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针对复杂约束优化问题,提出一种改进的粒子群方法。该粒子群算法对于不满足约束条件的粒子实行全概率接收,但令其目标函数值同为一个很小的常数,以保持粒子的多样性并使最优解在可行域内。另外,在PSO算法的基础上,使惯性权值按对数规律单调递减,同时引进选择遗传算子,以增强其全局寻优性能。数值实验表明:与PSO算法和一些其它优化算法相比,改进算法具有较强的寻优能力和寻优效率。工程应用表明,改进算法具有一定的优越性。
Aiming at the problem of complex constrained optimization, an improved particle swarm optimization method is proposed. The particle swarm optimization algorithm performs full probability reception on particles that do not satisfy the constraints, but makes the objective function value a small constant so as to keep the particle diversity and make the optimal solution within the feasible domain. In addition, based on the PSO algorithm, the inertia weight decreases monotonously according to the logarithmic law, and the selection of genetic operators is introduced to enhance the global optimization performance. Numerical experiments show that compared with PSO algorithm and some other optimization algorithms, the improved algorithm has better ability of optimization and optimization. Engineering application shows that the improved algorithm has some advantages.