1.Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing 210044 2.School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044
Abstract Considerating the importance of balancing global and local search for multi-objective particle swarm optimization algorithm(MOPSO) to obtain the complete and uniform Pareto front(PF), a balance search strategy is designed and an improved multi-objective particle swarm optimization algorithm (bsMOPSO) is proposed.The strategy is composed of two novel search sub-strategies. In the local search sub-strategy, self-exploitation of archive set is designed to achieve local search involving the entire Pareto front by disturbing fixed ratio of uniform particles in archive set with Cauchy mutation. In the global search sub-strategy, guided search by the best boundary particle is designed through using the optimal boundary particle as the global optimal solution, and therefore more boundary areas of each objective function are searched by part of particle swarm. By comparing five algorithms on the series of ZDT and DTLZ test functions, the results demonstrate that bsMOPSO achieves better Pareto optimal convergence and distribution.
Fund:Supported by National Natural Science Foundation of China(No.61403206), Natural Science Foundation of Jiangsu Province(No.BK20151458), Qing Lan Project of Jiangsu Province(2016)
About author:: GENG Huantong(Corresponding author), born in 1973, Ph.D., professor. His research interests include computation intelligence and multi-objective optimization. CHEN Zhengpeng, born in 1990, master student. His research interests include multi-objective optimization. (CHEN Zhe, born in 1991, master student. His research interests include multi-objective optimization. ZHOU Lifa, born in 1990, master student. His research interests include multi-objective optimization.
[1] 胡 旺, YEN G G, 张 鑫.基于Pareto熵的多目标粒子群优化算法.软件学报, 2014, 25(5): 1025-1050. (HU W, YEN G G, ZHANG X. Multiobjective Particle Swarm Optimization Based on Pareto Entropy. Journal of Software, 2014, 25(5): 1025-1050.) [2] COELLO C A C, LECHUGA M S. MOPOS: A Proposal for Multiple Objective Particle Swarm Optimization // Proc of the Congress on Evolutionary Computation. Washington, USA: IEEE, 2002, II: 1051-1056. [3] COELLO C A C, PULIDO G T, LECHUGA M S. Handling Multiple Objectives with Particle Swarm Optimization. IEEE Transactions on Evolutionary Computation, 2004, 8(3): 256-279. [4] SIERRA M R, COELLO C A C. Improving PSO-Based Multi-objective Optimization Using Crowding, Mutation and ∈-Dominance // Proc of the International Conference on Evolutionary Multi-criterion Optimization. Berlin, Germany: Springer, 2005: 505-519. [5] LI X D. Better Spread and Convergence: Particle Swarm Multiobjective Optimization Using the Maximin Fitness Function // Proc of the International Conference on Genetic and Evolutionary Computation. Berlin, Germany: Springer, 2004: 117-128. [6] PADHYE N, BRANKE J, MOSTAGHIM S. Empirical Comparison of MOPSO Methods: Guide Selection and Diversity Preservation // Proc of the IEEE Congress on Evolutionary Computation. Washington, USA: IEEE, 2009: 2516-2523. [7] ABIDO M A. Multiobjective Particle Swarm Optimization with Nondominated Local and Global Sets. Natural Computing, 2010, 9(3): 747-766. [8] YEN G G, LEONG W F. Dynamic Multiple Swarms in Multiobjective Particle Swarm Optimization. IEEE Transactions on Systems Man and Cybernetics(Systems and Humans), 2009, 39(4): 890-911. [9] LIANG J J, QU B Y, SUGANTHAN P N, et al. Dynamic Multi-swarm Particle Swarm Optimization for Multi-objective Optimization Problems // Proc of the IEEE Congress on Evolutionary Computation. Washington, USA: IEEE, 2012. DOI: 10.1109/CEC.2012.6256416. [10] DANESHYARI M, YEN G G. Constrained Multiple-swarm Particle Swarm Optimization within a Cultural Framework. IEEE Transactions on Systems, Man and Cybernetics(Systems and Humans), 2012, 42(2): 475-490. [11] COOREN Y, CLERC M, SIARRY P. MO-TRIBES: An Adaptive Multiobjective Particle Swarm Optimization Algorithm. Computational Optimization and Applications, 2011, 49(2): 379-400. [12]HU W, YEN G G. Adaptive Multiobjective Particle Swarm Optimization Based on Parallel Cell Coordinate System. IEEE Transactions on Evolutionary Computation, 2015, 19(1): 1-18. [13] 公茂果,焦李成,杨咚咚,等.进化多目标优化算法研究.软件学报, 2009, 20(2): 271-289. (GONG M G, JIAO L C, YANG D D, et al. Research on Evolutionary Multi-objective Optimization Algorithms. Journal of Software, 2009, 20(2): 271-289.) [14] ZITZLER E, DEB K, THIELE L. Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation, 2000, 8(2): 173-195. [15] DEB K, THIELE L, LAUMANNS M, et al. Scalable Test Problems for Evolutionary Multiobjective Optimization // Proc of the International Conference on Evolutionary Multiobjective Optimization. Berlin, Germany: Springer, 2005: 105-145. [16] RAQUEL C R, NAVAL JR P C. An Effective Use of Crowding Distance in Multiobjective Particle Swarm Optimization // Proc of the 7th Annual Conference on Genetic and Evolutionary Computation. New York, USA: ACM, 2005: 257-264. [17] ALVAREZ-BENITEZ J E, EVERSON R M, FIELDSEND J E. A MOPSO Algorithm Based Exclusively on Pareto Dominance Concepts // Proc of the 3rd International Conference on Evolutionary Multi-criterion Optimization. Berlin, Germany: Springer, 2005: 459-473. [18] DEB K, PRATAP A, AGARWAL S, et al. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197. [19] ZITZLER E, THIELE L. Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. IEEE Transactions on Evolutionary Computation, 1999, 3(4): 257-271. [20] BOSMAN P A N, THIERENS D. The Balance between Proximity and Diversity in Multiobjective Evolutionary Algorithms. IEEE Transactions on Evolutionary Computation, 2003, 7(2): 174-188.
2017, Vol. 30 
