CAJ | 학술논문

针对基本人工鱼群算法的参数视野固定不变导致算法后期收敛速度慢、运算量大、易陷入局部最优等问题，提出自适应视野的改进人工鱼群算法。改进后的算法只对人工鱼的觅食行为的视野进行调整，使其随着算法的迭代次数的增加而逐渐减小，但当视野小于初始值的一半时，停止减小，使其等于初始值的一半。将提出的改进型人工鱼群算法应用到求解基于道路网络的最短路径问题中，并通过实验证明了改进后的人工鱼群算法比基本人工鱼群算法及蚁群优化算法收敛速度快、计算量小，而且更加准确和稳定。
침대기본인공어군산법적삼수시야고정불변도치산법후기수렴속도만、운산량대、역함입국부최우등문제，제출자괄응시야적개진인공어군산법。개진후적산법지대인공어적멱식행위적시야진행조정，사기수착산법적질대차수적증가이축점감소，단당시야소우초시치적일반시，정지감소，사기등우초시치적일반。장제출적개진형인공어군산법응용도구해기우도로망락적최단로경문제중，병통과실험증명료개진후적인공어군산법비기본인공어군산법급의군우화산법수렴속도쾌、계산량소，이차경가준학화은정。
To solve basic artificial fish-swarm algorithm(AFSA)’s drawbacks of low convergence rate in the latter stage, a large amount of computation and easiness of trapping in local optimal solution, caused by the constant vision of the ar-tificial fish, an improved artificial fish-swarm algorithm based on adaptive vision(AVAFSA) was proposed. The improved algorithm only adjusted the vision of the preying behavior of artificial fish to make the vision gradually decrease with the increase of the number of iterations of the algorithm. When the value became less than half the initial value, it made the value be equal to half the initial value. The proposed improved artificial fish swarm algorithm was applied to the static shortest path problem based on road network to provide customers with the best path. Simulation results depict the im-proved algorithm has higher convergence rate, a smaller amount of calculation, and is more accurate and stable than the basic AFSA and ant colony optimization(ACO).