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http://dx.doi.org/10.3837/tiis.2022.09.008

Many-objective Evolutionary Algorithm with Knee point-based Reference Vector Adaptive Adjustment Strategy  

Zhu, Zhuanghua (Information Department, Shanxi Finance & Taxation College)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.16, no.9, 2022 , pp. 2976-2990 More about this Journal
Abstract
The adaptive adjustment of reference or weight vectors in decomposition-based methods has been a hot research topic in the evolutionary community over the past few years. Although various methods have been proposed regarding this issue, most of them aim to diversify solutions in the objective space to cover the true Pareto fronts as much as possible. Different from them, this paper proposes a knee point-based reference vector adaptive adjustment strategy to concurrently balance the convergence and diversity. To be specific, the knee point-based reference vector adaptive adjustment strategy firstly utilizes knee points to construct the adaptive reference vectors. After that, a new fitness function is defined mathematically. Then, this paper further designs a many-objective evolutionary algorithm with knee point-based reference vector adaptive adjustment strategy, where the mating operation and environmental selection are designed accordingly. The proposed method is extensively tested on the WFG test suite with 8, 10 and 12 objectives and MPDMP with state-of-the-art optimizers. Extensive experimental results demonstrate the superiority of the proposed method over state-of-the-art optimizers and the practicability of the proposed method in tackling practical many-objective optimization problems.
Keywords
Evolutionary algorithm; Artificial intelligence; Reference vector; Adaptive adjustment; Knee point;
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