A Modified Particle Swarm Optimization with Elite Archive for Typical Multi-Objective Problems
- 8 Downloads
The solution to multi-objective optimization problems with conflicting objectives is a Pareto-optimal solution set. It is well known that the critical work in multi-objective particle swarm optimization (MOPSO) is to find the global best guides for each particle in order to obtain satisfied Pareto fronts with high diversity. In this paper, a modified version of MOPSO is proposed, where dense and sparse distance are adopted to determine the global best guides, and Pareto archive with size limit is used to store the non-dominated solutions. In addition, a random number is used to judge whether the crowding distance considered or not, and the inertia weight decreases linearly to improve the speed of convergence and avoid precocity. The proposed approach is applied to several well-known benchmark functions, and the experimental results show that the diversity of swarm and distribution of Pareto fronts are well satisfied.
KeywordsMulti-objective optimization Particle swarm optimization External archive Convergence Dense distance Sparse distance
The research was supported by the Fundamental Research Funds for the Central Universities (2018MS076, 2015MS128) and the Hebei Province Natural Science Fund Program (F2014502081).
- Coello CA, Lechuga MS (2002) A proposal for multiple objective particle swarm optimization. In: IEEE proceedings, world congress on computational intelligence (CEC2002), pp 1051–1056Google Scholar
- Coello CAC, Lechuga MS (2002) MOPSO: a proposal for multiple objective particle swarm optimization. In: Proceedings of the 2002 congress on evolutionary computationGoogle Scholar
- Eberhart HA (2002) Multiobjective optimization using dynamic neighborhood particle swarm optimization. In: IEEE proceedings, world congress on computational intelligence (CEC2002)Google Scholar
- Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human scienceGoogle Scholar
- Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE international conference on neural networks Perth, Australia. IEEE, PiscatawayGoogle Scholar
- Ren D, Cai Y, Huang H (2018) Genetic learning particle swarm optimization with diverse selection. In: 14th International Conference on Intelligent Computing, ICIC 2018, August 15, 2018–August 18, 2018, Wuhan, China, SpringerGoogle Scholar
- Schaffer JD (1985) Multiple objective optimization with vector evaluated genetic algorithms. In: Proceedings of the 1st international conference on genetic algorithms. Associates Inc., HillsdaleGoogle Scholar
- Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: 1998 IEEE international conference on evolutionary computation proceedings. IEEE World Congress on Computational Intelligence (Cat. No. 98TH8360)Google Scholar