Abstract: Inspired by the hierarchical coordination mechanism of the nervous system, this paper proposes a multi-layer co-evolutionary Neural Perception Optimization Algorithm (NPOA) for complex optimization problems. The proposed algorithm is built upon three core modules. First, we design a gated elite layer inspired by the prefrontal cortex that integrates dualsource attention and spatial gating to guide the search toward high-value regions and regulate search directions. Second, inspired by hippocampal associative learning, we develop a differential association-based synaptic development layer for the structured reuse of historical search information and refined local search. Finally, to mimic the stimulus-response mechanism of the sensory cortex, we introduce a stimulus exploration layer with probabilistic switching updates to preserve diversity and balance exploration and exploitation. Through the information interaction and coordinated functioning among these modules, NPOA constructs a unified search framework that enhances population collaboration, improves information reuse, and strengthens global-local coordination throughout the evolutionary process. To assess its performance, NPOA was evaluated against more than ten advanced metaheuristic algorithms on benchmark functions and practical constrained optimization problems. Experimental results showed that NPOA achieved competitive or superior performance in terms of solution accuracy, convergence speed, and robustness, demonstrating its effectiveness and potential for solving complex optimization problems. Source code is available at the link in the end of this paper.

Key words: Neurons')">Neurons, Prefrontal cortex, Meta-heuristic algorithm, Global optimization