Integrating Preferred Weights with Decomposition Based Multi-objective Evolutionary Algorithm
Zhenhua Li, Hai‐Lin Liu · 2014
Incorporating decision maker's preference with multi objective optimization problems keeps an active research area. In this paper, we suggest an algorithm to integrate decision maker's preference into multiobjective evolutionary optimization algorithm based on decomposition technique (MOEA/D). Decomposition techniques require a set of evenly distributed weight vectors to generate a diverse set of solutions on the Pareto front. This newly proposed algorithm incorporates preferred weights generated by desirability functions. A set of evenly distributed weights in desirability space are mapped into objective space to represent decision maker's preference. The solutions corresponding to these preferred weights consist preferred population. Further, a second population associated with evenly distributed weights in objective space is utilized to boost the search for promising areas and present to the decision maker a global perspective of view. Experimental results show the algorithm could be able to find a set of trade-offs on the Pareto front.