A reinforcement-learning-guided memetic Narwhal Optimization Algorithm for global and engineering optimization

Abstract

The Narwhal Optimization Algorithm is a recent swarm metaheuristic that, like most population-based optimisers, is prone to premature convergence, is sensitive to random initialisation, and relies on a rigid, schedule-driven exploration–exploitation balance. This paper develops and rigorously evaluates two enhanced variants that address these weaknesses. NWOA-OBL adds opposition-based initialisation and a stagnation-triggered, dynamic-opposition restart that replenishes population diversity, while NWOA-RL replaces the fixed exploration ratio with a Q-learning controller that selects the search behaviour online from the observed progress of the optimisation. Both variants are made memetic through a shared elite local search that supplies the local-refinement drive the original wave-based moves lack. The variants are compared against the baseline algorithm and seven established and recent optimisers on the CEC2017 suite at dimension thirty and the CEC2022 suite at dimensions ten and twenty, on six constrained engineering-design problems, and through parameter-sensitivity and ablation studies, all under a common evaluation budget with thirty independent runs and full nonparametric statistical analysis. Pooled over the benchmark functions, NWOA-RL attains the joint-best mean rank, statistically indistinguishable from the strongest competitor and significantly ahead of the remaining baselines, and reaches near-optimal engineering designs. The ablation identifies the elite local search as the decisive component of the design.