Evolutionary algorithms with goal and priority information for multi-objective optimization

This paper presents a high performance multi-objective evolutionary algorithm with novel multiple-goal based Pareto cost assignment scheme that is capable of integrating any combination of goal and priority information. In addition, the algorithm is incorporated with a few advanced features for effective multi-objective optimization. These include the development of a dynamic sharing distance computation that is simple and adaptive to the on-line population distribution at each generation; an easy formation to deal with both soft and hard optimization constraints concurrently; a new way of convergence representation for multi-objective optimization based upon the concept of population domination; and a switching criteria preserved strategy to ensure stability and diversity of the multi-objective evolution. The effectiveness of the proposed algorithm is illustrated upon a benchmark optimization problem