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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| Veröffentlicht in: | Journal of computational design and engineering Jg. 6; H. 3; S. 243 - 259 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Oxford University Press
01.07.2019
한국CDE학회 |
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| ISSN: | 2288-5048, 2288-4300, 2288-5048 |
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| Abstract | 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. |
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| AbstractList | The whale optimization algorithm (WOA) is a new bio-inspired meta-heuristic algorithm which is pre-sented 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. KCI Citation Count: 52 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. The 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. |
| Author | Mostafa Bozorgi, Seyed Yazdani, Samaneh |
| Author_xml | – sequence: 1 givenname: Seyed surname: Mostafa Bozorgi fullname: Mostafa Bozorgi, Seyed email: s.m.bozorgi@iau-tnb.ac.ir organization: Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran – sequence: 2 givenname: Samaneh surname: Yazdani fullname: Yazdani, Samaneh email: s_yazdani@iau-tnb.ac.ir organization: Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran |
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| Keywords | Differential evolution Swarm intelligence Meta-heuristic algorithm Whale optimization algorithm Optimization |
| Language | English |
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Graphical Abstract
AbstractThe whale optimization algorithm (WOA) is a new bio-inspired meta-heuristic algorithm which is presented based on... The whale optimization algorithm (WOA) is a new bio-inspired meta-heuristic algorithm which is presented based on the social hunting behavior of humpback... The whale optimization algorithm (WOA) is a new bio-inspired meta-heuristic algorithm which is pre-sented based on the social hunting behavior of humpback... |
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| Title | IWOA: An improved whale optimization algorithm for optimization problems |
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