Neural-Network Guided Minima Forecasting for an Enhanced Particle Swarm Optimizer

Particle Swarm Optimization (PSO) is appreciated for its simplicity and ease of adaptation, yet its progress may stall when the swarm fails to identify promising regions early in the search. We present Neural-Network-Predicting Adaptive PSO (NNP-APSO), a variant that embeds an artificial neural network trained online to approximate the lower envelope of the objective landscape. At each iteration the network forecasts a candidate minimum, which is injected into the velocity update of under-performing particles; the swarm therefore shifts its exploration–exploitation balance automatically, without introducing additional control parameters. NNP-APSO is evaluated on the standard single-objective test suite (15 classical benchmarks) and compared, under identical computational budgets, with six established optimizers: Simulated Annealing, canonical PSO, Whale Optimization Algorithm, Grey Wolf Optimizer, Genetic Algorithm and Artificial Bee Colony. The experimental study indicates that NNP-APSO offers a promising alternative, delivering good and noteworthy results while highlighting the potential of real-time neural guidance within the PSO framework.