Improved artificial fish school algorithm to solve traveling salesman problem
She Xiang-yang · Jisuanji yingyong yanjiu · 2010
After analyzing disadvantages of artificial fish school algorithm solving combinational optimization problems,put forward an improved artificial fish school algorithm. Facing the characteristic of TSP,the algorithm used random-keys expression to encode. In order to accelerate the optimizing speed of artificial fish,it directly simulated artificial fish behavior in the code space,which used the distance between the cities as heuristic information according to state transition probability and the roulette selection strategy for optimal decoding. Designed three update operators to improve the global search ability of artificial fish. At last,carried out comparation and analysis combining examples. Experimental results show that this algorithm can improve the speed of convergence efficiently and has much higher capacity of global optimization.