A Reference Vector based Many-Objective Evolutionary Algorithm with Feasibility-aware Adaptation

TL;DR

This paper introduces TEEA, a reference vector based evolutionary algorithm for many-objective optimization that adaptively guides the population towards the Pareto Front by maintaining and updating reference vectors and archives.

Contribution

It presents a novel adaptive reference vector mechanism that improves guidance and convergence in difficult many-objective problems.

Findings

TEEA achieves competitive performance on benchmark problems.

The adaptive reference vector strategy maintains even distribution within the Pareto Front.

Experimental results demonstrate the effectiveness of the proposed approach.

Abstract

The infeasible parts of the objective space in difficult many-objective optimization problems cause trouble for evolutionary algorithms. This paper proposes a reference vector based algorithm which uses two interacting engines to adapt the reference vectors and to evolve the population towards the true Pareto Front (PF) s.t. the reference vectors are always evenly distributed within the current PF to provide appropriate guidance for selection. The current PF is tracked by maintaining an archive of undominated individuals, and adaptation of reference vectors is conducted with the help of another archive that contains layers of reference vectors corresponding to different density. Experimental results show the expected characteristics and competitive performance of the proposed algorithm TEEA.