Vector evaluated particle swarm optimization: The archive's influence on performance

Multi-objective optimization (MOO) algorithms often use external archives to keep track of the Pareto-optimal solutions. Vector evaluated particle swarm optimization (VEPSO) is one such algorithm. In contrast to other MOO algorithms, VEPSO does not clearly define how to implement the archive. In this paper, the performance of various archive implementations, as found throughout the literature, are evaluated using the well-known Inverted Generational Distance (IGD) measure. A new archive implementation based on the hypersurface contribution is proposed and evaluated. The results show that overall the well-known crowding distance archive outperformed all other archive implementations. The hypersurface contribution archive also showed promise. Finally, it is shown that the distance metric and nearest neighbor archives perform worse than even the random archive.

[1]  P. Hingston,et al.  A Scalable Multi-objective Test Problem Toolkit ( corrected version : 22 June 2005 ) , 2005 .

[2]  M.N. Vrahatis,et al.  Particle swarm optimizers for Pareto optimization with enhanced archiving techniques , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[3]  M. N. Vrahatis,et al.  Particle swarm optimization method in multiobjective problems , 2002, SAC '02.

[4]  Andries Petrus Engelbrecht,et al.  CIlib: A collaborative framework for Computational Intelligence algorithms - Part II , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

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

[6]  Marc Parizeau,et al.  Revisiting the NSGA-II crowding-distance computation , 2013, GECCO '13.

[7]  Andries Petrus Engelbrecht,et al.  Knowledge Transfer Strategies for Vector Evaluated Particle Swarm Optimization , 2013, EMO.

[8]  Kalyanmoy Deb,et al.  A Fast and Effective Method for Pruning of Non-dominated Solutions in Many-Objective Problems , 2006, PPSN.

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

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

[11]  Marco Laumanns,et al.  Archiving With Guaranteed Convergence And Diversity In Multi-objective Optimization , 2002, GECCO.

[12]  R. Eberhart,et al.  Comparing inertia weights and constriction factors in particle swarm optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[13]  Michael N. Vrahatis,et al.  Recent approaches to global optimization problems through Particle Swarm Optimization , 2002, Natural Computing.

[14]  Antonio J. Nebro,et al.  Redesigning the jMetal Multi-Objective Optimization Framework , 2015, GECCO.

[15]  Carlos A. Coello Coello,et al.  A Study of the Parallelization of a Coevolutionary Multi-objective Evolutionary Algorithm , 2004, MICAI.

[16]  Jacomine Grobler,et al.  Particle swarm optimization and differential evolution for multi-objective multiple machine scheduling , 2009 .

[17]  Kalyanmoy Deb,et al.  A Computationally Efficient Evolutionary Algorithm for Real-Parameter Optimization , 2002, Evolutionary Computation.

[18]  Zhihua Cai,et al.  Enhance the Convergence and Diversity for epsilon-MOPSO by Uniform Design and Minimum Reduce Hypervolume , 2009, 2009 International Conference on Artificial Intelligence and Computational Intelligence.

[19]  Eckart Zitzler,et al.  Evolutionary algorithms for multiobjective optimization: methods and applications , 1999 .

[20]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[21]  Carlos A. Coello Coello,et al.  MOPSOhv: A new hypervolume-based multi-objective particle swarm optimizer , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

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

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

[24]  C.A. Coello Coello,et al.  MOPSO: a proposal for multiple objective particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

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

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

[27]  Andries Petrus Engelbrecht,et al.  A scalability study of multi-objective particle swarm optimizers , 2013, 2013 IEEE Congress on Evolutionary Computation.

[28]  Dimitris K. Tasoulis,et al.  Vector evaluated differential evolution for multiobjective optimization , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[29]  Eckart Zitzler,et al.  HypE: An Algorithm for Fast Hypervolume-Based Many-Objective Optimization , 2011, Evolutionary Computation.

[30]  Andries Petrus Engelbrecht,et al.  CIlib: A collaborative framework for Computational Intelligence algorithms - Part I , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[31]  Kalyanmoy Deb,et al.  AMGA: an archive-based micro genetic algorithm for multi-objective optimization , 2008, GECCO '08.

[32]  Konstantinos E. Parsopoulos,et al.  MULTIOBJECTIVE OPTIMIZATION USING PARALLEL VECTOR EVALUATED PARTICLE SWARM OPTIMIZATION , 2003 .