Nature-inspired optimization algorithms: Challenges and open problems
Nature-inspired optimization algorithms: Challenges and open problems
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DOI：
10.1016/j.jocs.2020.101104
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发表时间：
2020-10-01
影响因子：
3.3
通讯作者：
Yang, Xin-She
中科院分区：
文献类型：
--
作者：
Yang, Xin-She
科学和工程中的许多问题可以表述为受复杂非线性约束的优化问题。高度非线性问题的解决方案通常需要复杂的优化算法，传统的算法可能难以处理这样的问题。目前的趋势是使用自然启发的算法，因为它们的灵活性和有效性。然而，有一些关于自然启发的计算和群体智能的关键问题。本文对近年来的一些自然启发式算法进行了深入的综述，重点介绍了它们的搜索机制和数学基础。一些具有挑战性的问题被确定和突出的五个开放的问题，涉及算法的收敛性和稳定性，参数调整，数学框架，基准和可扩展性的作用的分析。对这些问题进行了讨论，并提出了今后的研究方向。(c)2020爱思唯尔B.V.保留所有权利。
Many problems in science and engineering can be formulated as optimization problems, subject to complex nonlinear constraints. The solutions of highly nonlinear problems usually require sophisticated optimization algorithms, and traditional algorithms may struggle to deal with such problems. A current trend is to use nature-inspired algorithms due to their flexibility and effectiveness. However, there are some key issues concerning nature-inspired computation and swarm intelligence. This paper provides an in-depth review of some recent nature-inspired algorithms with the emphasis on their search mechanisms and mathematical foundations. Some challenging issues are identified and five open problems are highlighted, concerning the analysis of algorithmic convergence and stability, parameter tuning, mathematical framework, role of benchmarking and scalability. These problems are discussed with the directions for future research. (c) 2020 Elsevier B.V. All rights reserved.