Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 189, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2021.116118
Keywords
Many-objective optimization problems; Many-objective particle swarm optimization; Initialization mechanism; Competitive strategy
Categories
Funding
- National Natural Science Foundation of China [18XGL010]
- Natural Science Foundation of Shanxi Province [2020JC-44]
Ask authors/readers for more resources
An improved competitive particle swarm optimization algorithm is proposed in this paper, which achieves optimization through multi-stage initialization and competitive learning strategy, showing promising performance in balancing convergence and diversity.
Multi-objective particle swarm optimization (MOPSO) has been widely applied to solve multi-objective optimization problems (MOPs), due to its efficient implementation and fast convergence. However, most MOPSOs are ineffective in achieving the balance between convergence and diversity in the high-dimensional objective space. In this paper, an improved competitive particle swarm optimization is proposed for solving manyobjective optimization problems. To improve the quality of the first generation population, a decision variable dividing-based multi-step initialization mechanism is presented, decision variables are divided into two groups and optimized individually. Moreover, an improved competitive learning strategy is suggested as the main part to further optimization, where particles are updated via leader information from winner particles with well convergence and diversity. The performance of the proposed algorithm is verified by benchmark comparisons with several state-of-the-art evolutionary algorithms. Experimental results demonstrate the promising performance of the algorithm in terms of balance convergence and diversity.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available