed and tested. Overall, the results show that improved attainment surfaces can be produced using population based interactions over not using them. The new EO approach is also shown to be highly competitive with an implementation of NSGA-II. [ABSTRACT FROM AUTHOR]

Purpose |!|#8211; The purpose of this work is to study the capability of heuristic algorithms like genetic algorithm to estimate the electron transport parameters of the Gallium Arsenide (GaAs). Also, the paper provides a simple but complete electron mobility model for the GaAs based on the genetic algorithm that can be suitable for use in simulation, optimization and design of GaAs-based electronic and optoelectronic devices. Design/methodology/approach |!|#8211; The genetic algorithm as a powerful heuristic optimization technique is used to approximate the electron transport parameters during the model development. Findings |!|#8211; The capability of the model to approximate the electron transport properties of Gallium Arsenide is tested using experimental and Monte Carlo data. Results show that the genetic algorithm based model can provide a reliable estimate of the electron mobility in Gallium Arsenide for a wide range of temperatures, concentrations and electric fields. Based on the obtained results, this paper shows that the genetic algorithm can be a useful tool for the estimation of the transport parameters of semiconductors. Originality/value |!|#8211; For the first time, the genetic algorithm is used to calculate the electron transport parameters in Gallium Arsenide. A complete electron mobility model for a wide range of temperatures, doping concentrations, compensation ratios and electric fields is developed. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to describe a new satellite mission scheduling (SMS) problem based on an genetic algorithm (GA). Design/methodology/approach – The SMS involves scheduling tasks to be performed by a satellite, where new task requests can arrive non-deterministically (i.e. at any time) and must be scheduled in real-time. This paper investigates algorithmic approaches for determining an optimal or near-optimal sequence of tasks, allocated to a satellite payload over time, with dynamic tasking considerations. Findings – Simulation results show that the proposed approach is effective and efficient when utilized in actual cases. Originality/value – This paper shows how to adopt the GA search approach to generate an SMS within allowable computation time. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to improve forecasting accuracy for short-term load series. Design/methodology/approach – A forecasting method based on chaotic time series and optimal diagonal recurrent neural networks (DRNN) is presented. The input of the DRNN is determined by the embedding dimension of the reconstructed phase space, and adaptive dynamic back propagation (DBP) algorithm is used to train the network. The connection weights of the DRNN are optimized via modified genetic algorithms, and the best results of optimization are regarded as initial weights for the network. The new method is applied to predict the actual short-term load according to its chaotic characteristics, and the forecasting results also validate the feasibility. Findings – For the chaos time series, the hybrid neural genetic method based on phase space reconstruction can carry out the short-term prediction with the higher accuracy. Research limitations/implications – The proposed method is not suited to medium and long-term load forecasting. Practical implications – The accuracy of the load forecasting is important to the economic and secure operation of power systems; also, the neural genetic method can improve forecasting accuracy. Originality/value – This paper will help overcome the defects of traditional neural network and make short-term load forecasting more accurate and fast. [ABSTRACT FROM AUTHOR]

Purpose - This paper aims to develop and describe an improved process for determining the rate of heat generation in living tissue. Design/methodology/approach - Previous work by the authors on solving the bioheat equation has been updated to include a new localized meshless method which will create a more robust and computationally efficient technique. Inclusion of this technique will allow for the solution of more complex and realistic geometries, which are typical of living tissue. Additionally, the unknown heat generation rates are found through genetic algorithm optimization. Findings - The localized technique showed superior accuracy and significant savings in memory and processor time. The computational efficiency of the newly proposed meshless solver allows the optimization process to be carried to a higher level, leading to more accurate solutions for the inverse technique. Several example cases are presented to demonstrate these conclusions. Research limitations/implications - This work includes only 2D development of the approach, while any realistic modeling for patient-specific cases would be inherently 3D. The extension to 3D, as well as studies to improve the technique by decreasing the sensitivity to measurement noise and to incorporate non-invasive measurement positioning, are under way. Practical implications - As medical imaging continuously improves, such techniques may prove useful in patient diagonosis, as heat generation can be correlated to the presence of tumors, infections, or other conditions. Originality/value - This paper describes a new application of meshless methods. Such methods are becoming attractive due to their decreased pre-processing requirements, especially for problems involving complex geometries (such as patient specific tissues), as well as optimization problems, where geometries may be constantly changing. [ABSTRACT FROM AUTHOR]

Purpose - This paper seeks to identify and propose a standard approach for the selection and optimization of fuzzy sets used in fuzzy decision-making systems. Design/methodology/approach - The design was based on two principles: selection and optimization. The selection methodology was based on the "Fuzzimetric Arcs" principle, which is an analogy of the trigonometric circle principle. This would allow an initial sinusoidal fuzzy set shape. Other shapes may also be selected using the described formula (trapezoidal, triangular,?…?, etc.). As the proposal methodology is based on the trigonometric circle, other trigonometric formulae can be applied. For example, linguistic hedges can be defined using standard trigonometric formulae. Regarding optimization, the initial fuzzy set selection was assumed to be of regular shape (sinusoidal, trapezoidal or triangular). An irregular shape may be required by some systems. Hence, a genetic algorithm was proposed as a methodology to optimize the performance of fuzzy systems by mutating different regular shapes. Findings - A simplified business decision-making application was described and the proposed selection methodology was explained in the form of an example. Currently, there is no standard for the selection of fuzzy sets as this is dependent on knowledge engineering and the type of application chosen. The proposed methodology offers an easy-to-use possible standard which all developers/researchers may adopt irrespective of their application field. Moreover, the proposed methodology may integrate well with object-oriented technology. Originality/value - The paper presents standardization of the fuzzy sets selection and optimization technique used in any type of management information systems. This will aid all developers and researchers to enhance their technical communication. It would also enhance the simplicity and effectiveness of optimizing the performance of such systems. [ABSTRACT FROM AUTHOR]

Abstract: We address a capacitated vehicle routing problem (CVRP) in which the demand of a node can be split on several vehicles celled split services by assuming heterogeneous fixed fleet. The objective is to minimize the fleet cost and total distance traveled. The fleet cost is dependent on the number of vehicles used and the total unused capacity. In most practical cases, especially in urban transportation, several vehicles transiting on a demand point occurs. Thus, the split services can aid to minimize the number of used vehicles by maximizing the capacity utilization. This paper presents a mix-integer linear model of a CVRP with split services and heterogeneous fleet. This model is then solved by using a simulated annealing (SA) method. Our analysis suggests that the proposed model enables users to establish routes to serve all given customers using the minimum number of vehicles and maximum capacity. Our proposed method can also find very good solutions in a reasonable amount of time. To illustrate these solutions further, a number of test problems in small and large sizes are solved and computational results are reported in the paper. [Copyright &y& Elsevier]

Purpose - The paper aims to present a new methodology for hybrid genetic algorithms (GA) in the solution of electromagnetic optimization problems. Design/methodology/approach - This methodology can be seen as a local search operator which uses local quadratic approximations for each objective and constraint function in the problem. In the local search phase, these approximations define an associated local search problem that is efficiently solved using a formulation based on linear matrix inequalities. Findings - The paper illustrates the proposed methodology comparing the performance of the hybrid GA against the basic GA in two analytical problems and in the well-known TEAM benchmark Problem 22. For the analytical problems, 30 independent runs for each algorithm were considered whereas for Problem 22, ten independent runs for each algorithm were taken. Research limitations/implications - For the analytical problems, the hybrid GA enhanced both the convergence speed, in terms of the number of function evaluations, and the accuracy of the final result. For Problem 22, the hybrid GA was able to reach a better solution, with a better value of the standard deviation with less CPU time. Practical implications - The paper could be useful both for device designers and researchers involved optimization in computational electromagnetics. Originality/value - The hybrid GA proposed enhanced the convergence speed, in terms of the number of function evaluations, representing a faster and robust algorithm for practical optimization problems. [ABSTRACT FROM AUTHOR]

Abstract: Roadside appurtenances, such as guardrails, signs, and luminaries form a major portion of highway features whose upkeep and timely maintenance is very critical to maximize highway life-cycle. Their life-expectancy is largely reduced by poor maintenance. While significant work has been done in pavement and bridge maintenance roadside appurtenances have received limited attention. In this paper we develop a Markovian model for maximizing highway life-cycle by considering optimal maintenance strategies for roadside appurtenances over a given planning horizon. A genetic algorithm is developed to solve the developed optimization model since the resulting Markov tree tends to be clustered and relatively complex. Due to this complexity traditional approaches, such as dynamic programming seem to be computationally inefficient. Separate formulations under deterministic and probabilistic approaches to be undertaken for different maintenance conditions are developed. A probabilistic deterioration function for roadside appurtenances is also developed. An illustrative example to investigate the effectiveness of the deterministic approach is presented. The paper concludes with directions for future research. [Copyright &y& Elsevier]

The parameter-less genetic algorithm was introduced a couple of years ago as a way to simplify genetic algorithm operation by incorporating knowledge of parameter selection and population sizing theory in the genetic algorithm itself This paper shows how that technique can be used in practice by applying it to a network expansion problem. The existence of the parameter-less genetic algorithm stresses the fact that some problems need more processing power than others. Such observation leads to the development of a problem difficulty measure which is also introduced in this paper. The measure can be useful for comparing the difficulty of real-world problems. [ABSTRACT FROM AUTHOR]

For evolutionary algorithms, the search data during evolution has attracted considerable attention and many kinds of data mining methods have been proposed to derive useful information behind these data so as to guide the evolution search. However, these methods mainly centered on the single objective optimization problems. In this paper, an adaptive differential evolution algorithm based on analysis of search data is developed for the multi-objective optimization problems. In this algorithm, the useful information is firstly derived from the search data during the evolution process by clustering and statistical methods, and then the derived information is used to guide the generation of new population and the local search. In addition, the proposed differential evolution algorithm adopts multiple subpopulations, each of which evolves according to the assigned crossover operator borrowed from genetic algorithms to generate perturbed vectors. During the evolution process, the size of each subpopulation is adaptively adjusted based on the information derived from its search results. The local search consists of two phases that focus on exploration and exploitation, respectively. Computational results on benchmark multi-objective problems show that the improvements of the strategies are positive and that the proposed differential evolution algorithm is competitive or superior to some previous multi-objective evolutionary algorithms in the literature. [ABSTRACT FROM AUTHOR]

Regression testing is the process of retesting a system after it or its environment has changed. Many techniques aim to find the cheapest subset of the regression test suite that achieves full coverage. More recently, it has been observed that the tester might want to have a range of solutions providing different trade-offs between cost and one or more forms of coverage, this being a multi-objective optimisation problem. This paper further develops the multi-objective agenda by adapting a decomposition-based multi-objective evolutionary algorithm (MOEA/D). Experiments evaluated four approaches: a classic greedy algorithm; non-dominated sorting genetic algorithm II (NSGA-II); MOEA/D with a fixed value for a parameter c ; and MOEA/D in which tuning was used to choose the value of c . These used six programs from the SIR repository and one larger program, VoidAuth. In all of the experiments MOEA/D with tuning was the most effective technique. The relative performance of the other techniques varied, although MOEA/D with fixed c outperformed NSGA-II on the larger programs (Space and VoidAuth). [ABSTRACT FROM AUTHOR]

A generic coupling method between a multi-physics workflow engine and an optimization framework is presented in this paper. The coupling architecture has been developed in order to preserve the integrity of the two frameworks. The objective is to provide the possibility to replace a framework, a workflow or an optimizer by another one without changing the whole coupling procedure or modifying the main content in each framework. The coupling is achieved by using a socket-based communication library for exchanging data between the two frameworks. Among a number of algorithms provided by optimization frameworks, Genetic Algorithms (GAs) have demonstrated their efficiency on single and multiple criteria optimization. Additionally to their robustness, GAs can handle non-valid data which may appear during the optimization. Consequently GAs work on most general cases. A parallelized framework has been developed to reduce the time spent for optimizations and evaluation of large samples. A test has shown a good scaling efficiency of this parallelized framework. This coupling method has been applied to the case of SYCOMORE (SYstem COde for MOdeling tokamak REactor) which is a system code developed in form of a modular workflow for designing magnetic fusion reactors. The coupling of SYCOMORE with the optimization platform URANIE enables design optimization along various figures of merit and constraints. [ABSTRACT FROM AUTHOR]

In industries which employ large numbers of mobile field engineers (resources), there is a need to optimize the task allocation process. This particularly applies to utility companies such as electricity, gas and water suppliers as well as telecommunications. The process of allocating tasks to engineers involves finding the optimum area for each engineer to operate within where the locations available to the engineers depends on the work area she/he is assigned to. This particular process is termed as work area optimization and it is a sub-domain of workforce optimization. The optimization of resource scheduling, specifically the work area in this instance, in large businesses can have a noticeable impact on business costs, revenues and customer satisfaction. In previous attempts to tackle workforce optimization in real world scenarios, single objective optimization algorithms employing crisp logic were employed. The problem is that there are usually many objectives that need to be satisfied and hence multi-objective based optimization methods will be more suitable. Type-2 fuzzy logic systems could also be employed as they are able to handle the high level of uncertainties associated with the dynamic and changing real world workforce optimization and scheduling problems. This paper presents a novel multi-objective genetic type-2 fuzzy logic based system for mobile field workforce area optimization, which was employed in real world scheduling problems. This system had to overcome challenges, like how working areas were constructed, how teams were generated for each new area and how to realistically evaluate the newly suggested working areas. These problems were overcome by a novel neighborhood based clustering algorithm, sorting team members by skill, location and effect, and by creating an evaluation simulation that could accurately assess working areas by simulating one day's worth of work, for each engineer in the working area, while taking into account uncertainties. The results show strong improvements when the proposed system was applied to the work area optimization problem, compared to the heuristic or type-1 single objective optimization of the work area. Such optimization improvements of the working areas will result in better utilization of the mobile field workforce in utilities and telecommunications companies. [ABSTRACT FROM AUTHOR]

This paper presents a proposed new approach for complex control combining several simpler individual fuzzy controllers. This method is particularly useful when the case of study is a multivariable control system. The proposed method has a hierarchical architecture with 2 levels (individual fuzzy systems and a superior control to adjust the global result). The behavior of the proposed method is illustrated with a problem of flight control that requires several individual controllers. In addition a statistical comparison is performed using the t student test, where the proposed control strategy is compared against a simple fuzzy control approach. Finally, an optimization method is also applied to achieve an optimal design of the fuzzy systems. [ABSTRACT FROM AUTHOR]

This paper investigates the multi-objective optimal design of distributed order fractional damper. A new hybrid method which takes advantages of both the evolutionary algorithms and the simple cell mapping method is used to obtain the Pareto solutions of the multi-objective optimal design. Three different cases of distributed damping coefficient c ( α ) are studied. The objective functions are taken from the time domain specifications of step response in the feedback control design, which include the overshoot, peak time and integrated tracking error. It is found that the multi-objective optimal designs of the damper can deliver much improved step responses in terms of the three objective functions, compared with the single-objective optimal design by minimizing the integrated tracking error alone. Numerical simulations of the three cases of c ( α ) demonstrate that better overall performance can be achieved with more design parameters in the multi-objective optimization setting. [ABSTRACT FROM AUTHOR]

In this paper, we develop a parallel-structured real-coded genetic algorithm (RCGA), named the RGA-RDD, for numerical optimization. Technically, the proposed RGA-RDD integrates three specially designed evolutionary operators – the Ranking Selection (RS), Direction-Based Crossover (DBX), and the Dynamic Random Mutation (DRM) – as a whole to mimic a specific evolutionary process. Unlike the conventional RCGAs that perform evolutionary operators in a series framework, the RGA-RDD embeds a coordinator in the inner parallel loop to organize the operations of the DBX and DRM so that a higher possibility of locating the global optimum is ensured. Besides, based on the results of a systematic parametric analysis, we provide a parameter selection guideline for the settings of the proposed RGA-RDD. Furthermore, a data-driven optimization scheme, which incorporates the uniform design for design of experiments and a shape-tunable neural network for auxiliary decision support, is applied to search for an optimal set of the algorithm parameters. The effectiveness and applicability of the proposed RGA-RDD are demonstrated through a variety of benchmarked optimization problems, followed by comprehensive comparisons with some existing state-of-the-art evolutionary algorithms. Extensive simulation results reveal that the performance of the proposed RGA-RDD is superior to comparative methods in locating the global optimum for real-parameter optimization problems, especially for unsolved multimodal and high-dimensional hybrid functions. [ABSTRACT FROM AUTHOR]

Purpose - The purpose of this paper is to elaborate an algorithm and the software for the rotor structure optimization of the permanent magnet synchronous motor (PMSM) with a magnet composed of two materials made with the use of different technologies: sintered Neodymium magnets and powder dielectromagnets. To execute of optimization of selected motor structure using the non-deterministic procedure. Design/methodology/approach - The mathematical model of the devices includes: the equation of the electromagnetic field, the electric circuit equations and equation of mechanical motion. The numerical implementation is based on finite element method and step-by-step algorithm. The genetic algorithm has been applied in the optimization procedures. The computer code has been developed. Findings - The elaborated computer software has been applied for the optimization and design of PMSMs. The elaborated algorithm has been tested and a good convergence has been attained. The parameters of two optimal structures of PMSM motors have been compared. Originality/value - The presented approach and computer software can be successfully applied to the design and optimization of different structure of PMSM with different type of rotors. [ABSTRACT FROM AUTHOR]

Bi-objective optimization of a multi-product multi-period three-echelon supply-chain-network problem is aimed in this paper. The network consists of manufacturing plants, distribution centers (DCs), and customer nodes. To bring the problem closer to reality, the majority of the parameters in this network including fixed and variable costs, customer demand, available production time, set-up and production times, all are considered stochastic. The goal is to determine the quantities of the products produced by the manufacturing plants in different periods, the number and locations of the warehouses, the quantities of products transported between the supply chain entities, the inventory of products in warehouses and plants, and the shortage of products in periods such that both the expected and the variance of the total cost are minimized. The problem is first formulated into the framework of a single-objective stochastic mixed integer linear programming model. Then, it is reformulated into a bi-objective deterministic mixed-integer nonlinear programming model. To solve the complicated problem, a non-dominated sorting genetic algorithm (NSGA-II) is utilized next. As there is no benchmark available in the literature, another GA-based algorithm called non-dominated ranking genetic algorithm (NRGA) is used to validate the results obtained. In both algorithms, a modified priority-based encoding is proposed. Some numerical illustrations are provided at the end to not only show the applicability of the proposed methodology, but also to select the best method using a t -test along with the simple additive weighting (SAW) method. [ABSTRACT FROM AUTHOR]