Solving Constrained Multi-objective Optimization Problems Using Non-dominated Ranked Genetic Algorithm

A criticism of evolutionary algorithms might be the lack of efficient and robust generic methods to handle constraints. The most widespread approach for constrained search problems is to use penalty methods, because of their simplicity and ease of implementation. Nonetheless, the most difficult aspect of the penalty function approach is to find an appropriate penalty parameters. In this paper, a method combining the new non-dominated ranked genetic algorithm (NRGA), with a parameterless penalty approach are exploited to devise the search to find Pareto optimal set of solutions. The new parameterless penalty and the nondominated ranked genetic algorithm (PP-NRGA) continuously find better Pareto optimal set of solutions. This new algorithm have been evaluated by solving four test problems, reported in the multi-objective evolutionary algorithm (MOEA) literature. Performance comparisons based on quantitative metrics for accuracy, coverage, and spread are presented.