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

Qing Zhang, Ruwang Jiao, Sanyou Zeng, Zhigao Zeng

OAI: oai:igi-global.com:273135 • DOI: 10.4018/IJCINI.20211001.oa25

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.

Cognitive Informatics Computer Science and Information Technology Cognitive Informatics