What weights work for you? adapting weights for any pareto front shape in decomposition-based evolutionary multiobjective optimisation

Research output: Contribution to journalArticle

Colleges, School and Institutes

External organisations

  • Southern University of Science and Technology, Shenzhen, China

Abstract

The quality of solution sets generated by decomposition-based evolutionary multiobjective optimisation (EMO) algorithms depends heavily on the consistency between a given problem’s Pareto front shape and the specified weights’ distribution. A set of weights distributed uniformly in a simplex often lead to a set of well-distributed solutions on a Pareto front with a simplex-like shape, but may fail on other Pareto front shapes. It is an open problem on how to specify a set of appropriate weights without the information of the problem’s Pareto front beforehand. In this paper, we propose an approach to adapt weights during the evolutionary process (called AdaW). AdaW progressively seeks a suitable distribution of weights for the given problem by elaborating several key parts in weight adaptation — weight generation, weight addition, weight deletion, and weight update frequency. Experimental results have shown the effectiveness of the proposed approach. AdaW works well for Pareto fronts with very different shapes: 1) the simplex-like, 2) the inverted simplex-like, 3) the
highly nonlinear, 4) the disconnect, 5) the degenerate, 6) the scaled, and 7) the highdimensional.

Details

Original languageEnglish
Number of pages26
JournalEvolutionary Computation
Publication statusAccepted/In press - 4 Dec 2018

Keywords

  • Multi-objective optimisation, many-objective optimisation, evolutionary algorithms, decomposition-based EMO, weight adaptation