Hypervolume Performance of Conical Area Evolutionary Algorithm for Bi-Objective Optimization

Abstract:

The conical area evolutionary algorithm (CAEA) can efficiently solve the bi-objective optimization problems by borrowing some ideas from decomposition and hypervolume. In this paper, the optimal hypervolume performance of the CAEA with an infinite number of sub-problems is proved through the squeeze theorem for limits. Experimental results on several bi-objective optimization problems have shown that not only CAEA performs much better than NSGA-II and MOEA/D in terms of efficiency, but also CAEA with a larger number of sub-problems has the better hypervolume performance.

• Periodical: Applied Mechanics and Materials (Volumes 513-517)
• Pages: 2215-2219
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.513-517.2215
• Online since: February 2014
• Authors: Hong Ke Zhao, Wei Qin Ying*, Yu Wu, Yue Hong Xie, Li Wen
• Keywords: Bi-Objective Optimization, Decomposition, Evolutionary Algorithm (EA), Hypervolume

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