A Systematic and Meta-Analysis Survey of Whale Optimization Algorithm.

Hardi M. Mohammed,Shahla U. Umar,Tarik A. Rashid

doi:10.1155/2019/8718571

Abstract

The whale optimization algorithm (WOA) is a nature-inspired metaheuristic optimization algorithm, which was proposed by Mirjalili and Lewis in 2016. This algorithm has shown its ability to solve many problems. Comprehensive surveys have been conducted about some other nature-inspired algorithms, such as ABC and PSO. Nonetheless, no survey search work has been conducted on WOA. Therefore, in this paper, a systematic and meta-analysis survey of WOA is conducted to help researchers to use it in different areas or hybridize it with other common algorithms. Thus, WOA is presented in depth in terms of algorithmic backgrounds, its characteristics, limitations, modifications, hybridizations, and applications. Next, WOA performances are presented to solve different problems. Then, the statistical results of WOA modifications and hybridizations are established and compared with the most common optimization algorithms and WOA. The survey's results indicate that WOA performs better than other common algorithms in terms of convergence speed and balancing between exploration and exploitation. WOA modifications and hybridizations also perform well compared to WOA. In addition, our investigation paves a way to present a new technique by hybridizing both WOA and BAT algorithms. The BAT algorithm is used for the exploration phase, whereas the WOA algorithm is used for the exploitation phase. Finally, statistical results obtained from WOA-BAT are very competitive and better than WOA in 16 benchmarks functions. WOA-BAT also outperforms well in 13 functions from CEC2005 and 7 functions from CEC2019.

Highlights

Optimization becomes one of the most interesting issues in different life aspects, such as engineering designs, browsing the Internet, and business management
MOWOA satisfied the Multiobjective method for vehicle traveling based on whale optimization algorithm (WOA) (MOWOA)
Implementation and Results e proposed algorithm WOA-BAT is implemented and evaluated by using different benchmark functions. ree different benchmark functions are used to test the proposed algorithm; these are 23 mathematical optimization problems (Table 2 and [5]), CEC2005 (Table 7), and CEC2019 (Table 8). e code of WOA-BAT is available at the following link: https://github.com/Hardi-Mohammed/WOA-BATmodification

Summary

Introduction

Optimization becomes one of the most interesting issues in different life aspects, such as engineering designs, browsing the Internet, and business management. Many effective search algorithms, which are using mathematical formulae and computational simulations, have been implemented to solve optimization problems. Metaheuristic algorithms try to balance between randomization and local search. Most of these algorithms are used for global optimization [1, 2]. Metaheuristic algorithms have two basic elements, which are exploitation and exploration; in exploration, different solutions are found to explore the search space to find the global optimal, but in exploitation, local search is used by exploiting information about the best solutions that have been recently found. Is combination with choosing the best solutions will guarantee that solutions reach the optimality, exploration bypasses the local optima problem through randomization and raises the diversity of the solutions [1, 3] Metaheuristic algorithms have two basic elements, which are exploitation and exploration; in exploration, different solutions are found to explore the search space to find the global optimal, but in exploitation, local search is used by exploiting information about the best solutions that have been recently found. is combination with choosing the best solutions will guarantee that solutions reach the optimality, exploration bypasses the local optima problem through randomization and raises the diversity of the solutions [1, 3]

Methods

Results

Conclusion