Handling multi-objective optimization problems with a multi-swarm cooperative particle swarm optimizer

Abstract This paper presents a new multi-objective optimization algorithm in which multi-swarm cooperative strategy is incorporated into particle swarm optimization algorithm, called multi-swarm cooperative multi-objective particle swarm optimizer (MC-MOPSO). This algorithm consists of multiple slave swarms and one master swarm. Each slave swarm is designed to optimize one objective function of the multi-objective problem in order to find out all the non-dominated optima of this objective function. In order to produce a well distributed Pareto front, the master swarm is developed to cover gaps among non-dominated optima by using a local MOPSO algorithm. Moreover, in order to strengthen the capability locating multiple optima of the PSO, several improved techniques such as the Pareto dominance-based species technique and the escape strategy of mature species are introduced. The simulation results indicate that our algorithm is highly competitive to solving the multi-objective optimization problems.

[1]  Tong Heng Lee,et al.  Evolutionary algorithms with dynamic population size and local exploration for multiobjective optimization , 2001, IEEE Trans. Evol. Comput..

[2]  Carlos A. Coello Coello,et al.  Improving PSO-Based Multi-objective Optimization Using Crowding, Mutation and epsilon-Dominance , 2005, EMO.

[3]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[4]  Andries Petrus Engelbrecht,et al.  Locating multiple optima using particle swarm optimization , 2007, Appl. Math. Comput..

[5]  Tsung-Ying Sun,et al.  Cross-searching strategy for multi-objective particle swarm optimization , 2007, 2007 IEEE Congress on Evolutionary Computation.

[6]  Sanghamitra Bandyopadhyay,et al.  Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients , 2007, Inf. Sci..

[7]  Xiaodong Li,et al.  A Non-dominated Sorting Particle Swarm Optimizer for Multiobjective Optimization , 2003, GECCO.

[8]  Gary G. Yen,et al.  PSO-Based Multiobjective Optimization With Dynamic Population Size and Adaptive Local Archives , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[9]  Russell C. Eberhart,et al.  Comparison between Genetic Algorithms and Particle Swarm Optimization , 1998, Evolutionary Programming.

[10]  Kalyanmoy Deb,et al.  An Investigation of Niche and Species Formation in Genetic Function Optimization , 1989, ICGA.

[11]  Jason R. Schott.  Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization. , 1995 .

[12]  Carlos A. Coello Coello,et al.  MRMOGA: a new parallel multi‐objective evolutionary algorithm based on the use of multiple resolutions , 2007, Concurr. Comput. Pract. Exp..

[13]  Marco Laumanns,et al.  Scalable Test Problems for Evolutionary Multiobjective Optimization , 2005, Evolutionary Multiobjective Optimization.

[14]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[15]  Xiaodong Li,et al.  Using a distance metric to guide PSO algorithms for many-objective optimization , 2009, GECCO.

[16]  Manoj Kumar Tiwari,et al.  Multiobjective Particle Swarm Algorithm With Fuzzy Clustering for Electrical Power Dispatch , 2008, IEEE Transactions on Evolutionary Computation.

[17]  Gary B. Lamont,et al.  Multiobjective evolutionary algorithms: classifications, analyses, and new innovations , 1999 .

[18]  Kay Chen Tan,et al.  A Multiobjective Memetic Algorithm Based on Particle Swarm Optimization , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[19]  C. Coello,et al.  Improving PSO-based Multi-Objective Optimization using Crowding , Mutation and �-Dominance , 2005 .

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

[21]  Richard C. Chapman,et al.  Application of Particle Swarm to Multiobjective Optimization , 1999 .

[22]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[23]  Yujia Wang,et al.  Particle swarm optimization with preference order ranking for multi-objective optimization , 2009, Inf. Sci..

[24]  J. David Schaffer,et al.  Proceedings of the third international conference on Genetic algorithms , 1989 .

[25]  Carlos A. Coello Coello,et al.  Handling multiple objectives with particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[26]  M Reyes Sierra,et al.  Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art , 2006 .

[27]  Jürgen Teich,et al.  Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO) , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

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

[29]  Xiaodong Li,et al.  This article has been accepted for inclusion in a future issue. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 1 Locating and Tracking Multiple Dynamic Optima by a Particle Swarm Model Using Speciation , 2022 .