Abstract
Multi-objective evolutionary algorithms (EMOA) commonly approximate the true Pareto Front (PF) in a strong manner and have been widely utilized to many-objective problems (MaOPs) of science and engineering. However, as the number of objectives increases gradually, the selection pressure of population towards true PF will reduce, resulting in high searching complexity and low solution quality. This literature proposes an efficient EMOA using high abundant historical information and evolutionary phase self-adjusting (EPSEMOA-HBHEI) to overcome the shortcoming of optimizing problems with huge objectives. Inspired by SMS-EMOA and HypE, the proposed algorithm introduces an adaptive u + k evolutionary framework, where k elimination mechanism adopts different offspring combinations accompanied by the variation of evolutionary phase. Furthermore, two types of hypervolume approximation are employed to extract the abundant elite information from distinct history stages and both of them are stored into guided evolution set and external archive for offering more fully guidance of generating offspring. Five state-of-the-art MOEAs are used to compare with our algorithm on multiple types of problems about DTLZ. The experimental result reflects that our EPSEMOA-HBHEI performs significantly well than other comparing MOEAs, as well as dominates most of testing problems in DTLZ.
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Yang, L., Zhu, B.J., Luo, X.w., Liang, S.M. (2022). An Efficient Multi-objective Algorithm with High Abundant Elite Information and Evolutionary Phase Self-adjusting for Many Objective Optimization. In: Li, K., Liu, Y., Wang, W. (eds) Exploration of Novel Intelligent Optimization Algorithms. ISICA 2021. Communications in Computer and Information Science, vol 1590. Springer, Singapore. https://doi.org/10.1007/978-981-19-4109-2_3
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DOI: https://doi.org/10.1007/978-981-19-4109-2_3
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