Performance Analysis of Whale Optimization Algorithm
Through the research and analysis of a relatively novel natural heuristic, meta-heuristic swarm intelligence optimization algorithm, this swarm intelligence algorithm is defined as a whale optimization algorithm. The algorithm builds a mathematical model by simulating a social behavior of humpback whales. This optimization algorithm was inspired by the bubble-like net hunting phenomenon that humpback whales prey on. By analyzing the four benchmark optimization problems with or without offset and rotation, the convergence performance of the whale optimization algorithm and the ability to solve the optimization problem are proved. The performance of the whale optimization algorithm is based on the computer simulation technology. Through the convergence curve obtained from the experiment, we can see that the whale optimization algorithm performs best for the five benchmark optimization problems without rotation.
KeywordsSwarm intelligence Whale optimization algorithm Convergence Numerical optimization
This research was supported in part by the National Natural Science Foundation of China (Project No. 61601329, 61603275), the Tianjin Higher Education Creative Team Funds Program, the Applied Basic Research Program of Tianjin (Project No. 15JCYBJC51500, 15JCYBJC52300), and the Doctoral Fund Project of Tianjin Normal University (Project No. 043-135202XB1602).
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