Self-adaptive bat algorithm based on velocity inertia coefficient

Abstract

The invention relates to the technical field of intelligent algorithms, in particular to a self-adaptive bat algorithm based on a velocity inertia coefficient. Comprising the following steps: step 1, initializing population parameters, setting the number of initial populations as n, setting the initial position of each bat as xi (i = 1, 2... n), setting the initial speed as vi, setting the individual frequency of the bat as fi, setting the pulse rate as ri, and setting the loudness as Ai; 2, all the bat individuals in the population generate a group of new solutions (namely, the new position where each bat individual is located) according to a position updating equation by adjusting the pulse frequency, and a global optimal solution is selected from the new solutions to serve as a reference target of other bat individuals. According to the self-adaptive bat algorithm based on the velocity inertia coefficient, when the individual adaptive value is different from the group average adaptive value, global high-speed search can be kept, and falling into a local optimal solution is avoided; when the individual fitness value is superior to the group average fitness value, the global search speed is reduced through the speed inertia coefficient, the optimization efficiency is ensured, and the algorithm convergence speed is increased.

Description

technical field [0001] The invention relates to the technical field of intelligent algorithms, in particular to an adaptive bat algorithm based on a speed inertia coefficient. Background technique [0002] The Bat Algorithm (BA) is a heuristic global search algorithm based on swarm intelligence. The algorithm is an iterative-based optimization method, which initializes a set of random solutions, and then searches for the optimal solution globally through a given logic iterative calculation, and randomly searches around the individual optimal solution to generate a new local optimal solution, which strengthens the local search. Compared with other algorithms, the bat algorithm has better accuracy and effectiveness, and there are not too many parameters to adjust. [0003] Usually, when the heuristic intelligent optimization algorithm is calculated in the later stage, there will be shortcomings such as slower convergence speed and weakened search ability. In order to ensure ...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the technical problems existing in the prior art, the present invention provides an adaptive bat algorithm based on the speed inertia coefficient to solve the problem that the above-mentioned heuristic intelligent algorithm is calculated in the later stage, and the convergence speed slows down.

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