An Analysis of Archive Update for Vector Evaluated Particle Swarm Optimization

Yıl 2016, , 5 - 11, 31.03.2016

Faradila Naim , Ibrahim Zuwairie Lim Kian Sheng Mohd Falfazli Mat Jusof Nurul Wahidah Arshad

https://doi.org/10.18201/ijisae.48588

Öz

Multi-objective optimization problem is commonly found in many real world problems. In computational intelligence, Particle Swarm Optimization (PSO) algorithm is a popular method in solving optimization problems. An extended PSO algorithm called Vector Evaluated Particle Swarm Optimization (VEPSO) has been introduced to solve multi-objective optimization problems. VEPSO algorithm requires an archive, which is used to record the solutions found. However, the outcome may be differ depending on how the archive is used. Hence, in this study, the performance of VEPSO algorithm when updates the archive at different instance is investigated by measuring the convergence and diversity by using standard test functions. The results show that the VEPSO algorithm performs better when update the archive during the search process, in the iterations.

Anahtar Kelimeler

Multi-objective, Optimization, Particle Swarm Optimization, Vector-Evaluated, Archive

Kaynakça

• J. Kennedy and R. Eberhart. Particle swarm optimization. in Proceedings IEEE International Conference on Neural Networks. 1995, volume 4, pages 1942-1948.
• D. Besozzi, P. Cazzaniga, G. Mauri, D. Pescini, and L. Vanneschi, A Comparison of Genetic Algorithms and Particle Swarm Optimization for Parameter Estimation in Stochastic Biochemical Systems, in Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, C. Pizzuti, M. Ritchie, and M. Giacobini, Editors. 2009, Springer Berlin / Heidelberg. p. 116-127.
• R. Hassan, B. Cohanim, O. De Weck, and G. Venter. A Comparison Of Particle Swarm Optimization And The Genetic Algorithm. in AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. 2005, volume pages 1-13.
• X. Hu and R. Eberhart. Multiobjective optimization using dynamic neighborhood particle swarm optimization. in Congress on Evolutionary Computation (CEC 2002). 2002, volume 2, pages 1677-1681. IEEE Computer Society.
• C. A. Coello Coello and M. S. Lechuga. MOPSO: a proposal for multiple objective particle swarm optimization. in Congress on Evolutionary Computation (CEC 2002). 2002, volume 2, pages 1051-1056.
• G. T. Pulido and C. A. Coello Coello, Using Clustering Techniques to Improve the Performance of a Multi-objective Particle Swarm Optimizer, in Genetic and Evolutionary Computation. 2004, Springer Berlin / Heidelberg. p. 225-237.
• K. E. Parsopoulos and M. N. Vrahatis. Particle swarm optimization method in multiobjective problems. in Proceedings of the ACM symposium on Applied computing. 2002, volume pages 603-607. Madrid, Spain: ACM.
• J. D. Schaffer, Some experiments in machine learning using vector evaluated genetic algorithms (artificial intelligence, optimization, adaptation, pattern recognition), in Faculty Of Graduate School. 1984, Vanderbilt University: Nashville, Tennessee. p. 166.
• S. M. V. Rao and G. Jagadeesh, Vector Evaluated Particle Swarm Optimization of Supersonic Ejector for Hydrogen Fuel Cells. Journal of Fuel Cell Science and Technology, 2010. 7(4): p. 041014-7.
• D. Gies and Y. Rahmat-Samii. Vector evaluated particle swarm optimization: optimization of a radiometer array antenna. in IEEE Antennas and Propagation Society International Symposium. 2004, volume 3, pages 2297-2300.
• S. N. Omkar, D. Mudigere, G. N. Naik, and S. Gopalakrishnan, Vector evaluated particle swarm optimization (VEPSO) for multi-objective design optimization of composite structures. Computers and Structures, 2008. 86(1-2): p. 1-14.
• J. G. Vlachogiannis and K. Y. Lee, Review: Multi-objective based on parallel vector evaluated particle swarm optimization for optimal steady-state performance of power systems. Expert Systems with Applications, 2009. 36(8): p. 10802-10808.
• J. Grobler, Particle swarm optimization and differential evolution for multi objective multiple machine scheduling, in Department of Industrial and Systems Engineering 2009, University of Pretoria: Pretoria, South Africa. p. 159.
• D. A. Van Veldhuizen, Multiobjective evolutionary algorithms: classifications, analyses, and new innovations, in Faculty of the Graduate School of Engineering. 1999, Air Force Institute of Technology, Air University. p. 249.
• K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions On Evolutionary Computation, 2002. 6(2): p. 182-197.
• E. Zitzler and L. Thiele, Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Transactions On Evolutionary Computation, 1999. 3(4): p. 257-271.
• E. Zitzler, K. Deb, and L. Thiele, Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation, 2000. 8(2): p. 173-195.
• K. Deb, L. Thiele, M. Laumanns, and E. Zitzler, Scalable Test Problems for Evolutionary Multi-Objective Optimization, in KanGAL Report 2001001. 2001, Kanpur Genetic Algorithms Laboratory, Indian Institute of Technology Kanpur: Kanpur, India. p. 27.
• S. Huband, L. Barone, L. While, and P. Hingston, A Scalable Multi-objective Test Problem Toolkit, in Evolutionary Multi-Criterion Optimization, C.A. Coello Coello, A. Hernández Aguirre, and E. Zitzler, Editors. 2005, Springer Berlin / Heidelberg. p. 280-295.
• J. Kennedy, R. C. Eberhart, and Y. Shi, Swarm Intelligence. The Morgan Kaufmann Series in Evolutionary Computation, ed. D.B. Fogel. 2001, San Francisco: Morgan Kaufmann Publishers. 512.
• N. K. Khalid, Particle Swarm Optimization for Solving DNA Sequence Design Problem, in Faculty of Electrical Engineering. 2010, Universiti Teknologi Malaysia: Skudai, Johor Bahru. p. 148.
• M. A. Abido. Two-level of nondominated solutions approach to multiobjective particle swarm optimization. in Proceedings of the Conference on Genetic and Evolutionary Computation. 2007, volume pages 726-733. London, England: ACM.
• J. E. Alvarez-Benitez, R. M. Everson, and J. E. Fieldsend, A MOPSO Algorithm Based Exclusively on Pareto Dominance Concepts, in Evolutionary Multi-Criterion Optimization, C.A. Coello Coello, A. Hernández Aguirre, and E. Zitzler, Editors. 2005, Springer Berlin / Heidelberg. p. 459-473.
• J. E. Fieldsend and S. Singh. A multi-objective algorithm based upon particle swarm optimization, an efficient data structure and turbulence. in Workshop on Computational Intelligence. 2002, volume pages 37–44.
• S. Mostaghim and J. Teich. Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO). in IEEE Proceedings of the Swarm Intelligence Symposium (SIS 2003). 2003, volume pages 26-33.
• C. A. Coello Coello, G. T. Pulido, and M. S. Lechuga, Handling multiple objectives with particle swarm optimization. IEEE Transactions On Evolutionary Computation, 2004. 8(3): p. 256-279.
• X. Hu, R. C. Eberhart, and Y. Shi. Particle swarm with extended memory for multiobjective optimization. in IEEE Swarm Intelligence Symposium 2003. 2003, volume pages 193. Indianapolis, IN, USA: IEEE.
• S.-K. S. Fan and J.-M. Chang, A parallel particle swarm optimization algorithm for multi-objective optimization problems. Engineering Optimization, 2009. 41(7): p. 673 - 697.
• J. Durillo, J. García-Nieto, A. Nebro, C. A. Coello Coello, F. Luna, and E. Alba, Multi-Objective Particle Swarm Optimizers: An Experimental Comparison, in Evolutionary Multi-Criterion Optimization, M. Ehrgott, et al., Editors. 2009, Springer Berlin / Heidelberg. p. 495-509.
• S. Huband, P. Hingston, L. Barone, and L. While, A review of multiobjective test problems and a scalable test problem toolkit. IEEE Transactions On Evolutionary Computation, 2006. 10(5): p. 477-506.