基于布谷鸟迭代更新策略的多重优化算法

doi:10.3778/j.issn.1002-8331.2105-0480

摘要/Abstract

摘要： 针对布谷鸟算法在寻优过程中存在的收敛速度慢和局部探索能力弱的缺点，基于布谷鸟迭代更新策略提出了一种新型的多重优化算法。通过引入Skew tent映射初始化种群，增强种群的多样性。受粒子群算法启发，引入社会学习和自我学习来提高种群的信息交流能力，并采用对数自适应参数平衡来提高全局搜索能力和局部开发能力。借鉴灰狼算法的狩猎机制，引导种群中其他个体进行变异，以进一步提高算法收敛速度。采用8个标准测试函数对算法性能进行了仿真实验，实验结果表明，改进算法的总体性能明显优异于对比算法。

关键词: 布谷鸟算法, 灰狼算法, tent映射, 对数自适应

Abstract:

Aiming at the shortcomings of slow convergence speed and weak local exploration ability in the optimization process of cuckoo search algorithm, this paper proposes a new multiple optimization algorithm based on cuckoo iterative updating strategy. The improved algorithm introduces Skew tent mapping to initialize the population and enhance the diversity of the population. Inspired by the particle swarm optimization algorithm, the social learning and self-learning are introduced to improve the information communication ability of the population, and the logarithmic adaptive parameter balance is introduced to improve the global search ability and the local development ability. Learning from the hunting mechanism of the gray wolf optimization algorithm, other individuals in the population are guided to mutate to further improve the convergence speed　of algorithm. This paper uses 8 standard test functions to simulate the performance of the algorithm. The experiment shows that the overall performance of the improved algorithm is significantly better than the comparison algorithm.

Key words: cuckoo algorithm, gray wolf algorithm, tent mapping, logarithmic adaptive

丁希成, 王红军. 基于布谷鸟迭代更新策略的多重优化算法[J]. 计算机工程与应用, 2022, 58(9): 67-73.

DING Xicheng, WANG Hongjun. Multiple Optimization Algorithm Based on Cuckoo Iterative Updating Strategy[J]. Computer Engineering and Applications, 2022, 58(9): 67-73.

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