Hybrid algorithm for global optimization based on periodic selection scheme in engineering computation

IF 1.5 4区 工程技术 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Ting Zhou, Yingjie Wei, Jian Niu, Yuxin Jie
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引用次数: 0

Abstract

Purpose

Metaheuristic algorithms based on biology, evolutionary theory and physical principles, have been widely developed for complex global optimization. This paper aims to present a new hybrid optimization algorithm that combines the characteristics of biogeography-based optimization (BBO), invasive weed optimization (IWO) and genetic algorithms (GAs).

Design/methodology/approach

The significant difference between the new algorithm and original optimizers is a periodic selection scheme for offspring. The selection criterion is a function of cyclic discharge and the fitness of populations. It differs from traditional optimization methods where the elite always gains advantages. With this method, fitter populations may still be rejected, while poorer ones might be likely retained. The selection scheme is applied to help escape from local optima and maintain solution diversity.

Findings

The efficiency of the proposed method is tested on 13 high-dimensional, nonlinear benchmark functions and a homogenous slope stability problem. The results of the benchmark function show that the new method performs well in terms of accuracy and solution diversity. The algorithm converges with a magnitude of 10-4, compared to 102 in BBO and 10-2 in IWO. In the slope stability problem, the safety factor acquired by the analogy of slope erosion (ASE) is closer to the recommended value.

Originality/value

This paper introduces a periodic selection strategy and constructs a hybrid optimizer, which enhances the global exploration capacity of metaheuristic algorithms.

工程计算中基于周期选择方案的全局优化混合算法
目的 基于生物学、进化论和物理原理的元智算法已被广泛用于复杂的全局优化。本文旨在提出一种新的混合优化算法,该算法结合了基于生物地理学的优化(BBO)、入侵杂草优化(IWO)和遗传算法(GAs)的特点。选择标准是循环排放和种群适合度的函数。它不同于传统的优化方法,在传统优化方法中,精英总是获得优势。采用这种方法,较好的种群仍有可能被剔除,而较差的种群则有可能被保留下来。研究结果在 13 个高维非线性基准函数和一个同质斜坡稳定性问题上测试了所提方法的效率。基准函数的结果表明,新方法在精度和解的多样性方面表现良好。算法的收敛幅度为 10-4,而 BBO 为 102,IWO 为 10-2。在边坡稳定性问题中,通过边坡侵蚀类比(ASE)获得的安全系数更接近推荐值。 原创性/价值 本文引入了一种周期性选择策略,并构建了一种混合优化器,增强了元搜索算法的全局探索能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Engineering Computations
Engineering Computations 工程技术-工程:综合
CiteScore
3.40
自引率
6.20%
发文量
61
审稿时长
5 months
期刊介绍: The journal presents its readers with broad coverage across all branches of engineering and science of the latest development and application of new solution algorithms, innovative numerical methods and/or solution techniques directed at the utilization of computational methods in engineering analysis, engineering design and practice. For more information visit: http://www.emeraldgrouppublishing.com/ec.htm
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