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将黏菌算法与模拟退火算法相结合：一种用于数值和工程设计问题的混合统计方法。

Hybridizing slime mould algorithm with simulated annealing algorithm: a hybridized statistical approach for numerical and engineering design problems.

作者信息

Ch Leela Kumari, Kamboj Vikram Kumar, Bath S K

机构信息

Domain of Power Systems, School of Electronics and Electrical Engineering, Lovely Professional University, Punjab, India.

Department of Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, Canada.

出版信息

Complex Intell Systems. 2023;9(2):1525-1582. doi: 10.1007/s40747-022-00852-0. Epub 2022 Sep 21.

DOI:10.1007/s40747-022-00852-0
PMID:36160761
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9490722/
Abstract

The existing slime mould algorithm clones the uniqueness of the phase of oscillation of slime mould conduct and exhibits slow convergence in local search space due to poor exploitation phase. This research work exhibits to discover the best solution for objective function by commingling slime mould algorithm and simulated annealing algorithm for better variation of parameters and named as hybridized slime mould algorithm-simulated annealing algorithm. The simulated annealing algorithm improves and accelerates the effectiveness of slime mould technique as well as assists to take off from the local optimum. To corroborate the worth and usefulness of the introduced strategy, nonconvex, nonlinear, and typical engineering design difficulties were analyzed for standard benchmarks and interdisciplinary engineering design concerns. The proposed technique version is used to evaluate six, five, five unimodal, multimodal and fixed-dimension benchmark functions, respectively, also including 11 kinds of interdisciplinary engineering design difficulties. The technique's outcomes were compared to the results of other on-hand optimization methods, and the experimental results show that the suggested approach outperforms the other optimization techniques.

摘要

现有的黏菌算法模仿了黏菌行为振荡阶段的独特性，并且由于开发阶段较差，在局部搜索空间中收敛速度较慢。这项研究工作旨在通过将黏菌算法与模拟退火算法相结合，以实现更好的参数变化，从而找到目标函数的最佳解决方案，该算法被命名为混合黏菌算法 - 模拟退火算法。模拟退火算法提高并加速了黏菌算法的有效性，同时有助于摆脱局部最优解。为了证实所提出策略的价值和实用性，针对标准基准测试以及跨学科工程设计问题，分析了非凸、非线性和典型的工程设计难题。所提出的技术版本分别用于评估六个、五个、五个单峰、多峰和固定维度的基准函数，还包括11种跨学科工程设计难题。将该技术的结果与其他现有优化方法的结果进行了比较，实验结果表明，所提出的方法优于其他优化技术。

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