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A New Stochastic Optimization Approach — Dolphin Swarm Optimization Algorithm

Wang Yong

College of Information Science and Engineering, Guangxi University for Nationalities, Nanning 530006, P. R. China

Key Laboratory of Guangxi High Schools Complex System and Computational Intelligence, Nanning 530006, P. R. China

E-mail Address: wangygxnn@sina.com

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Wang Tao

College of Information Science and Engineering, Guangxi University for Nationalities, Nanning 530006, P. R. China

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Zhang Cheng-Zhi

College of Information Science and Engineering, Guangxi University for Nationalities, Nanning 530006, P. R. China

E-mail Address: zhchengzhi2007@163.com

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, and

Huang Hua-Juan

College of Information Science and Engineering, Guangxi University for Nationalities, Nanning 530006, P. R. China

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https://doi.org/10.1142/S1469026816500115Cited by:43 (Source: Crossref)

Abstract

A novel nature-inspired swarm intelligence (SI) optimization is proposed called dolphin swarm optimization algorithm (DSOA), which is based on mimicking the mechanism of dolphins in detecting, chasing after, and preying on swarms of sardines to perform optimization. In order to test the performance, the DSOA is evaluated against the corresponding results of three existing well-known SI optimization algorithms, namely, particle swarm optimization (PSO), bat algorithm (BA), and artificial bee colony (ABC), in the terms of the ability to find the global optimum of a range of the popular benchmark functions. The experimental results show that the proposed optimization seems superior to the other three algorithms, and the proposed algorithm has the performance of fast convergence rate, and high local optimal avoidance.

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