Implementation of Genetic algorithm Whale Optimization Algorithm (WOA) in MATLAB

Introduction—

MATLABSolutions demonstrate In this task we are going to design The whale optimization algorithm (WOA) was proposed by Mirjalili and Lewis in 2016. It is a new swarm intelligence optimization algorithm that simulates humpback whale hunting behavior. The main idea of the algorithm is to solve the target problem by imitating the whale’s predatory behavior. Since its introduction, the WOA has been favored by many scholars, and it has been widely used in optimal allocation of water resources , optimal control , and feature selection. But as a swarm intelligence optimization algorithm, like DE, PSO, ACO, and other algorithms, they all have the shortcomings of slow convergence and easy to fall into local optimum. *erefore, in practical applications, various improvements have been made to the standard algorithms, such as. Terefore, for the WOA algorithm, in recent years, many scholars have made a lot of improvements in improving algorithm convergence speed and optimization accuracy. For example, Abdel-Basset et al. used Levy flight and logical chaos ´ mapping to replace and determine the coefficient vector C and switching probability P in the WOA, proposed an improved whale optimization algorithm (IWOA), and verified the effectiveness of the proposed algorithm through experiments.

Inspiration of the algorithm:

Whale optimization algorithm (WOA): A nature inspired meta-heuristic optimization algorithm which mimics the hunting behaviour of humpback whales. The algorithm is inspired by the bubble-net hunting strategy

Foraging behavior of Humpback whales is called bubble-net feeding method. Humpback whales prefer to hunt school of krill or small fishes close to the surface. It has been observed that this foraging is done by creating distinctive bubbles along a circle or ‘9’-shaped path as shown in Video