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Hybridized Particle Swarm Optimization for Constrained Problems
Keywords:
particle swarm optimization, NP hardness, constrained optimization, engi- neering problems, swarm intelligence.
Abstract:
This paper presents hybridized implementation of the well-known particle swarm optimization algorithm that belongs to the family of swarm intel- ligence metaheuristics. The proposed approach was adapted for tackling constrained optimization problems. With the basic goals to enhance the converge of the algorithm and to improve the exploitation – exploration tradeoff, the mechanism that replaces exhausted solutions from the popula- tion with randomly generated solutions from the search domain was adopted from the artificial bee colony approach. Proposed metaheuristic was tested on standard constrained engineering benchmark, and comparative analysis with other state-of-the-art algorithms was conducted. Empirical results ob- tained from practical simulations proved that the hybridized particle swarm optimization for constrained problems is able to successfully tackle this type of NP hard challenges.
CITATION:
IEEE format
N. Bačanin Džakula, I. Štrumberger, E. Tuba, M. Tuba, “Hybridized Particle Swarm Optimization for Constrained Problems,” in Sinteza 2019 - International Scientific Conference on Information Technology and Data Related Research, Belgrade, Singidunum University, Serbia, 2019, pp. 17-25. doi:10.15308/Sinteza-2019-17-25
APA format
Bačanin Džakula, N., Štrumberger, I., Tuba, E., Tuba, M. (2019). Hybridized Particle Swarm Optimization for Constrained Problems. Paper presented at Sinteza 2019 - International Scientific Conference on Information Technology and Data Related Research. doi:10.15308/Sinteza-2019-17-25
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