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Computational Properties of Hybrid Methods with PSO and DE
Author(s) -
Muranaka Kenichi,
Aiyoshi Eitaro
Publication year - 2014
Publication title -
electronics and communications in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.131
H-Index - 13
eISSN - 1942-9541
pISSN - 1942-9533
DOI - 10.1002/ecj.11527
Subject(s) - particle swarm optimization , differential evolution , global optimization , mathematical optimization , computer science , heuristic , hybrid algorithm (constraint satisfaction) , multi swarm optimization , component (thermodynamics) , mathematics , physics , stochastic programming , constraint programming , thermodynamics , constraint logic programming
SUMMARY In this paper, we present a new type of hybrid methods for global optimization with particle swarm optimization (PSO) and differential evolution (DE), which have been attracting interest as heuristic and global optimization methods. Concretely, “p‐best solutions” as the targets of PSO's particles are actuated by DE's evolutional mechanism in order to promote PSO's global searching ability. The presented hybrid method works effectively because PSO acts as a local optimizer and DE plays a role as a global optimizer. To evaluate performance of the hybridization, our method is applied to some benchmarks and is compared with the separated PSO and DE. Through computer simulations, it is demonstrated that the proposed hybrid method performs rather better than the component algorithms separately.