DP-ADPSO算法在机组负荷优化组合分配问题中的应用

Application of Dynamic Programming-Adaptive Discrete Particle Swarm Optimization Algorithm in Power Plant Units Load Commitment Dispatch Problem

针对离散粒子群应用于机组负荷优化问题中存在早熟收敛的难题,提出了动态规划-自适应离散粒子群算法求解机组负荷优化组合问题。该方法首先保证所有随机生成的粒子均为满足基本约束条件的可行解,使整个算法只在可行解区域内进行动态优化搜索,缩短了计算时间。计算实例表明:动态规划-自适应离散粒子群算法能较好地收敛到最优解,而且该方法得出的解具有精度高、收敛速度快的优点,应用效果优于动态规划法和离散粒子群算法,说明该方法是有效的、合理的,具有较好的应用前景。

To solve the optimization premature convergence problem of power plant units load commitment by using the binary particle swarm optimization algorithm,Dynamic programming-adaptive discrete particle swarm optimization algorithm has been designed for unit load optimization combination problem.A new strategy of this method for particles generation was dynamically searched for which could firstly make all the random particles feasible and narrow the search space within the feasible solutions with shorter computing time.The results of calculation examples showed that the presented algorithm could be significant for converging to optimal solution,and that the solution of the presented algorithm possessed high precision and the high convergence speed.This method was valid and reasonable with good application prospects in engineering application,whose effect was better than dynamic programming and discrete particle swarm optimization.