Research of Improved Whale Optimization Algorithm Based on Variable Convergence Factor and Forced Global Search

Research of Improved Whale Optimization Algorithm Based on Variable Convergence Factor and Forced Global Search

Optimization Algorithm is new meta-heuristic optimization algorithm which mimicking the hunting behavior of humpback whales. It enjoys the advantages of simple principle, less parameters, remarkable search ability and good global convergence, also suffer many defects, such as slow convergence speed, low convergence precision and easy to fall into the local optimum. This paper analyzes the problems of original WOA, making use of Good- Point Set to generate initial population. Through variable convergence factor, the progress of search is more flexible and pertinent. At the same time, mechanism of forced global search makes the ability of jumping out local optimum is promoted in substance. Results and convergence curves of benchmark functions indicate that exploration, exploitation, local optima avoidance of algorithm in this paper are competitive with original WOA.