These days, hybrid nature-inspired population-based intelligent optimization methods are a wide range of the algorithms, which are used very often to solve real-world (industrial) optimizationproblems. As it was shown in [item 1) in the Appendix] by Slowik and Cpalka, nature-inspired methods can be divided into several groups of algorithms. In these groups of nature-inspired algorithms, we can find physics-based algorithms (gravitational search algorithm, harmony search algorithm, big bang-big crunch algorithm, etc.) and bio-inspired methods. In bio-inspired methods, we can find evolutionary algorithms (genetic algorithms, evolution strategies, genetic programming, etc.), swarm intelligence algorithms (particle swarm optimization algorithm, ant colony optimization algorithm, bat algorithm, etc.), immune algorithms (clonal selection algorithm, negative selection algorithm, etc.), and others (flower pollination algorithm, great Salomon run, Japanese tree frogs calling, etc.). These four selected groups of nature-inspired population-based optimization algorithm are commonly used in creating hybrid methods.

中文翻译：

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客座社论：基于自然启发的基于人口的工业应用智能优化的混合方法

如今，混合自然启发的基于人口的智能优化方法是范围广泛的算法，它们经常用于解决现实世界（工业）优化问题。正如Slowik和Cpalka在附录中的[第1项]中所示，自然启发的方法可以分为几组算法。在这几组受自然启发的算法中，我们可以找到基于物理的算法（引力搜索算法、和声搜索算法、big bang-big crunch 算法等）和仿生方法。在仿生方法中，我们可以找到进化算法（遗传算法、进化策略、遗传编程等）、群体智能算法（粒子群优化算法、蚁群优化算法、蝙蝠算法等）、免疫算法（克隆算法）。选择算法，负选择算法等），以及其他（花授粉算法、大所罗门运行、日本树蛙叫声等）。这四组受自然启发的基于种群的优化算法通常用于创建混合方法。