Multi-objective particle swarm-differential evolution algorithm
A multi-objective particle swarm-differential evolution algorithm (MOPSDE) is proposed that combined a particle swarm optimization (PSO) with a differential evolution (DE). During consecutive generations, a scale factor is produced by using a proposed mechanism based on the simulated annealing metho...
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| Published in | Neural computing & applications Vol. 28; no. 2; pp. 407 - 418 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
London
Springer London
01.02.2017
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0941-0643 1433-3058 |
| DOI | 10.1007/s00521-015-2073-y |
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| Abstract | A multi-objective particle swarm-differential evolution algorithm (MOPSDE) is proposed that combined a particle swarm optimization (PSO) with a differential evolution (DE). During consecutive generations, a scale factor is produced by using a proposed mechanism based on the simulated annealing method and is applied to dynamically adjust the percentage of use of PSO and DE. In addition, the mutation operation of DE is improved, to satisfy that the proposed algorithm has different mutation operation in different searching stage. As a result, the capability of the local searching is enhanced and the prematurity of the population is restrained. The effectiveness of the proposed method has been validated through comprehensive tests using benchmark test functions. The numerical results obtained by this algorithm are compared with those obtained by the improved non-dominated sorting genetic algorithm (NSGA-II) and the other algorithms mentioned in the literature. The results show the effectiveness of the proposed MOPSDE algorithm. |
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| AbstractList | A multi-objective particle swarm-differential evolution algorithm (MOPSDE) is proposed that combined a particle swarm optimization (PSO) with a differential evolution (DE). During consecutive generations, a scale factor is produced by using a proposed mechanism based on the simulated annealing method and is applied to dynamically adjust the percentage of use of PSO and DE. In addition, the mutation operation of DE is improved, to satisfy that the proposed algorithm has different mutation operation in different searching stage. As a result, the capability of the local searching is enhanced and the prematurity of the population is restrained. The effectiveness of the proposed method has been validated through comprehensive tests using benchmark test functions. The numerical results obtained by this algorithm are compared with those obtained by the improved non-dominated sorting genetic algorithm (NSGA-II) and the other algorithms mentioned in the literature. The results show the effectiveness of the proposed MOPSDE algorithm. |
| Author | Su, Yi-xin Chi, Rui |
| Author_xml | – sequence: 1 givenname: Yi-xin surname: Su fullname: Su, Yi-xin email: suyixin@whut.edu.cn organization: School of Automation, Wuhan University of Technology – sequence: 2 givenname: Rui surname: Chi fullname: Chi, Rui email: rui85@126.com organization: School of Automation, Wuhan University of Technology |
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| Keywords | Differential evolution Multi-objective optimization Scale factor Particle swarm optimization |
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| References_xml | – reference: Zitzler E, Laumanns M, Thiele L (2001) SPEA2: Improving the strength Pareto evolutionary algorithm for multiobjective optimization. In: Proceedings of the EUROGEN 2001-evolutionary method for design: optimization and control for industrial problem, K.C. Giannakoglou, Ed., pp 95–100 – reference: WangXSHaoMLChengYHLeiRHPDE-PEDA: a new Pareto-based multi-objective optimization algorithmJ Univ Comput Sci200915472274125118441216.90080 – reference: Coello Coello CA, Lechuga MS et al (2002) MOPSO: a proposal for multiple objective particle swarm optimization. In: Proceedings of the IEEE international conference on evolutionary computation. New Jersey, pp 1051–1056 – reference: BazaraaMSSheraliHDShettyCMNonlinear programming, theory and algorithm[m]1979New YorkAcademic Press0476.90035 – reference: Hu X, Eberhart RC (2002) Multi-objective optimization using dynamic neighborhood particle swarm optimization. In: IEEE congress on evolutionary computation (CEC 2002). Honolulu. Hawaii, USA, pp 1677–1681 – reference: DebKMulti-objective optimization using evolutionary algorithms2001ChichesterWiley0970.90091 – reference: Knowles J, Corne D (1999) The Pareto archived evolutionary strategy: a new baseline algorithm for multi-objective optimization. In: Proceedings of the conference on evolutionary computation. IEEE Press, Piscataway, NJ, pp 98–105 – reference: Horn J, Nafpliotis N, Goldberg DE (1994) A niched Pareto genetic algorithm for multi-objective optimization. In: Proceedings of the first IEEE conference on evolutionary computation. IEEE, Piscataway, pp 82–87 – reference: Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of the IEEE Intentional joint conference on neural networks. IEEE Press, pp 1942–1948 – reference: StornRPriceKDifferential evolution-a simple and efficient heuristic for global optimization over continuous spacesJ Global Optim1997114341359147955310.1023/A:10082028213280888.90135 – reference: Coello CoelloCALamontGBVan VeldhuizenDAEvolutionary algorithms for solving multi-objective problems2007Berlin, Heidelberg, New YorkSpringer Science & Business Media1142.90029 – reference: JoshuaTKDavidJSMatthewDCTesting of a spreading mechanism to promote diversity in multi-objective particle swarm optimizationOptim Eng2014162279302 – reference: Coello CoelloCAPulidoGTLechugaMSHandling multiple objectives with particle swarm optimizationIEEE Trans Evol Comput20048325627910.1109/TEVC.2004.826067 – reference: Hernández-DomínguezJSToscano-PulidoGCoello CoelloACA multi-objective particle swarm optimizer enhanced with a differential evolution schemeArtif Evol2012Berlin, HeidelbergSpringer16918010.1007/978-3-642-35533-2_15 – reference: DebKAgrawalSPratapAMeyarivanTA fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-IILect Notes Comput Sci2000191784985810.1007/3-540-45356-3_83 – reference: HaoZFGuoGHHuangHA particle swarm optimization algorithm with differential evolutionIEEE Int Conf Syst Mach Learn Cybernet200721031103510.1109/ICMLC.2007.4370294 – reference: Van Veldhuizen DA and Lamont GB (1998) evolutionary computation and convergence to a Pareto Front. In: Late breaking papers at the genetic programming 1998 conference. Stanford University, pp 221–228 – reference: DebKPratapAAgarwalSMeyarivanTA fast and elitist multi-objective genetic algorithm: NSGA-IIIEEE Trans Evol Comput20026218219710.1109/4235.996017 – reference: ZitzlerEThieleLMulti-objective evolutionary algorithms: a comparative case study and the strength Pareto approachIEEE Trans Evol Comput19993425727110.1109/4235.797969 – reference: SrinivasNDebKMulti-objective function optimization using non-dominated sorting genetic algorithmsEvol Comput19942322124810.1162/evco.1994.2.3.221 – reference: WuLHWangYNChenZLModified differential evolution algorithm for mixed-integer non-linear programming problemsJ Chin Comput Syst2007284666669 – ident: 2073_CR6 – start-page: 169 volume-title: Artif Evol year: 2012 ident: 2073_CR12 doi: 10.1007/978-3-642-35533-2_15 – volume: 1917 start-page: 849 year: 2000 ident: 2073_CR19 publication-title: Lect Notes Comput Sci doi: 10.1007/3-540-45356-3_83 – ident: 2073_CR5 – ident: 2073_CR11 – ident: 2073_CR7 doi: 10.1109/CEC.2002.1004388 – volume-title: Evolutionary algorithms for solving multi-objective problems year: 2007 ident: 2073_CR21 – volume: 3 start-page: 257 issue: 4 year: 1999 ident: 2073_CR4 publication-title: IEEE Trans Evol Comput doi: 10.1109/4235.797969 – volume: 28 start-page: 666 issue: 4 year: 2007 ident: 2073_CR15 publication-title: J Chin Comput Syst – volume: 2 start-page: 221 issue: 3 year: 1994 ident: 2073_CR2 publication-title: Evol Comput doi: 10.1162/evco.1994.2.3.221 – volume-title: Multi-objective optimization using evolutionary algorithms year: 2001 ident: 2073_CR14 – ident: 2073_CR18 – ident: 2073_CR1 – volume: 15 start-page: 722 issue: 4 year: 2009 ident: 2073_CR17 publication-title: J Univ Comput Sci – volume: 11 start-page: 341 issue: 4 year: 1997 ident: 2073_CR13 publication-title: J Global Optim doi: 10.1023/A:1008202821328 – volume: 8 start-page: 256 issue: 3 year: 2004 ident: 2073_CR9 publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2004.826067 – ident: 2073_CR8 doi: 10.1109/ICNN.1995.488968 – volume: 16 start-page: 279 issue: 2 year: 2014 ident: 2073_CR10 publication-title: Optim Eng – volume-title: Nonlinear programming, theory and algorithm[m] year: 1979 ident: 2073_CR20 – volume: 6 start-page: 182 issue: 2 year: 2002 ident: 2073_CR3 publication-title: IEEE Trans Evol Comput doi: 10.1109/4235.996017 – volume: 2 start-page: 1031 year: 2007 ident: 2073_CR16 publication-title: IEEE Int Conf Syst Mach Learn Cybernet |
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| SubjectTerms | Algorithms Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Evolutionary algorithms Evolutionary computation Genetic algorithms Image Processing and Computer Vision Multiple objective analysis Mutation Original Article Particle swarm optimization Probability and Statistics in Computer Science Searching Simulated annealing Sorting algorithms |
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