求解机组组合问题的改进型人工鱼群算法研究

提出了改进型人工鱼群算法。采用线性递减的函数取代标准人工鱼群算法（BAFSA）中的固定视野；在觅食行为中，利用粒子群算法（PSO）中的惯性权重线性递减的视野来加速算法的收敛速度；同时用混沌现象代替BAFSA中的随机现象。给出了算法的全局收敛性证明，并将算法应用于求解电力系统机组组合问题，分别对基准测试函数、三机组和十机组系统进行仿真计算，结果均表明新算法能有效跳出局部极值，收敛速度快且具有更高的精度。因此，改进型算法可以作为求解机组组合问题的有效算法。

Improved artificial fish swarm algorithm for combined allocation problem

An improved artificial fish swarm algorithm is proposed. The new algorithm uses the linear decreasing func-tion instead of a fixed visual, uses linear decreasing inertia weight as the Particle Swarm Optimization（PSO）to accelerate the convergence speed of the algorithm, and uses chaos phenomenon instead of random phenomena of BAFSA. It pres-ents the global convergence proof and carries on the simulation experiment with the test function and the systems of three units and ten units. The results show that the improved algorithm can escape from the local extremum effectively, and has higher convergence speed and precision. So it can be used as an effective algorithm for combined allocation problem.