CMDO: A Cognitive Memory-Driven Optimization Algorithm for Adaptive Population-Based Search

2026-09-28

AI Summary

This study addresses the limitation of existing population-based optimization methods in retaining memory of search contexts and success-failure experiences for adaptive guidance. We propose a cognitive memory-driven optimization algorithm featuring a tri-level architecture comprising working, episodic, and consolidated memory. This framework introduces explicit "context-action-outcome" associations as an active control mechanism for the first time. By retrieving historical experiences via similarity matching and integrating negative evidence from failure cases, the method adaptively reconstructs exploration, exploitation, and local search strategies to achieve geometric adjustment of the search process. As a derivative-free approach, it demonstrates superior performance on BBOB and CEC2017 benchmarks, achieving the lowest median error on function F10. Its effectiveness is further validated in photovoltaic parameter estimation, confirming that the proposed memory mechanism significantly reshapes the distribution of search behaviors.

Abstract

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.

Problem

• Population-based optimization
• Search context
• Cognitive memory
• Derivative-free optimization
• Experience retention

Innovation

• Cognitive Memory
• Context-Behavior-Outcome Representation
• Derivative-free Optimization
• Population-based Search
• Negative Evidence