IWOA: An improved whale optimization algorithm for optimization problems

Abstract

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.

Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm

“…The WOA was created by Mirjalili and Lewis in 2016 [22] and shortly after many modified and improved WOA versions emerged [23][24][25]. Some WOA adaptations for resource scheduling in cloud computing can be found in the literature survey [26].…”

“…The improved WOA (IWOA), that adopts a differential evolution (DE) mutation operator and utilizes adaptive strategy for balancing between exploitation and exploration was proposed in [25] and proved to be more efficient an approach than the original WOA. Another improved WOA version that is based on different searching paths and perceptual disturbance was proposed and tested on 23 standard unconstrained benchmarks and obtained better result quality than the original version [84].…”

Serial Manipulator Time-Jerk Optimal Trajectory Planning Based on Hybrid IWOA-PSO Algorithm

“…As a new intelligent optimization algorithm, the whale optimization algorithm (WOA) was proposed in 2016 [20]. Studies show that WOA has superior characteristics such as simple principle, few parameters, and easy implementation [27][28]. Accordingly, WOA has been successfully applied in diverse engineering optimization areas.…”

Lamarckian learning‐based whale optimization algorithm to localize non‐line‐of‐sight nodes for achieving reliable data dissemination in vehicular ad‐hoc networks

“…If the current best solution determined by a search agent is a local optimum solution, then the probability of the search agents to fall into the local optimum solution during the process of exploration is high. This limitation mainly occurs, since the position updating process of the remaining search agents completely concentrates on the current estimated local optimum point 28 . This proposed localization scheme, thereby utilizes a search agents' individual development strategy‐based capability evaluation method based on the merits of Lamark's Evolutionary Theory.…”