Abstract
Meta-heuristic algorithms are popular for their efficiency in solving complex optimization problems. Although there are many known algorithms, identifying ways to improve their performance remains an important research area. This paper proposes a brain neuroscience-inspired meta-heuristic algorithm called the Neural Population Dynamics Optimization Algorithm (NPDOA). There are three strategies in NPDOA. (1) The attractor trending strategy drives neural populations towards optimal decisions, thereby ensuring exploitation capability. (2) The coupling disturbance strategy deviates neural populations from attractors by coupling with other neural populations, thus improving exploration ability. (3) The information projection strategy controls the communication between neural populations, enabling a transition from exploration to exploitation. The results of benchmark and practical problems verified the effectiveness of NPDOA.
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