一种求解非线性规划问题的混合粒子群优化算法

粒子群优化算法(PSO)与其他演化算法相似,也是基于群体的·每一个粒子被随机初始化以表示一个可能的解,并在解空间追随最优的粒子进行搜索·提出一种基于改进的混合粒子群优化算法求解非线性约束规划方法·在介绍PSO算法基本原理的基础上,设计了约束适应度优先排序处理约束条件的方法,并通过动态邻域算子和可变惯性权重进行联合演化以求得全局最优解·对非线性规划例子的实例计算表明,该算法稳定性好,简单容易实现而又功能强大,易于掌握,对于多维非线性、复杂问题的求解具有普遍适用性·

Application of Particle Swarm Optimization to Nonlinear Constrained Programming

?Particle swarm optimization (PSO) is an optimal technique based on population, which is the same to other evolutionary computations. It is initialized with a population of random solutions and searches for optima by updating generations. Particle swarm optimization has become the hotspot of evolutionary computation because of its excellent performance and simple implementation. Introducing the basic principle of the PSO,a particle swarm optimization algorithm with embedded priority ranking of constraint fitness is proposed to solve nonlinear programming problem,for which the fitness function and constraints-handling procedure are designed. The proposed PSO can co-evolution with dynamic neighborhood and the weight value of variable inertia to find the global optimum. The results of this preliminary investigation are quite promising and show that this algorithm is reliable and applicable to almost all of problems in multiple-dimensions, nonlinear,complex constrained programming. The solutions to some constrained nonlinear programming problems as example also show its high validity, robustness and efficiency.