Advances in meta-heuristic methods for large-scale black-box optimization problems
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摘要: 大型复杂工程装备的优化设计通常为高复杂度、高维度的优化问题，即所谓的大规模黑箱优化问题，其特点是目标函数和/或约束函数解析式不可知且设计变量维度很高。近年来，大规模黑箱优化问题在各领域引起了学者们的兴趣，而元启发式算法被认为是求解该问题的有效方法。为此，全面总结了近年来求解该问题的元启发式算法的研究进展，包括使用与不使用分解策略的元启发式算法，以及处理大规模昂贵优化问题的代理模型辅助元启发式算法，并指出了针对此问题的元启发式求解方法未来可能的研究方向。Abstract: The optimal design of complex engineering equipment usually faces high-complexity, high-dimensional optimization problems – the so-called "large-scale black-box optimization problems (LBOPs)" – which are characterized by unavailable mathematical expressions of objective functions and/or constraint functions, and high dimensionality of design variables. The LBOPs have attracted the interest of scholars in various fields in recent years, and meta-heuristic algorithms are considered effective methods for solving these problems. This paper comprehensively summarizes recent research progress in meta-heuristic algorithms for solving LBOPs, including meta-heuristic algorithms with and without decomposition strategies, and meta-heuristic algorithms for handling computationally expensive large-scale optimization problems. Finally, possible future research directions of meta-heuristic methods for solving LBOPs are proposed.
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图 5 代理模型与元启发式算法交互示意图
Figure 5. Diagram of interaction between surrogate modle and meta-heuristic algorithm
表 1 大规模黑箱优化问题元启发式算法研究文献汇总
Table 1. Summary of research advances using meta-heuristic algorithms for LBOPs
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表 2 合作协同进化算法研究文献汇总
Table 2. Summary of research advances of CCEA
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