Novel bat optimization algorithm system

Abstract

The invention discloses a novel bat optimization algorithm system (Iterated local search and stochastic inertia weight bat algorithm system, ILSSIWBAS), comprising: a bat algorithm operation module, a disturbance module and an iterative local search module, a judgment global optimal solution module, a global optimal solution module, and a global optimal solution module. The optimal solution storage module; through the bat algorithm operation module, run the bat algorithm to obtain the local optimal solution; on the basis of the local optimal solution, through the disturbance module, add disturbance, according to the local iterative part iterative search module, use the iterative local search algorithm The global optimal solution of the population position is searched, and when the judgment conditions of the global optimal solution are met, the global optimal solution is obtained, and the global optimal solution is stored in the global optimal solution storage module. The system proposed by the invention mainly solves the problems that the existing bat optimization algorithm is easy to fall into local optimum and the optimization result is unstable, and improves the optimization accuracy of the optimization algorithm and the stability of the optimization result.

Description

technical field [0001] The invention relates to the field of intelligent optimization algorithms, in particular to a novel bat optimization algorithm system. Background technique [0002] The bat optimization algorithm is a heuristic optimization algorithm for searching the global optimal solution proposed by Professor Yang Xinshe in 2010 based on swarm intelligence. This algorithm is a global optimization algorithm developed according to the characteristics that bats use ultrasonic waves to detect prey and avoid obstacles in nature. It has the characteristics of less parameters to be adjusted, fast solution speed and high precision. At present, the bat optimization algorithm has been successfully used in modeling, optimization, control and other fields. [0003] Aiming at the problem that the bat optimization algorithm is easy to fall into local optimum, some scholars introduce the idea of ​​simulated annealing into the bat optimization algorithm, and perform Gaussian per...

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Problems solved by technology

[0004] However, the above-mentioned optimization algorithm uses the method of combining with other intelligent algorithms or limiting the pulse rate and loudness, so that its ability to jump out of the local optimum in the face of complex fun

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