Dynamic Multi-Objectives Optimization with a Changing Number of Objectives

Research output: Contribution to journalArticlepeer-review

Authors

Colleges, School and Institutes

External organisations

  • University of Exeter

Abstract

Existing studies on dynamic multi-objective optimization focus on problems with time-dependent objective functions, while the ones with a changing number of objectives have rarely been considered in the literature. Instead of changing the shape or position of the Paretooptimal front/set when having time-dependent objective functions, increasing or decreasing the number of objectives usually leads to the expansion or contraction of the dimension of the Paretooptimal front/set manifold. Unfortunately, most existing dynamic handling techniques can hardly be adapted to this type of dynamics. In this paper, we report our attempt toward tackling the dynamic multi-objective optimization problems with a changing number of objectives. We implement a dynamic two-archive evolutionary algorithm which maintains two co-evolving populations simultaneously. In particular, these two populations are complementary to each other: one concerns more about the convergence while the other concerns more about the diversity. The compositions of these two populations are adaptively reconstructed once the environment changes. In addition, these two populations interact with each other via a mating selection mechanism. Comprehensive experiments are conducted on various benchmark problems with a time-dependent number of objectives. Empirical results fully demonstrate the effectiveness of our proposed algorithm.

Details

Original languageEnglish
Number of pages33
JournalIEEE Transactions on Evolutionary Computation
Early online date24 Mar 2017
Publication statusE-pub ahead of print - 24 Mar 2017

Keywords

  • changing objectives, decomposition-based method, Multi-objective optimization, dynamic optimization, evolutionary algorithms