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

Mohammed Yusuf Mujawar; Shahram Rahimi; Noorbakhsh Amiri Golilarz

cs.NESep 28, 2026

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

Authors: Mohammed Yusuf Mujawar, Shahram Rahimi, Noorbakhsh Amiri Golilarz

Organizations: Department of Computer Science The University of Alabama, AL, USA

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.

Figures & tables

Figure 1 : Overall architecture of Cognitive Memory-Driven Optimization (CMDO).

Method	BBOB ( $D=5$ )			CEC2017 ( $D=10$ )
Method	F1	F8	F15	F1	F4	F10
CMDO	$1.450\times 10^{-3}$	5.7813	21.4916	$3.7848\times 10^{9}$	189.9318	1322.7172
HHO	$2.406\times 10^{-1}$	35.9889	47.1264	$1.1906\times 10^{10}$	566.7792	1622.6580
GWO	$1.088\times 10^{-2}$	3.9398	21.7654	$3.8409\times 10^{8}$	15.6910	1415.7922
ORCA	$1.038\times 10^{-2}$	3.2394	11.5687	$1.2090\times 10^{10}$	1900.3090	1586.3345
DE	$5.859\times 10^{-4}$	3.8711	20.5967	$9.6074\times 10^{6}$	7.6000	1946.1414

Table 1: Median final error over three seeds on BBOB and CEC2017 benchmarks (lower is better).

Panel of Figure 2 — Figure 2 : Comparison of convergence and memory-driven search behavior.

Panel of Figure 3 — Figure 3 : Photovoltaic parameter-estimation setup and response: (a) equivalent circuit of the seven-parameter two-diode model, and (b) measured versus fitted $I$ – $V$ characteristics for the RTC France solar cell at $33^{\circ}\mathrm{C}$ and $1000~\mathrm{W/m^{2}}$ .

Method	Median MAE	Mean MAE	Median RMSE	CMDO wins
CMDO	$0.005402$	$0.010135$	$0.007720$	–
HHO	$0.067527$	$0.066728$	$0.106345$	10/10
GWO	$0.015631$	$0.017507$	$0.025084$	7/10
ORCA	$0.014460$	$0.019404$	$0.020608$	8/10
DE	$0.001841$	$0.002706$	$0.002306$	2/10
CMA-ES	$0.005338$	$0.005368$	$0.006985$	6/10

Table 2: RTC France two-diode parameter-estimation results over ten seeds. Errors are in amperes.

Appendix figures & tables3 assets

Supplementary material from the paper’s appendix.

Appendix

Suite	Functions	$D$	Budget	Seeds
BBOB/COCO	F1, F8, F15	5	500	15–17
CEC2017	F1, F4, F10	10	1000	15–17

Appendix

Table 3: Numerical benchmark configuration.

Panel of Figure 4 — Figure 4 : Median best-so-far optimization error over seeds 15–17. The top row shows BBOB F1, F8, and F15; the bottom row shows CEC2017 F1, F4, and F10. The vertical axis is logarithmic.

Parameter	Lower bound	Upper bound
$I_{\mathrm{ph}}$ (A)	0	1
$I_{01}$ (A)	$10^{-15}$	$10^{-3}$
$I_{02}$ (A)	$10^{-15}$	$10^{-3}$
$R_{s}$ ( $\Omega$ )	0	0.5
$R_{p}$ ( $\Omega$ )	0.001	100
$a_{1}$	0.5	5

Appendix

Table 4: Parameter bounds for the RTC France two-diode model.

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