求解经济负荷分配的自适应混沌粒子群算法

针对电力系统经济负荷分配(economic load dispatch,ELD)这一典型的非凸、非线性的多约束优化问题,提出一种自适应混沌粒子群算法(self adaptive chaotic particle swarm optimization,SACPSO).在混沌粒子群算法(CPSO)的基础上,先利用引入变异算子和社会因子的粒子群算法进行全局搜索,再对搜索得到的先验解进行基于Tent映射的混沌细搜索(CLS),并将逆映射回的决策变量和全局最优粒子的线性组合作为CLS的搜索结果输出.通过6机组、15机组电力系统的仿真,验证了该算法的有效性.

A Self Adaptive Chaotic Particle Swarm Optimization Algorithm Applied in Economic Load Dispatch

A self adaptive chaotic particle swarm optimization( SACPSO) algorithm was presented on the basis of economic load dispatch,which is of a problem of non-convex,nonlinear and complex constrained. Based on the chaotic particle swarm optimization( CPSO),a global search made by the basic particle swarm optimization( PSO) which introduced mutation operator and social factor was not stopped until premature happened,then the optimal solutions of global search were used as initial population of chaotic local search( CLS) with Tent map. A linear combination of the decision variable returned by inverse mapping and the best particle of population was output as CLS's results. In the end,the effectiveness of the new algorithm was verified by the system simulations of 6 and 15 units.