Improved Whale Optimization Algorithm via the Inertia Weight Method Based on the Cosine Function

Abstract

Whale Optimization Algorithm (WOA) is a new meta-heuristic algorithm proposed by Australian scholar Mirjalili Seyedali in 2016 based on the feeding behavior of whales in the ocean. In response to the disadvantages of this algorithm, such as low solution accuracy, slow convergence speed and easy to fall into local optimum, an improved Whale Optimization Algorithm (IWOA) is proposed in this paper. We introduce chaotic mapping in the initialization of the algorithm to keep the whale population with diversity; introduce adaptive inertia weights in the spiral position update of humpback whales to prevent the algorithm from falling into local optimum; and introduce Levy flight in the random search for food of humpback whales to improve the global search ability of the algorithm. In the simulation experiments, we compare the algorithm of this paper with other metaheuristic algorithms in seven classical benchmark test functions, and the numerical results of four indexes, minimum, maximum, mean and standard deviation, in different dimensions, illustrate that the algorithm of this paper has better performance results.