IWOA: An improved whale optimization algorithm for optimization problems

Graphical abstract Graphical Abstract AbstractThe whale optimization algorithm (WOA) is a new bio-inspired meta-heuristic algorithm which is presented based on the social hunting behavior of humpback whales. WOA suffers premature convergence that causes it to trap in local optima. In order to overco...

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Vydané v:Journal of computational design and engineering Ročník 6; číslo 3; s. 243 - 259
Hlavní autori: Mostafa Bozorgi, Seyed, Yazdani, Samaneh
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford University Press 01.07.2019
한국CDE학회
Predmet:
기계공학
Differential evolution
Swarm intelligence
Meta-heuristic algorithm
Whale optimization algorithm
Optimization
ISSN:2288-5048, 2288-4300, 2288-5048
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Shrnutí:Graphical abstract Graphical Abstract AbstractThe whale optimization algorithm (WOA) is a new bio-inspired meta-heuristic algorithm which is presented based on the social hunting behavior of humpback whales. WOA suffers premature convergence that causes it to trap in local optima. In order to overcome this limitation of WOA, in this paper WOA is hybridized with differential evolution (DE) which has good exploration ability for function optimization problems. The proposed method is named Improved WOA (IWOA). The proposed method, combines exploitation of WOA with exploration of DE and therefore provides a promising candidate solution. In addition, IWOA+ is presented in this paper which is an extended form of IWOA. IWOA+ utilizes re-initialization and adaptive parameter which controls the whole search process to obtain better solutions. IWOA and IWOA+ are validated on a set of 25 benchmark functions, and they are compared with PSO, DE, BBO, DE/BBO, PSO/GSA, SCA, MFO and WOA. Furthermore, the effects of dimensionality and population size on the performance of our proposed algorithms are studied. The results demonstrate that IWOA and IWOA+ outperform the other algorithms in terms of quality of the final solution and convergence rate. Highlights The exploration ability of WOA is improved via hybridizing it with DE's mutation.A new adaptive strategy is utilized for balancing the exploration and exploitation abilities.Re-initialization is used to increase the diversity of population.Two improvements are presented for WOA through balancing its exploration and exploitation.The results show that the proposed algorithms can improve the performance of WOA significantly.
ISSN:2288-5048
2288-4300
2288-5048
DOI:10.1016/j.jcde.2019.02.002