Bat optimization algorithm based on iterated local search and stochastic inertia weight

Abstract

The invention discloses a bat optimization algorithm (Iterated local search and stochastic inertia weight bat algorithm, ILSSIWBA) based on iterative local search and random inertia weight. The main steps include: initializing parameters in the bat algorithm; updating pulse frequency, population position, And use the random weight to update the population speed; run the bat algorithm to get the optimal solution; on the basis of the optimal solution, add disturbance, use the iterative local search algorithm; judge whether it meets the judgment conditions of the global optimal solution? If satisfied, the global optimal solution is obtained, and the algorithm ends. The method 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 bat optimization algorithm based on iterative local search and random inertia weights. 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 op...

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Problems solved by technology

[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 perturbation on some individuals in the algorithm, which can increase the global convergence and precision of the algorithm; some scholars A hybrid Leaping Leaping Bat Algorithm with Gaussian variation is proposed to enhance the local search ability of the algorithm while maintaining the strong global search ability of the basic bat algorithm; Improvement methods such as changing the search direction, adding weights to the generation formula of the optimal solution, limiting the range of pulse rate and loudness, etc.

[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 functions needs to be further improved; The local optimal problem ignores that the algorithm optimization result is greatly affected by the initial value, and the optimization result is unstable, and the overall optimization effect is not ideal

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