W-dominance: Tradeoff-inspired dominance relation for preference-based evolutionary multi-objective optimization
Rafał Szłapczyński, Joanna Szłapczyńska
Abstract
The paper presents a method of incorporating decision maker preferences into multi-objective meta-heuristics. It is based on tradeoff coefficients and extends their applicability from bi-objective to multi-objective. The method assumes that a decision maker specifies a priori each objective's importance as a weight interval . Based on this, w-dominance relation is introduced, which extends Pareto dominance. By replacing reference points with weight intervals the method eliminates the need for any knowledge concerning expected solutions. Instead, decision maker reflects his context-independent policy regarding objectives. The proposed w-dominance was incorporated into selected multi-objective metaheuristics . Following this, three new metrics were designed. The metrics include prescreening true Pareto Front and final population according to w-dominance relation. Based on preliminary tests, Vector Angle Evolutionary Algorithm (VaEA) was selected as the best match for w-dominance. W-dominance-extended VAEA (wVAEA) was compared in a series of simulations with four state-of-the-art reference point-based multi-objective algorithms. The results show that wVaEA outperforms the four representative algorithms for selected benchmark problems.