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2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

作者： NA-NA LI YONG-FENG DONG JUN-HUA GU RUI-YING ZHOU School of Computer Science and Technology Tianjin University Tianjin 300072 China School of Compu School of Computer Science and Engineering Hebei University of Technology Tianjin 300130 China

This paper analyzes immune theory and Hopfield Neural Network (HNN), and then proposes a new algorithm for multimodal *** new algorithm uses the advantages of both HNN and immune algorithm, and it appears excellent characteristic in optimal problems of multimodal *** detail, we obtain a group of solutions with variety by immune algorithm (IA) first;and then the solutions are partitioned into some *** we take cluster centroids returned by clustering algorithm as the initial value of each HNN, and run the Hopfield neural networks to obtain all minima.

关键词： Hopfield Network Immune algorithm Cluster multimodal function optimization

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multimodal function optimizations with multiple maximums and multiple minimums using an improved PSO algorithm

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APPLIED SOFT COMPUTING 2017年 60卷 60-72页

作者： Chang, Wei-Der Shu Te Univ Dept Comp & Commun Kaohsiung 824 Taiwan

In this paper, a multimodal function optimization problem consisting of multiple maximums and multiple minimums is solved using an improved particle swarm optimization (PSO) algorithm. In the proposed scheme, the original population needs to be randomly divided into two main groups in the first stage. One group is to tackle the maximum optimization of the multimodal function and the other then focuses on the function minimum optimization. In the second stage, each group is split up into several subgroups in order to seek for function optimums simultaneously. There is no relation among subgroups and each subgroup can individually seek for one of function optimums. To achieve that, it is necessary to enroll the best particle information of each subgroup. It means that the proposed structure contains a number of best particles, not a single global best particle. The third stage is to modify the velocity updating formula of the algorithm where the global best particle is simply replaced by the best particle of each subgroup. Under the proposed scheme, multiple maxima and minima of the multimodal function can probably be solved separately and synchronously. Finally, many different kinds of multimodal function problems are illustrated to certify the applicability of the presented method, including one maximum and one minimum, two maximums and two minimums, multiple maximums and multiple minimums, and a complex engineering optimization problem with inequality conditions. (C) 2017 Elsevier B.V. All rights reserved.

关键词： multimodal function optimization Multiple maximums and multiple minimums Particle swarm optimization (PSO)

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Particle swarm optimization with adaptive population size and its application

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APPLIED SOFT COMPUTING 2009年第1期9卷 39-48页

作者： Chen DeBao Zhao ChunXia Huibei Coal Ind Teachers Coll Dept Phys Huaibei 235000 Peoples R China Nanjing Univ Sci & Technol Comp Inst Nanjing 210094 Peoples R China

A particle swarm optimization (PSO) that uses an adaptive variable population size and periodic partial increasing or declining individuals in the form of ladder function is proposed in the paper. The aim is to enhance the overall performance of PSO. The proposed scheme adjusts the population size automatically according to the value of diversity of the population in ultimate time of current ladder. The processing of adding and declining the number of population is designed. The validity of the given algorithm is tested for a variety of benchmark problems and neural network training problems. The results of the proposed scheme are compared with the linearly decreasing inertia weight PSO (LDWPSO) and mutation PSO (MPSO), from which it is evident that the proposed scheme enhances the overall performance of PSO. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Particle swarm optimization multimodal function optimization Local convergence Diversity function Ladder particle swarm optimization (LPSO) Mutation particle swarm optimization (MPSO) Linearly decreasing inertia weight PSO (LDWPSO)

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multimodal optimization problem in contamination source determination of water supply networks

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SWARM AND EVOLUTIONARY COMPUTATION 2019年 47卷 66-71页

作者： Yan, Xuesong Zhao, Jing Hu, Chengyu Zeng, Deze China Univ Geosci Sch Comp Sci Wuhan 430074 Hubei Peoples R China

It makes great economic losses and bad social influence for our country about some accidental drinking water contamination and vicious attacks to water distribution networks. In terms of solving the problems of drinking water contamination caused by accidental contaminant event in water supply network, used the techniques just like sensor network which could determinate the source location to isolate the contaminated area and minimized its hazards. Previous studies have shown that the contamination source determination problem model can be utilized to convert the contamination source determination problem to an unimodal function optimization problem. However, we notice that it is a multimodal function optimization problem in essence and the number of its solution has non-uniqueness feature. In this paper, we first modified the problem model with formulate the threshold value based on the previous works and proposed the niching genetic algorithm calculate multiple contamination sources, and provide the possibility for screening the true contamination source. Furthermore, this paper applies different distribution networks verify the validity after the threshold formulation as well as the effectiveness of algorithm from various aspects.

关键词： Contamination source determination Niching technology Genetic algorithm multimodal function optimization

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A multipopulation cultural algorithm using fuzzy clustering

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APPLIED SOFT COMPUTING 2007年第2期7卷 506-519页

作者： Alami, J. El Imrani, A. Bouroumi, A. Univ Mohammed V Agdal Fac Sci Lab LCS Rabat Morocco

In this paper, we propose a new model for multimodal function optimization, based on the concepts of cultural algorithms and fuzzy clustering. Fuzzy clustering is used in order to partition a same population into communicating subpopulations which evolve in parallel, whilst cultural exchange ensures preserving population diversity. The ability of this model to identify global/ local optima without loss of diversity is demonstrated using several multimodal test functions, including a deceptive example. (c) 2006 Elsevier B. V. All rights reserved.

关键词： cultural algorithm multimodal function optimization fuzzy clustering

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Particle swarm optimization with age-group topology for multimodal functions and data clustering

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COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION 2013年第11期18卷 3134-3145页

作者： Jiang, Bo Wang, Ning Wang, Liping Zhejiang Univ Natl Lab Ind Control Technol Inst Cyber Syst & Control Hangzhou 310027 Zhejiang Peoples R China Zhejiang Univ Technol Coll Econ & Management Hangzhou 310023 Zhejiang Peoples R China

This paper proposes particle swarm optimization with age-group topology (PSOAG), a novel age-based particle swarm optimization (PSO). In this work, we present a new concept of age to measure the search ability of each particle in local area. To keep population diversity during searching, we separate particles to different age-groups by their age and particles in each age-group can only select the ones in younger groups or their own groups as their neighbourhoods. To allow search escape from local optima, the aging particles are regularly replaced by new and randomly generated ones. In addition, we design an age-group based parameter setting method, where particles in different age-groups have different parameters, to accelerate convergence. This algorithm is applied to nonlinear function optimization and data clustering problems for performance evaluation. In comparison against several PSO variants and other EAs, we find that the proposed algorithm provides significantly better performances on both the function optimization problems and the data clustering tasks. (C) 2013 Elsevier B. V. All rights reserved.

关键词： Particle swarm optimization Age-group topology multimodal function optimization Data clustering

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A Novel multimodal optimization Algorithm Applied to Electromagnetic optimization

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IEEE TRANSACTIONS ON MAGNETICS 2011年第6期47卷 1667-1673页

作者： Woo, Dong-Kyun Choi, Jong-Ho Ali, Mohammad Jung, Hyun-Kyo Seoul Natl Univ Sch Elect Engn Seoul 151742 South Korea

The selection of optimum parameters in electromagnetic design usually requires optimization of multimodal, nonlinear functions. This leads to extensive calculations which pose a huge inconvenience in the design process. This paper proposes a novel algorithm for dealing efficiently with this issue. The proposed algorithm interprets the problem as an unexplored terrain for climbing. Through the use of contour line concept coupled with Kriging, the algorithm finds out all the peaks in the problem domain with as few function calls as possible. The efficiency of the proposed algorithm is demonstrated by application to conventional test functions. In this paper, the simulation results show that skewing does not necessarily reduce the cogging torque but may cause it to increase for certain pole-arc to pole-pitch ratio. The developed algorithm is applied to the magnet shape optimization of an axial flux permanent magnet synchronous machine and the cogging torque was reduced to 79.8% of the initial one.

关键词： Axial-flux permanent magnet motor climb method Kriging multimodal function optimization

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Immune inspired restricted somatic hypermutation for multimodal optimization

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IMACS Multiconference on Computational Engineering in Systems Applications (CESA 2006)

作者： Tang, T. Y. Qiu, J. J. Zhejiang Univ Dept Elect Engn Hangzhou 310027 Zhejiang Peoples R China

ISBN: (纸本)9787302139225

An improved immune optimization algorithm is proposed to solve the contradiction between global search and local optimization which existed in most traditional optimization algorithms for multimodal function. The key idea lies on that hypermutation operator with restriction is designed for parallel search. By simulating the property of metadynamics in immune system,the algorithm can dynamically adjust the population size. In the view of the population size and individual space, the validity of the mutation operator is analyzed by transition probability. It is proved theoretically that the presented algorithm is convergence. The simulation to 4 benchmark functions verified that the algorithm can obtain the multiple local and global optima simultaneously.

关键词： multimodal function optimization hypermutation operator varidble population diversity

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An improved adaptive immune genetic algorithm based on information entropy

An improved adaptive immune genetic algorithm based on infor...

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International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration

作者： Liu Xingxing Yang, Zhichao Wang Pan Wuhan Univ Technol Sch Management Wuhan Peoples R China Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA Wuhan Univ Technol Sch Automat Wuhan Peoples R China

ISBN: (纸本)9781467383127

The promotion on search efficacy of immune genetic algorithm is an enduring issue. An adaptive immune genetic algorithm based on information entropy is proposed. The parameters of similarity and affinity are designed based on information entropy. It integrated density control, improved crossover and mutation. The algorithm can make better use of global and local information and differences between antibodies for diversity control. The adjustments improve the speed, accuracy and convergence stability. Simulation results on multimodal function show that the proposed method has better optimization capability.

关键词： Immune genetic algorithm Information entropy Similarity multimodal function optimization

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Adaptive polyclonal programming algorithm with applications 5

Adaptive polyclonal programming algorithm with applications

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5th International Conference on Computational Intelligence and Multimedia Applications

作者： Du, HF Jiao, LC Liu, RC Xidian Univ Natl Key Lab Rader Signal Progcessing Xian 710071 Peoples R China

ISBN: (纸本)0769519571

Based on the clonal selection theory, the main mechanism of immune clone applied in artificial intelligence is analyzed in this paper. A new operator, Adaptive Polyclonal Operator as well as a novel artificial immune system algorithm, APPA (Adaptive Polyclonal Programming Algorithm), is put forward. Compared with some other Evolutionary Programming algorithms (like Breeder Genetic Algorithm), APPA, behaving as an evolutionary strategy, is shown to be capable of solving complex machine learning tasks effectively, like multimodal function optimization.

关键词： learning (artificial intelligence) artificial life adaptive systems evolutionary computation clonal selection adaptive polyclonal programming algorithm immune clone artificial intelligence adaptive polyclonal operator artificial immune system algorithm APPA evolutionary programming algorithm breeder genetic algorithm complex machine learning multimodal function optimization

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