Two-stage adaptive weight vector design method for decomposition based many-objective evolutionary algorithm Online publication date: Fri, 18-Feb-2022
by Xiaofang Guo; Yuping Wang; Xiaozhi Gao
International Journal of Ad Hoc and Ubiquitous Computing (IJAHUC), Vol. 39, No. 1/2, 2022
Abstract: In order to acquire the searching information of the irregular shape of the PF accurately, this paper proposes a two-stage adaptive weight vector design approach for decomposition based many objective evolutionary algorithms. Firstly, a preset initial weight vector generation strategy based on the crowding information is proposed to capture the effective area of the distribution of population. Next, the self-organised mapping (SOM) weight vector design method is adopted to generate weight vectors to capture the topological structure of the distribution of PF. In addition, a new composite aggregation function combined Lp metric distance with angle-distance is proposed to improve both convergence and diversity. The performance of our algorithm is examined using a total of 10 problems with both regular and irregular PF. The experimental results show that the proposed method can significantly improve the convergence and diversity performance with irregular shape of PF.
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