基于DCC策略改进的多鱼群算法

本文针对人工鱼群算法在运行后期搜索的盲目性较大、寻优结果精度较低、运算速度慢的缺点，通过借鉴人类社会中普遍存在的分工、合作和竞争现象，提出一种具备分工、合作和竞争（简称DCC）策略的改进多鱼群算法。该算法策略通过设立多个人工鱼群，分别使用不同的搜索策略，进行算法集成，各人工鱼群之间既进行分工合作，同时又保持竞争关系，通过这种多关系共存的方式来改善算法求解性能，提高求解的质量和效率。通过对几种典型多峰函数的测试表明，该算法策略比其他几种算法能更有效、更精确地找出全局最优解，避免陷入局部最优解，而且该算法策略可通过自适应的演进策略来进一步提高求解的质量。

An Improved Multi-AFSA Based on the DCC Strategy

Due to the blindness searching in the final period,the Artificial Fish Swarm Algorithm(AFSA) can hardly obtain precision optima with high speed,according to the phenomenon of the division of labor,cooperation and competition that commonly found in society,this paper proposes a strategy characterized by the division of labor，cooperation and competition,which is designed by integrating several artificial fish with different algorithms. And among the fish,they keep the relationship of division,cooperation and competition,in which they achieve complementary advantages on improving the convergence property and accuracy. The test of application to some typical multimodal functions shows that this strategy has a better ability to locate the global solutions more efficiently and precisely，and to avoid falling into the locally optimal solutions. This strategy can also further improve the quality of solution by taking the adaptive strategy.