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.

中文翻译：

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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启发式算法已被广泛开发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，结合了基于生物地理学的优化（BBO）、侵入性杂草优化（IWO）和遗传算法（GA）的特点。

设计/方法论/途径

新算法与原始优化器之间的显着区别在于后代的周期性选择方案。选择标准是循环排放和种群适应性的函数。它不同于传统的优化方法，精英总是获得优势。使用这种方法，更健康的群体仍然可能被拒绝，而更贫穷的群体可能会被保留。选择方案用于帮助摆脱局部最优并保持解决方案的多样性。

发现

该方法的效率在 13 个高维非线性基准函数和齐次边坡稳定性问题上进行了测试。基准函数的结果表明，新方法在精度和解多样性方面表现良好。该算法的收敛幅度为 10-4，而 BBO 中的收敛幅度为 102，IWO 中的收敛幅度为 10-2。在边坡稳定性问题中，类比边坡侵蚀（ASE）得到的安全系数更接近推荐值。

原创性/价值

本文引入了周期性选择策略并构造了混合优化器，增强了元启发式算法的全局探索能力。