解约束优化问题的新粒子群算法

提出了一种新的求解约束优化问题的粒子群算法。基于一个合理的假设前提:任何可行解总是比非可行解好,算法通过在标准粒子群算法中引入了一个新的约束处理机制,将约束优化问题转化为无约束问题来求解。此外,为了提高收敛性能,新构建的算法通过引入变异策略,使算法在迭代过程中保持较高的种群多样性,增强算法跳出局部最优解的概率,从而提高算法的收敛速度和解的质量。与遗传算法以及标准粒子群算法的实验比较表明,所提出的方法是一个可行的约束优化问题的求解算法。

A New Particle Swarm Optimization for Solving Constrained Optimization Problems

This paper proposes a new particle swarm optimization(PSO) for solving the constrained optimization problems.Based upon an acceptable assumption that any feasible solution is better than any infeasible solution,a new mechanism for constraints handling is incorporated in the standard PSO to transform the constrained optimization problem into an unconstrained optimization problem.In addition to the mechanism of constraints handling,a mutation strategy to increase population diversity is added to the proposed algorithm,which can enhance the probability of leading the particle swarm escape from local optimums,and then improve the convergence speed and solution quality.The experimental results compared with genetic algorithm and a standard PSO show that the proposed algorithm is a feasible algorithm for solving constrained optimization problems.