Integrating Preferred Weights with Decomposition Based Multi-objective Evolutionary Algorithm

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.