CMDO：一种用于自适应种群搜索的认知记忆驱动优化算法

中文摘要 AI 辅助

基于种群的优化方法通常通过成功的解、参数自适应或算子性能来利用先前的搜索信息，但它们很少保留搜索行为成功或失败时的上下文。我们引入了认知记忆驱动优化（CMDO），一种无导数的基于种群的优化器，它将经验表示为搜索上下文、搜索行为和观察结果之间的关系。CMDO将这些经验组织到工作记忆、情景记忆和巩固记忆中，根据与当前搜索状态的相似性进行检索，并利用正反两方面的证据来指导后续搜索。检索到的经验不会重放先前的候选位置；相反，它选择从当前种群中通过探索性、定向和局部搜索行为以及自适应搜索几何结构重建的搜索策略。我们在COCO上的选定黑盒优化基准测试套件（BBOB/COCO）和2017年进化计算大会（CEC2017）问题上，将CMDO与DE、CMA-ES、SHADE、GWO、HHO和ORCA进行了评估，并进一步研究了其在七参数光伏模型估计中的应用，使用实测的电流-电压数据。结果表明，CMDO具有问题依赖但具有竞争力的优化性能，包括在CEC2017 F10上相比其他方法获得最低的中位误差。更重要的是，对搜索轨迹的分析表明，上下文相关的回忆改变了所执行搜索行为的分布，而不成功的经验仍可作为后续决策的负面证据，这表明累积的经验直接影响后续的搜索行为。这些结果支持将显式的上下文-行为-结果记忆作为控制基于种群搜索的主动机制。

英文摘要

Population-based optimization methods often use previous search information through successful solutions, parameter adaptation, or operator performance, but they rarely retain the context in which a search behavior succeeded or failed. We introduce Cognitive Memory-Driven Optimization (CMDO), a derivative-free population-based optimizer that represents experience as the relationship between search context, search behavior, and observed outcome. CMDO organizes these experiences across working, episodic, and consolidated memory, retrieves them according to similarity with the current search state, and uses both positive and negative evidence to guide subsequent search. Retrieved experience does not replay previous candidate locations; instead, it selects search recipes that are reconstructed from the current population through exploratory, directed, and local search behaviors with adaptive search geometry. We evaluate CMDO on selected Blackbox Optimization Benchmarking test suite on COCO (BBOB/COCO) and Congress on Evolutionary Computation 2017 (CEC2017) problems against DE, CMA-ES, SHADE, GWO, HHO, and ORCA, and further study its application to seven-parameter photovoltaic model estimation using measured current--voltage data. The results show problem-dependent but competitive optimization performance, including the lowest median error among the compared methods on CEC2017 F10. More importantly, analysis of the search traces shows that context-dependent recall changes the distribution of executed search behaviors, while unsuccessful experiences remain available as negative evidence for later decisions, showing that accumulated experience directly influences subsequent search behavior. These results support the use of explicit context--behavior--outcome memory as an active mechanism for controlling population-based search.

发表机构

• The University of Alabama（阿拉巴马大学）

机构由 AI 辅助整理，请以论文原文为准。