Abstract：A new multi-objective optimization algorithm based on marginal distribution estimation is proposed, in which marginal probability distribution of the selected better individuals is estimated and is used to guide the search of Pareto optimal solutions of the multi-objective optimization problems. Combined with non-dominant ranking, diversity preserving technique based on crowding mechanism, tournament selection based on non-dominant ranking, and elitist strategy, the algorithm achieves a good balance between convergence and diversity. A set of typical test functions are used to evaluate the performance of the proposed algorithm, and comparison is made between some well-known multi-objective optimization algorithms, i.e. NSGA-II, SPEA, PAES. The experimental results show that the proposed algorithm can achieve a good balance between convergence and diversity, and is suited to complex multi-objective problems.
2007, Vol. 29 
