Multi-Robot Path Planning (MRPP) requires optimizing multiple conflicting objectives, including minimizing path length, maximizing smoothness, avoiding obstacles, and reducing inter-robot path intersections. Addressing these objectives necessitates a careful balance between global exploration of the search space and local exploitation for solution refinement. To this end, we propose a hybrid optimization algorithm that combines the exploration capability of Grey Wolf Optimization (GWO) with the exploitation strength of Electric Eel Foraging Optimization (EEFO). The resulting hybrid EEFO–GWO approach employs an adaptive switching mechanism and an enhanced local search procedure to dynamically integrate their complementary strengths. The performance of the proposed method is evaluated through extensive simulations and compared against five state-of-the-art swarm intelligence algorithmss—EEFO, GWO, Whale Optimization Algorithm (WOA), African Vultures Optimization Algorithm (AVOA), and Harris Hawk Optimization (HHO). The evaluation spans multi-robot configurations ranging from two to four robots and three obstacle scenarios containing four to six circular obstacles of varying sizes and placements, thereby reflecting diverse levels of spatial clutter and coordination complexity. All performance metrics are averaged over ten independent runs for each configuration to ensure statistical reliability. The results show that the proposed hybrid EEFO–GWO consistently outperforms all baselines, with notable reductions in obstacle collisions and path intersections. In terms of overall best cost, the algorithm achieves an average improvement of 41.18% over the strongest competing method across all configurations, with a coefficient of variance of 30.23%, indicating both substantial gains and stable convergence.
Loganathan et al. (Thu,) studied this question.