Olurluğu arttırılmış parçacık sürü optimizasyonunda çok amaçlı ve tek amaçlı yaklaşımların kıyaslanması

Abstract

Highlights:Graphical/Tabular Abstract  Multi-objective and singleobjective feasibility enhanced particle swarm optimization  Pareto optimality based multi-objective optimization approach  Fixed weight linearly aggregated objective functions Figure A. Solutions obtained by the multi-objective approach provides design alternatives in the solution domain whereas the single objective approach gives a single solution for the given criteriaPurpose: Comparison of a Pareto based multi-objective feasibility enhanced particle swarm optimization approach with fixed weight linearly aggregated single-objective optimization using a variant of the same algorithm. Evaluation of the advantages presented by the multi-objective approach. Theory and Methods:Solutions for three separate multi-objective constrained problems were obtained using two different approaches: Pareto based multi-objective feasibility enhanced particle swarm optimization Singleobjective feasibility enhanced particle swarm optimization using fixed weight linearly aggregated singleobjective variants of the problems. Results: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 singleobjective criteria. Results obtained by a single multi-objective optimization run were found to be at least as good as results obtained by separate aggregated-objective runs for each aggregated criterion. Conclusion:In Pareto optimality based optimization algorithms a Pareto front consisting of many non-dominated solutions are found in each run instead of a single solution. Therefore: The designer can choose from these non-dominated solutions with a clear trade-off between known values of all objectives. This approach allows performing the trade-off without requiring rerunning the optimization with new objective weights. If efficient Pareto optimality based multi-objective optimization algorithms are available, it is unnecessary to employ single-objective approaches by combining the objectives with presumably biased weights assigned with insufficient information related with the problem.