关键词: 三级领导式, 快速自适应狼群算法, 优化函数

Abstract: In order to improve the convergence speed of wolf swarm algorithm, an adaptive wolf swarm algorithm called three-level leadership and particle evolution equation is proposed, man-made grey wolves are divided into two categories：the leadership grey wolves, such as alpha, beta and delta, and the rest are the fierce wolf. In the wandering search stage, a prey position is set, and the distance between wolves and prey is used to guide the wandering search. In the calling stage, three best gray wolf particles are used to guide the wolves to approach the prey, avoiding the traditional situation that only one wolf is easy to fall into local optimum. In the siege stage, inertia factor is used to express the past experience of running. The product of learning factor and random number represents the knowledge and summary of the experience of the wolves themselves, and the iteration influence factor represents the knowledge and adjustment of the experience of the wolves as a whole. The speed of attack of wolf swarm particles accelerate the convergence speed and jump out of the local optimum, so as to find the real global optimum. The comparative experimental results of the eight test functions selected in this paper show that the method finds the optimal value of the test function more accurately, and converges to the optimal value earlier and faster, and to the real optimal value smoothly in the later period. The algorithm is applicable to multi-dimension and multi-peak function extremum problem with unknown search space.

Key words: three-level leadership, fast adaptive wolf swarm algorithm, optimization function