Differential evolution strategies for multi-objective optimization

Multi-objective optimization (MOO) using evolutionary algorithms has gained popularity in the recent past due to its ability of producing number of solutions in a single run and handling multiple objectives simultaneously. In this effort, several MOO algorithms are developed. In this manuscript several strategies of multi-objective differential evolution algorithm (namely, MODE-I, MODE-III, elitist MODE and hybrid MODE) are briefly discussed. Three important unconstrained test problems are considered for validating the performance (in terms of Pareto front and convergence & diversity metrics) of strategies of MODE algorithm with other popular algorithms from literature. It is observed that the strategies of MODE algorithm are in general able to produce Pareto front with good convergence to the true Pareto front.