Feasibility-Guided Constraint-Handling Techniques for Engineering Optimization Problems

The particle swarm optimization (PSO) algorithm is an established nature-inspired population-based meta-heuristic that replicates the synchronizing movements of birds and fish. PSO essentially unconstrained requires constraint handling techniques (CHTs) to solve constrained problems (COPs). For this purpose, we integrate two CHTs, superiority feasibility (SF) violation constraint-handling (VCH), with a PSO. These CHTs distinguish feasible solutions from infeasible ones. Moreover, in SF, selection based on their degree violations, whereas VCH, number violations by solution more importance. Therefore, adapted for optimization, yielding variants, denoted SF-PSO VCH-PSO. Both VCH-PSO are evaluated respect five engineering problems: Himmelblau’s nonlinear welded beam design, spring pressure vessel three-bar truss design. simulation results show both algorithms consistent terms these problems, including different available versions. Comparison other existing tested shows proposed have lower computational cost function evaluations used. We also report our disagreement some unjust comparisons made researchers regarding variants.