Hybrid cuckoo search algorithm

Abstract

The present invention discloses a hybrid cuckoo search algorithm. Two different one-dimensional update strategies are introduced into a cuckoo search algorithm. Further, by using the long jump characteristic of Levy distribution, the correct selection between Levy flight random walk and the one-dimensional update strategies is realized by setting a limit value. Thus, the hybrid cuckoo search algorithm not only overcomes inter-dimensional interference and is improved in local search ability, but also increases a probability of jumping out of local optimum, thereby achieving a balance between exploration and development. The hybrid cuckoo search algorithm can be effectively improved in convergence speed and convergence precision, reduces time complexity and have good convergence performancefor solving a function optimization problem.

Description

technical field [0001] The invention belongs to the technical field of function optimization, in particular to a hybrid cuckoo search algorithm. Background technique [0002] In the past 20 years, optimization has become an important theoretical tool in solving complex problems. The main purpose of optimization algorithm design is to solve the maximum or minimum value of these problems. In order to effectively solve these optimization problems, researchers have proposed many optimization algorithms, such as particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), and teaching and learning algorithm (TLBO). In general, these algorithms can be divided into two broad categories: deterministic and stochastic. As a class of stochastic methods, metaheuristic algorithms are usually inspired by some natural mechanisms and have been widely used in solving optimization problems. Compared with traditional gradient methods, the search results of meta-he...

AI Technical Summary

Problems solved by technology

Although the random long jump is beneficial to improve the exploration ability of the algorithm, it may cause the local spatial information not to be fully utilized

In addition, related research has also proved that the solution space cannot be effectively explored only by using occasional long jumps, especially when solving complex multimodal optimization problems.

Images