A Hybrid PSO-DE Intelligent Algorithm for Solving Constrained Optimization Problems Based on Feasibility Rules

In this paper, we study swarm intelligence computation for constrained optimization problems and propose a new hybrid PSO-DE algorithm based on feasibility rules. Establishing individual feasibility rules as a way to determine whether the position of an individual satisfies the constraint or violates the degree of the constraint, which will determine the choice of the individual optimal position and the global optimal position in the particle population. First, particle swarm optimization (PSO) is used to act on the top 50% of individuals with higher degree of constraint violation to update their velocity and position. Second, Differential Evolution (DE) is applied to act on the individual optimal position of each individual to form a new population. The current individual optimal position and the global optimal position are updated using the feasibility rules, thus forming a hybrid PSO-DE intelligent algorithm. Analyzing the convergence and complexity of PSO-DE. Finally, the performance of the PSO-DE algorithm is tested with 12 benchmark functions of constrained optimization and 57 engineering optimization problems, the numerical results show that the proposed algorithm has good accuracy, effectiveness and robustness.

Automated summary

In this paper, a new hybrid PSO-DE algorithm based on feasibility rules is proposed for constrained optimization problems. The algorithm uses individual feasibility rules to determine the optimal positions in the particle population. PSO is used to update the velocity and position of individuals with higher degree of constraint violation, while DE is applied to update the individual optimal position. The algorithm is evaluated using benchmark functions and engineering optimization problems, showing good accuracy, effectiveness, and robustness.