计算机科学 ›› 2016, Vol. 43 ›› Issue (12): 248-254.doi: 10.11896/j.issn.1002-137X.2016.12.045
耿焕同,赵亚光,陈哲,李辉健
GENG Huan-tong, ZHAO Ya-guang, CHEN Zhe and LI Hui-jian
摘要: 粒子群优化算法已成为求解多目标优化问题的有效方法之一,而速度更新公式中的惯性、局部和全局3个速度项的系数的动态合理设置是算法优化效率的关键问题。为解决现有算法仅单独设置各速度项系数导致优化效率不高的问题,提出了一种均衡各速度项系数的多目标粒子群优化算法。该方法旨在通过粒子的局部最优和全局最优的信息来引导种群的进化方向,动态调整每一个粒子速度项系数来均衡惯性、局部和全局3个速度项在搜索中的作用,从而更为准确地刻画算法的搜索能力和搜索精度,更好地平衡算法的探究和探索能力,进一步提高粒子群优化算法解决复杂多目标优化问题的效率。在7个标准测试函数上进行实验,并与5种经典的进化算法进行对比,结果表明新算法在综合指标IGD以及多样性评估指标Δ评分上具有更好的收敛速度和分布性,验证了新算法的有效性。
[1] Lalwani S,Singhal S,Kumar R,et al.A comprehensive survey:applications of multi-objective particle swarm optimization(mopso) algorithm[J].Transactions on Combinatorics,2013,2(1):39-101 [2] Gong Mao-guo,Jiao Li-cheng,Yang Dong-dong,et al.Research on evolutionary multi-objective optimization algorithms[J].Journal of Software,2009,0(2):271-289(in Chinese) 公茂果,焦李成,杨咚咚,等.进化多目标优化算法研究[J].软件学报,2009,20(2):271-289 [3] Kennedy J,Eberhart R C.Particle swarm optimization[C]∥IEEE International Conference on Neural Networks.1995:1942-1948 [4] Coello Coello C A,Lechuga M S.Mopso:a proposal for multiple objective particle swarm optimization[C]∥IEEE Congress on Evolutionary Computation.2002:1051-1056 [5] Mukhopadhyay A,Maulik U,Bandyopadhyay S,et al.Survey of multiobjective evolutionary algorithms for data mining:part II[J].IEEE Transactions on Evolutionary Computation,2014,18(1):20-35 [6] Onieva E,Hernandez-Jayo U,Osaba E,et al.A multi-objective evolutionary algorithm for the tuning of fuzzy rule bases for uncoordinated intersections in autonomous driving[J].Information Sciences,2015,321(c):14-30 [7] Wei J,Jia L.A novel particle swarm optimization algorithm with local search for dynamic constrained multi-objective optimization problems[C]∥IEEE Congress on Evolutionary Computation.2013:2436-2443 [8] Yao Xin,Yang Sheng-xiang.Evolutionary computation for dy-namic optimization problems[M].Springer-Verlag Berlin Heidelberg,2013 [9] Shi Y,Eberhart R.A modified particle swarm optimizer[C]∥IEEE World Congress on Computational Intelligence.1998:69-73 [10] Wang H,Yen G G.Adaptive multiobjective particle swarm optimization based on parallel cell coordinate system[J].IEEE Transactions on Evolutionary Computation,2015,19(1):1-18 [11] Bansal J C,Singh P K,Saraswat M,et al.Inertia weight strategies in particle swarm optimization[C]∥2011 Third World Congress on Nature and Biologically Inspired Computing.2011:633-640 [12] Han Hong-jiang,Li Zheng-rong,Wei Zhen-chun.An adaptive ine-rtia weight particle swarm optimization and its simulation research[J].Journal of System Simulation,2006,8(10):2969-2971(in Chinese) 韩江洪,李正荣,魏振春.一种自适应粒子群优化算法及其仿真研究[J].系统仿真学报,2006,18(10):2969-2971 [13] Cooren Y,Clerc M,Siarry P.MO-TRIBES.An adaptive multiobjective particle swarm optimization algorithm[J].Computational Optimization and Applications,2011,49(2):379-400 [14] Smith E A,Boyd R,Richerson P J.Culture and the evolutionary process[J].American Anthropologist,1987,89(1):203-205 [15] Hu Wang,Yen G G,Zhang Xin.Based on pareto entropy multi-objective particle swarm optimization[J].Journal of Software,2014,25(5):1025-1050(in Chinese) 胡旺,Yen G G,张鑫.基于Pareto熵的多目标粒子群优化算法[J].软件学报,2014,25(5):1025-1050 [16] Zitzler E,Deb K,Thiele L.Comparison of multiobjective evolutionary algorithms:empirical results[J].IEEE Transactions on Evolutionary Computation,2000,8(2):173-195 [17] Deb K,Thiele L,Laumanns M,et al.Scalable multi-objective optimization test problems[C]∥IEEE Congress on Evolutionary Computation.2002:825-830 [18] Kukkonen S,Lampinen J.Performance assessment of genera-lized differential evolution 3 with a given set of constrained multi- objective test problems[C]∥IEEE Congress on Evolutionary Computation.2009:1943-1950 [19] Nebro A J,Durillo J J,García-Nieto J,et al.SMPSO:a new pso-based metaheuristic for multi-objective optimization[C]∥IEEE symposium on Computational intelligence in multi-criteria decision-making.2009:66-73 [20] Zhao Y,Liu H L.Multi-objective particle swarm optimization algorithm based on population decomposition[M].Intelligent Data Engineering and Automated Learning-IDEAL 2013.Springer Berlin Heidelberg,2013:463-470 [21] Moubayed N A,Petrovski A,Mccall J.A novel smart multi-objective particle swarm optimisation using decomposition[M].Parallel Problem Solving from Nature,PPSN XI.Springer Berlin Heidelberg,2010:1-10 [22] Zhang Q,Li H.MOEA/D:A multiobjective evolutionary algorithm based on decomposition[J].IEEE Transactions on Evolutionary Computation,2007,1(6):712-731 [23] ETH Zurich [EB/OL].[2015-09-25].http://people.ee.ethz.ch/~sop/ download/supplementary/testproblem [24] Deb K,Pratap A,Agarwal S,et al.A fast and elitist multi-objective genetic algorithm:NSGA II[J].IEEE Transactions on Evolutionary Computation,2002,6(2):182-197 |
No related articles found! |
|