基于约束优化问题的人工鱼群算法及其改进

Artificial Fish Swarm Algorithm and Its Improvement Based on Constrained Optimization Problem

在人工鱼群算法基础上,对人工鱼群算法进行改进,结合遗传算法提出的适应度函数来解决约束优化问题.具体表现在改进了人工鱼的觅食行为,另外引入了吞噬行为以便加快收敛速度,得到更优的适应度值.仿真结果表明改进的人工鱼群算法在解决约束优化问题时,具有收敛速度快、适应度值优、全局寻优性能强等优点.改进的人工鱼群算法较之基本人工鱼群算法具有更好的性能.

This thesis resolves the constrained optimization problem by means of combination of the fitness function proposed by genetic algorithm on the basis of artificial swarm algorithm and its improvement. It is specifically embodied in the improvement of the foraging behavior of the artificial fish. In addition, phagocytosis behavior is introduced so as to accelerate the rate of convergence for a more optimal fitness value. The simulation result shows that the improved artificial fish swarm algorithm has such advantages as fast convergence rate, optimal fitness rate and strong global optimization performance in the solution to constrained optimization problem. The improved artificial fish swarm algorithm is equipped with better performance than the basic artificial fish swarm algorithm.