An Adaptive Parameter Tuning Strategy for Many-objective Evolutionary Algorithm

Wei Zheng, Jianyong Sun, Hui Li

Abstract: Decomposition-based multi-objective evolutionary algorithm has been acknowledged as a promising paradigm for multi-objective optimization problems. Nevertheless, its performance deteriorates seriously when the number of objectives increases. To improve its performance, generating high-quality solution is vital. Acknowledging the success of hybridizing different recombination operators, a selection strategy to choose from a set of differential evolution (DE) operators is adopted in this paper. The selection strategy could combine the advantages of these DE operators. Yet, the performance of DE operators depends highly on their control parameters, which should be tuned adaptively along the search process to fully explore their search abilities. An adaptive parameter tuning strategy is hereby proposed by estimating a Cauchy and a normal distribution from history information for the control parameters, respectively. Experimental comparison using DTLZ1-DTLZ4, with the number of objectives ranging from three to ten, is carried out between six state-of-the-art algorithms and the developed algorithm. Empirical results justify the outperformance of the developed algorithm against the compared algorithms in terms of some commonly-used performance metrics.