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Balancing Exploration and Exploitation With Decomposition-Based Dynamic Multi-Objective Evolutionary Algorithm

Balancing Exploration and Exploitation With Decomposition-Based Dynamic Multi-Objective Evolutionary Algorithm

Qing Zhang, Ruwang Jiao, Sanyou Zeng, Zhigao Zeng
Copyright: © 2021 |Volume: 15 |Issue: 4 |Pages: 23
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781799859857|DOI: 10.4018/IJCINI.20211001.oa25
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MLA

Zhang, Qing, et al. "Balancing Exploration and Exploitation With Decomposition-Based Dynamic Multi-Objective Evolutionary Algorithm." IJCINI vol.15, no.4 2021: pp.1-23. http://doi.org/10.4018/IJCINI.20211001.oa25

APA

Zhang, Q., Jiao, R., Zeng, S., & Zeng, Z. (2021). Balancing Exploration and Exploitation With Decomposition-Based Dynamic Multi-Objective Evolutionary Algorithm. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 15(4), 1-23. http://doi.org/10.4018/IJCINI.20211001.oa25

Chicago

Zhang, Qing, et al. "Balancing Exploration and Exploitation With Decomposition-Based Dynamic Multi-Objective Evolutionary Algorithm," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 15, no.4: 1-23. http://doi.org/10.4018/IJCINI.20211001.oa25

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Abstract

Balancing exploration and exploitation is a crucial issue in evolutionary global optimization. This paper proposes a decomposition-based dynamic multi-objective evolutionary algorithm for addressing global optimization problems. In the proposed method, the niche count function is regarded as a helper objective to maintain the population diversity, which converts a global optimization problem to a multi-objective optimization problem (MOP). The niche-count value is controlled by the niche radius. In the early stage of evolution, a large niche radius promotes the population diversity for better exploration; in the later stage of evolution, a niche radius close to 0 focuses on convergence for better exploitation. Through the whole evolution process, the niche radius is dynamically decreased from a large value to zero, which can provide a sound balance between the exploration and exploitation. Experimental results on CEC 2014 benchmark problems reveal that the proposed algorithm is capable of offering high-quality solutions, in comparison with four state-of-the-art algorithms.