Many optimization problems in engineering and industrial design applications can be formulated as optimization problems with highly nonlinear objectives, subject to multiple complex constraints. Solving such optimization problems requires sophisticated algorithms and optimization techniques. A major trend in recent years is the use of nature-inspired metaheustic algorithms (NIMA). Despite the popularity of nature-inspired metaheuristic algorithms, there are still some challenging issues and open problems to be resolved. Two main issues related to current NIMAs are: there are over 540 algorithms in the literature, and there is no unified framework to understand the search mechanisms of different algorithms. Therefore, this paper attempts to analyse some similarities and differences among different algorithms and then presents a generalized evolutionary metaheuristic (GEM) in an attempt to unify some of the existing algorithms. After a brief discussion of some insights into nature-inspired algorithms and some open problems, we propose a generalized evolutionary metaheuristic algorithm to unify more than 20 different algorithms so as to understand their main steps and search mechanisms. We then test the unified GEM using 15 test benchmarks to validate its performance. Finally, further research topics are briefly discussed.

翻译：工程与工业设计应用中的许多优化问题可表述为具有高度非线性目标、且受多个复杂约束条件限制的优化问题。求解此类优化问题需要复杂的算法与优化技术。近年来的一大趋势是使用受自然启发的元启发式算法（NIMA）。尽管受自然启发的元启发式算法广受欢迎，但仍存在一些亟待解决的挑战性问题与开放难题。当前NIMA面临的两个主要问题是：文献中已存在超过540种算法，且缺乏统一框架来理解不同算法的搜索机制。因此，本文尝试分析不同算法间的异同，进而提出一种广义进化元启发式（GEM）算法，以期统一部分现有算法。在简要探讨受自然启发算法的若干见解及部分开放问题后，我们提出一种广义进化元启发式算法，用以统一20多种不同算法，从而理解其核心步骤与搜索机制。随后，我们使用15个测试基准对统一的GEM算法进行性能验证测试。最后，对进一步的研究方向进行了简要讨论。