Comparison of multi-objective and single-objective approaches in feasibility enhanced particle swarm optimization

In this study, solutions for multi-objective constrained problems and their fixed weight linearly aggregated single-objective variants were obtained using the Pareto based multi-objective feasibility enhanced particle swarm optimization and single-objective approaches respectively. Comparisons involving three problems (two of which were highly constrained) revealed that optimizations performed using the multi-objective approach resulted in solutions that were also suitable for all single-objective criteria. With the multi-objective approach, objectives can be weighted after the optimization run and trade-offs can be performed without repeating the run.