Name
Abstract | ||
|---|---|---|
This paper proposes a new fuzzy particle swarm optimization (NFPSO), which is based on a new defined population position and velocity diversity measure. The proposed NFPSO utilizes population diversity information to adjust the inertia weight and membership function of fuzzy variable charisma adaptively, aiming to improve the performance of fuzzy PSO algorithms for numerical optimization problems. Experiments compared the proposed NFPSO with standard PSO, fuzzy PSO, ARPSO, DE and ABC algorithms were conducted on a collection of 25 numerical benchmark problems provided by the IEEE Congress on Evolutionary Computation 2005 special session on real parameter optimization. The results and its statistical analysis show that the proposed NFPSO algorithm performed well when applied in the multi-modal numerical problems. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.3233/IFS-151577 | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS |
Keywords | Field | DocType |
Particle swami optimization,population diversity,fuzzy variable,parameter adaption,multi-modal | Mathematical optimization,Fuzzy particle swarm optimization,Multi-swarm optimization,Population diversity,Artificial intelligence,Mathematics,Machine learning,Metaheuristic | Journal |
Volume | Issue | ISSN |
29 | 1 | 1064-1246 |
Citations | PageRank | References |
4 | 0.49 | 15 |
Authors | ||
2 | ||