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Optimized Design of Permanent Magnet Assisted Synchronous Reluctance Motor Using Oriented Auto-tuning niching algorithm

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2021年第3期16卷 1495-1503页

作者： Lee, Tae-Hee Kang, Young-Rok Son, Ji-Chang Lim, Dong-Kuk Univ Ulsan Sch Elect & Elect Engn Daehak Ro 93 Ulsan 44610 South Korea

In this paper, an oriented auto-tuning niching algorithm (OANA) is proposed for the design optimization of a permanent magnet assisted synchronous reluctance motor (PMa-SynRM) for a pedal-assist system electric bicycle (PAS-EB). The OANA is a fast and accurate optimization algorithm for finding the optimal points in multi-modal functions. Specifically, the OANA remedies the shortcomings of the conventional auto-tuning niching genetic algorithm by adopting a probability method that is based on the navigational pathways. The OANA showed excellent performance by utilizing mathematical multi-modal test functions. Then, the OANA was applied to cogging torque optimization for a PMa-SynRM design of the PAS-EB and found several optimal points. Finally, the optimum design is derived by comparing the average torque and torque ripple of the obtained design points.

关键词： Cogging torque niching algorithm Pedal assist system electric bicycle Permanent magnet assisted synchronous reluctance motor

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niching comprehensive learning gravitational search algorithm for multimodal optimization problems

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EVOLUTIONARY INTELLIGENCE 2022年第1期15卷 695-721页

作者： Bala, Indu Yadav, Anupam Northcap Univ Dept Sci & Humanities Gurugram 122017 Haryana India Dr BR Ambedkar Natl Inst Technol Jalandhar Dept Math Jalandhar 144011 Punjab India

Many niching algorithms have been proposed in recent times to cater complex real-world problems having several global/local optima. These algorithms help in locating as many global/local optima as possible. However, to explore the search space along with locating and maintaining a large number of global solutions is always a challenging problem. Hence, we propose a new niching strategy named, 'niching Comprehensive Learning Gravitational Search algorithm', in which the CLGSA algorithm is used to solve complex problems having multiple solutions. The proposed algorithm first efficiently tries to explore the search space without trapping in local optima's and secondly it locates all possible global optima's. This algorithm has an external memory that enhances its capability to find the maximum number of optima's. The proposed approach is tested on the IEEE CEC-2013 Multimodal Optimization benchmark problems (Li et al. in Evolutionary computation and machine learning group, RMIT University, Melbourne, Australia, Technical Report, 2013). The results of the proposed scheme are compared with five other state-of-the-art methods. In order to show the efficiency of CLGSA algorithm over real-life multimodal optimization problems, it applied to non-linear, and a mixed variable problem this is, the Reactive Power Dispatch problem (RPD). A standard benchmark IEEE-57 bus system is considered to evaluate the efficiency of the CLGSA. The evaluated simulation results show that CLGSA algorithm successfully solve multimodal problems and the RPD problem with significant accuracy.

关键词： niching algorithm CLGSA Evolutionary algorithms Power system Reactive power dispatch

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Modified Football Game algorithm for Multimodal Optimization of Test Task Scheduling Problems Using Normalized Factor Random Key Encoding Scheme 1

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10th International Conference on Bioinspired Optimization Methods and Their Applications (BIOMA)

作者： Fadakar, Elyas Beihang Univ BUAA Beijing Peoples R China

ISBN: (数字)9783031210945

ISBN: (纸本)9783031210938;9783031210945

Test Task Scheduling Problems (TTSPs) are a type of scheduling problems that are very important in many big and complicated test systems in automotive industries where the reliability of the final product is fundamentally dependent on those tests while the time, workload, and agility of the production is dependent on the optimal scheduling. Scheduling problems are highly multimodal problems with high number of local and global optimum solutions. Availability of different promising solutions provide more freedom for engineering and management decisions based on other criteria which cannot be modeled easily in a design software. In this study we reviewed important researches in the field of multimodal optimization in general and specifically for scheduling problems and then proposed a major modification on Football Game algorithm (FGA). Then we applied modified FGA for solving some practical TTSPs using a new proposed encoding scheme called Normalized Factor Random Key (NF-RK). The experimental study shows that the modified FGA in combination with NF-RK is promising in solving continuous multimodal TTSPs and shows a notable out performance in comparison to the best-known reported results in the literature.

关键词： Test task scheduling problem Multimodal optimization niching algorithm Encoding scheme

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Double-layer-clustering differential evolution multimodal optimization by speciation and self-adaptive strategies

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INFORMATION SCIENCES 2021年 545卷 465-486页

作者： Liu, Qingxue Du, Shengzhi van Wyk, Barend Jacobus Sun, Yanxia Kunming Univ Sch Mech & Elect Engn Kunming 650214 Yunnan Peoples R China Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao 266590 Peoples R China Tshwane Univ Technol Dept Elect Engn ZA-0001 Pretoria South Africa Nelson Mandela Univ Fac Engn Built Environm & Technol ZA-6031 Port Elizabeth South Africa Univ Johannesburg Dept Elect & Elect Engn Sci ZA-2006 Johannesburg South Africa

Multimodal optimization aims to find and maintain as many global and local optima of a function as possible. niching techniques based on multi-populations and clustering proved to be efficient for tackling multimodal optimization problems. The main focus of this work is to enhance the diversity of the population and improve the global search ability to locate more optima. A Double-Layer-Clustering Speciation Differential Evolution (DLCSDE) algorithm for multimodal optimization is proposed. We also show how the DLCSDE can be improved by integrating with a self-adaptive strategy to form the Self-adaptive DLCSDE (SDLCSDE). Based on speciation, first layer clustering divides the entire population into multiple subpopulations to locate global and local optima. The seeds from each species then form a sub-population to search globally during the second layer clustering to find peaks missed during the first layer clustering search process. To test the performance, both DLCSDE and SDLCSDE are compared with 17 state-of-art niching algorithms on 29 multi modal problems with different dimensions. The experiment results demonstrate that both the proposed algorithms outperform or perform comparably to the 17 niching algorithms on all the test functions. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Differential evolution (DE) Multimodal optimization niching algorithm Speciation Self-adaptive strategy

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niching particle swarm optimization based on Euclidean distance and hierarchical clustering for multimodal optimization

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NONLINEAR DYNAMICS 2020年第3期99卷 2459-2477页

作者： Liu, Qingxue Du, Shengzhi van Wyk, Barend Jacobus Sun, Yanxia Kunming Univ Sch Mech & Elect Engn Kunming 650214 Yunnan Peoples R China Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao 266590 Peoples R China Tshwane Univ Technol Dept Elect Engn ZA-0001 Pretoria South Africa Univ Johannesburg Dept Elect & Elect Engn Sci ZA-2006 Johannesburg South Africa

Multimodal optimization is still one of the most challenging tasks in the evolutionary computation field, when multiple global and local optima need to be effectively and efficiently located. In this paper, a niching particle swarm optimization (PSO)-based Euclidean distance and hierarchical clustering (EDHC) for multimodal optimization is proposed. This technique first uses the Euclidean distance-based PSO algorithm to perform preliminarily search. In this phase, the particles are rapidly clustered around peaks. Secondly, hierarchical clustering is applied to identify and concentrate the particles distributed around each peak to finely search as a whole. Finally, a small-world network topology is adopted in each niche to improve the exploitation ability of the algorithm. At the end of this paper, the proposed EDHC-PSO algorithm is applied to the traveling salesman problems (TSP) after being discretized. The experiments demonstrate that the proposed method outperforms existing niching techniques on benchmark problems and is effective for TSP.

关键词： Particle swarm optimization Multimodal optimization niching algorithm Hierarchical clustering Small-world network topology Traveling salesman problem (TSP)

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Adaptive Clustering-Based Differential Evolution for Multimodal Optimization 8

Adaptive Clustering-Based Differential Evolution for Multimo...

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8th International Conference on Information Science and Technology (ICIST)

作者： Duan, Danting Gong, Yuejiao Huang, Ting Zhang, Jun South China Univ Technol Sch Informat Engn Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9781538637821

Multimodal optimization problems which widely exist in the scientific research and engineering applications, has aroused a wide interest of researchers. For solving multimodal optimization problems, numerous niching algorithms have been proposed to locate and track multiple optima. However, most of these algorithms need a very strict choice of the population size parameter. This paper presents a new niching differential evolution algorithm which adaptively adjusts population size during the evolution. Particularly, we propose three techniques for performance enhancement: a heuristic clustering method, a population adaptation strategy, and an auxiliary movement strategy for the best individuals. The algorithm divides the population into several subpopulations and adaptively adjust the number of individuals and subpopulations according to the evolutionary state. In this way, the diversity of population is increased, while the computational cost is reduced. Experimental results verify that the proposed algorithm outperforms the other niching algorithms for multimodal optimization.

关键词： multimodal optimization niching algorithm population adaptation differential evolution

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Inducing niching Behavior in Differential Evolution Through Local Information Sharing

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2015年第2期19卷 246-263页

作者： Biswas, Subhodip Kundu, Souvik Das, Swagatam Jadavpur Univ Dept Elect & Telecommun Engn Kolkata 32 India Indian Stat Inst Elect & Commun Sci Unit Kolkata 108 India

In practical situations, it is very often desirable to detect multiple optimally sustainable solutions of an optimization problem. The population-based evolutionary multimodal optimization algorithms can be very helpful in such cases. They detect and maintain multiple optimal solutions during the run by incorporating specialized niching operations to aid the parallel localized convergence of population members around different basins of attraction. This paper presents an improved information-sharing mechanism among the individuals of an evolutionary algorithm for inducing efficient niching behavior. The mechanism can be integrated with stochastic real-parameter optimizers relying on differential perturbation of the individuals (candidate solutions) based on the population distribution. Various real-coded genetic algorithms (GAs), particle swarm optimization (PSO), and differential evolution (DE) fit the example of such algorithms. The main problem arising from differential perturbation is the unequal attraction toward the different basins of attraction that is detrimental to the objective of parallel convergence to multiple basins of attraction. We present our study through DE algorithm owing to its highly random nature of mutation and show how population diversity is preserved by modifying the basic perturbation (mutation) scheme through the use of random individuals selected probabilistically. By integrating the proposed technique with DE framework, we present three improved versions of well-known DE-based niching methods. Through an extensive experimental analysis, a statistically significant improvement in the overall performance has been observed upon integrating of our technique with the DE-based niching methods.

关键词： Differential perturbation diversity local information sharing multimodal optimization niching algorithm

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Solving CEC 2015 Multi-modal Competition Problems Using Neighborhood Based Speciation Differential Evolution

Solving CEC 2015 Multi-modal Competition Problems Using Neig...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Qu, B. Y. Liang, J. J. Wang, Z. Y. Liu, D. M. Zhengzhou Univ Sch Informat Engn Zhengzhou Peoples R China Zhongyuan Univ Technol Sch Elect & Informat Engn Zhengzhou Peoples R China

ISBN: (纸本)9781479974924

In this article, a recently proposed niching algorithm called neighborhood based speciation Differential Evolution (NSDE) is used to solve CEC 2015 multi-modal competition problems. Although DE algorithm is effective in solving single global optimal, the result is not acceptalbe when solving multi-optima problems. NSDE was proposed to enable DE with the ability of handling multi-modal optimization problems. In NSDE, the mutation is performed within each Euclidean neighborhood. During the evolution the population of NSDE will evolve toward the respective global/local optimum and the neighborhood mutation can maintain the multiple optima found. The performance of NSDE is compared with the original SDE. From the simulation results, we can observe that NSDE is effective in solving multi-modal optimization problems.

关键词： differential evolution multimodal optimization neighborhood mutation niching algorithm

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Differential evolution based on fitness Euclidean-distance ratio for multimodal optimization

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NEUROCOMPUTING 2014年 137卷 252-260页

作者： Liang, J. J. Qu, B. Y. Mao, X. B. Niu, B. Wang, D. Y. Zhengzhou Univ Sch Elect Engn Zhengzhou 450052 Peoples R China Zhongyuan Univ Technol Sch Elect & Informat Engn Zhengzhou Peoples R China Shenzhen Univ Coll Management Shenzhen Peoples R China Chinese Acad Sci Hefei Inst Intelligent Machines Hefei Peoples R China

The prime target of multi-modal optimization is to find multiple global and local optima of a problem in one single run. Differential evolution is a recently proposed stochastic optimization technique. Though variants of differential evolution (DE) are highly effective in locating single global optimum, few DE algorithms perform well when solving multi-optima problems. In this paper, a modified Fitness Euclidean-distance Ratio (FER) technique is incorporated into DE to enhance the DE's ability of locating and maintaining multiple peaks. The proposed algorithm is tested on a number of benchmark test functions and the experimental results show that the proposed simple algorithm performs better comparing with a number of state-of-the-art multimodal optimization approaches. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Multimodal optimization niching algorithm Differential evolution Fitness Euclidean-distance ratio

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Differential Evolution with Neighborhood Mutation for Multimodal Optimization

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2012年第5期16卷 601-614页

作者： Qu, B. Y. Suganthan, P. N. Liang, J. J. Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Henan Peoples R China

In this paper, a neighborhood mutation strategy is proposed and integrated with various niching differential evolution (DE) algorithms to solve multimodal optimization problems. Although variants of DE are highly effective in locating a single global optimum, no DE variant performs competitively when solving multi-optima problems. In the proposed neighborhood based differential evolution, the mutation is performed within each Euclidean neighborhood. The neighborhood mutation is able to maintain the multiple optima found during the evolution and evolve toward the respective global/local optimum. To test the performance of the proposed neighborhood mutation DE, a total of 29 problem instances are used. The proposed algorithms are compared with a number of state-of-the-art multimodal optimization approaches and the experimental results suggest that although the idea of neighborhood mutation is simple, it is able to provide better and more consistent performance over the state-of-the-art multimodal algorithms. In addition, a comparative survey on niching algorithms and their applications are also presented.

关键词： Crowding differential evolution multimodal optimization neighborhood mutation niching algorithm sharing speciation

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