Chaos Adaptive Improved Particle Swarm Algorithm for Solving Multi-Objective Optimization

To overcome the problem of premature convergence on Particle Swarm Optimization (PSO), this paper proposes both the improved particle swarm optimization methods (IPSO) based on self-adaptive regulation strategy and the Chaos Theory. Given the effective balance of particles’ searching and development ability, the self-adaptive regulation strategy is employed to optimize the inertia weight. To improve efficiency and quality of searching, the learning factor is optimized by generating Chaotic Sequences by Chaos Theory. The improved method proposed in this paper achieves better convergence performance and increases the searching speed. Simulation results of some typical optimization problems and comparisons with typical multi-objective optimization algorithms show that IPSO has a fast convergence speed, the diversity of non-dominated and the ideal convergence. The algorithm meets the requirements of multi-objective optimization problem. DOI : http://dx.doi.org/10.11591/telkomnika.v12i1.3189

[1]  Kalyanmoy Deb,et al.  Multi-objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems , 1999, Evolutionary Computation.

[2]  Kyriakos C. Giannakoglou,et al.  A multilevel approach to single- and multiobjective aerodynamic optimization , 2008 .

[3]  Wu Wu,et al.  Scheduling Workflow in Cloud Computing Based on Hybrid Particle Swarm Algorithm , 2012 .

[4]  Lothar Thiele,et al.  Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study , 1998, PPSN.

[5]  Marco Laumanns,et al.  Combining Convergence and Diversity in Evolutionary Multiobjective Optimization , 2002, Evolutionary Computation.

[6]  M. Gunzburger,et al.  A Domain Decomposition Method for Optimization Problems for Partial Differential Equations , 2000 .

[7]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[8]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[9]  C. Fonseca,et al.  GENETIC ALGORITHMS FOR MULTI-OBJECTIVE OPTIMIZATION: FORMULATION, DISCUSSION, AND GENERALIZATION , 1993 .

[10]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[11]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[12]  DebK.,et al.  A fast and elitist multiobjective genetic algorithm , 2002 .

[13]  John J. Grefenstette,et al.  Proceedings of the 1st International Conference on Genetic Algorithms , 1985 .

[14]  Kin Keung Lai,et al.  A fuzzy approach to the multiobjective transportation problem , 2000, Comput. Oper. Res..

[15]  Jeffrey Horn,et al.  Multiobjective Optimization Using the Niched Pareto Genetic Algorithm , 1993 .

[16]  Zhu Ping.  Adaptive fuzzy particle swarm optimization algorithm , 2010 .

[17]  Marco Laumanns,et al.  Performance assessment of multiobjective optimizers: an analysis and review , 2003, IEEE Trans. Evol. Comput..

[18]  Peter J. Fleming,et al.  Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization , 1993, ICGA.

[19]  Michael de la Maza.  The Fifth International Conference on Genetic Algorithms , 1994, AI Mag..

[20]  Xuesong Yan,et al.  Orthogonal Particle Swarm Optimization Algorithm and Its Application in Circuit Design , 2013 .

[21]  Chen Min.  Adaptive Evolutionary Particle Swarm Algorithm for Multi-Objective Optimisation , 2009 .

[22]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[23]  Tong Heng Lee,et al.  Evolutionary Algorithms for Multi-Objective Optimization: Performance Assessments and Comparisons , 2004, Artificial Intelligence Review.

[24]  I. Loshchilov,et al.  A Comparison of Multiobjective Evolutionary Algorithms , 2009 .