Artificial Physics Optimization algorithm with a feasibility-based rule for constrained optimization problems

Artificial Physics Optimization (APO) Algorithm is a novel population-based stochastic algorithm for solving the unconstrained global problems. This paper first presents a simple mechanism to handle constrained optimization problems with APO. A feasibility-based rule is employed, because this rule can guide the swarm quickly to the feasible region and need not additional penalty parameters. The mass formula is constructed based on this rule, the feasible and infeasible individuals´ mass are calculated with different mass formulas. The force direction of the individual is determined based on the feasibility-based rule. The simulation results and comparisons with other methods in the literature show the feasibility, effectiveness, and efficiency of the proposed APO algorithm.