Physics-Based Metaheuristic Optimization Algorithms for Pathfinding

This paper examines the performance of nine physics-based metaheuristic algorithms—Electromagnetism-Like Algorithm (EMLA), Fluid Search Algorithm (FSA), Gravitational Search Algorithm (GSA), and six hybrids (EMLA + FSA, EMLA + GSA, FSA + ELMA, FSA + GSA, GSA + EMLA, GSA + FSA) for vehicle pathfinding. Performance is evaluated using four metrics: travel distance, time, energy consumption, and number of obstacles encountered, along with a weighted multi-objective cost combining these metrics. Simulation results show that hybrid algorithms generally outperform their individual counterparts. The ranking of algorithms varies with the weighting of the metrics. The hybrids involving EMLA consistently achieve the best overall performance across grid sizes. FSA + EMLA performs best when minimizing distance. EMLA + GSA is most effective when time is the priority. EMLA + GSA also performs best when energy use and obstacle avoidance dominate. It is recommended to use the cases FSA + GSA or FSA + EMLA for energy-efficient and obstacle-aware navigation, GSA or GSA + EMLA for achieving global optimality on complex maps, and EMLA + FSA and FSA + EMLA for dynamic environments requiring high safety.