关键词: 果蝇优化算法, 参数修正, 逐维扰动, 混沌扰动, 收敛判断因子

Abstract: Basic FOA（Fruit Fly Optimization Algorithm） often derives a local extreme and the basic FOA cannot solve optimization problem when there exists negative number in the domain because smell concentration judge value S is ?non-negative. In order to solve these problems, multiple improved strategies are proposed to improve basic FOA. The calculating formula of smell concentration judge value is modified to solve the problem that S cannot be negative number in basic FOA. In order to solve high-dimensional inter-perturbation, dimension by dimension perturbation strategy is used in update stage. Convergence judge threshold is used in the iteration process. The algorithm derives a local extreme if optimal value is not better after many time iterations. And then, some excellent flies optimize near the optimal fly and other flies find the optimum solution with chaotic disturbance in the solution domain. Because the optimization performance of algorithm is influenced by the convergence judge threshold, Experiments are done with various thresholds and the premium threshold is determined according to experiment results. Through simulation experiment on test functions, the proposed algorithm has higher searching precision and faster convergence speed than basic FOA.

Key words: Fruit Fly Optimization Algorithm（FOA）, parameter modification, dimension by dimension perturbation, chaotic disturbance, convergence judge threshold