Multi-objective differential evolution (MODE) is a powerful and efficient population-based stochastic search technique for solving multi-objective optimization problems in many scientific and engineering fields. However

premature convergence is the major drawback of MODE

especially when there are numerous local Pareto optimal solutions. To overcome this problem

we propose a MODE with a diversity enhancement (MODE-DE) mechanism to prevent the algorithm becoming trapped in a locally optimal Pareto front. The proposed algorithm combines the current population with a number of randomly generated parameter vectors to increase the diversity of the differential vectors and thereby the diversity of the newly generated offspring. The performance of the MODE-DE algorithm was evaluated on a set of 19 benchmark problem codes available from

http://www3.ntu.edu.sg/home/epnsugan/

http://www3.ntu.edu.sg/home/epnsugan/

. With the proposed method

the performances were either better than or equal to those of the MODE without the diversity enhancement.