Vector evaluated particle swarm optimization archive management: Pareto optimal front diversity sensitivity analysis

Vector evaluated particle swarm optimization (VEPSO) extends the particle swarm optimization (PSO) algorithm to deal with multi-objective optimization problems (MOPs). VEPSO stores the found non-dominated solutions in an archive. Management of the VEPSO archive is, however, not described in detail. In this paper, a Pareto optimal front (POF) diversity sensitivity analysis on the choice of the VEPSO's archive size and deletion approach is presented. The results indicate that the well-known spread, solution distribution, maximum spread, and spacing metrics are all sensitive to the choice of the archive size and deletion approach. It is concluded that care must be taken when selecting the archive size and deletion approach as the impact on the diversity of the POF is notable.

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

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

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

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

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

[6]  Andries Petrus Engelbrecht,et al.  Analysis of stagnation behavior of vector evaluated particle swarm optimization , 2013, 2013 IEEE Symposium on Swarm Intelligence (SIS).

[7]  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).

[8]  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..

[9]  David W. Corne,et al.  Properties of an adaptive archiving algorithm for storing nondominated vectors , 2003, IEEE Trans. Evol. Comput..

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

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

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

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

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

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

[16]  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).

[17]  Yaochu Jin,et al.  Dynamic Weighted Aggregation for evolutionary multi-objective optimization: why does it work and how? , 2001 .

[18]  Tong Heng Lee,et al.  Multiobjective Evolutionary Algorithms and Applications , 2005, Advanced Information and Knowledge Processing.

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

[20]  Gary B. Lamont,et al.  Considerations in engineering parallel multiobjective evolutionary algorithms , 2003, IEEE Trans. Evol. Comput..

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

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

[23]  Kay Chen Tan,et al.  An Investigation on Noisy Environments in Evolutionary Multiobjective Optimization , 2007, IEEE Transactions on Evolutionary Computation.

[24]  Enrique Alba,et al.  SMPSO: A new PSO-based metaheuristic for multi-objective optimization , 2009, 2009 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making(MCDM).

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

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

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

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

[29]  Andries Petrus Engelbrecht,et al.  Archive management for dynamic multi-objective optimisation problems using vector evaluated particle swarm optimisation , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).