A gravity search algorithm with a distance-fitness evaluation strategy for path planning

Abstract gravity Search Algorithm (GSA) is a swarm intelligence optimization algorithm inspired by Newton's law of universal gravitation. It uses particle masses and interparticle gravity forces to transmit information and evolve candidate solutions. Although the traditional GSA has been successfully applied in several fields, it still suffers from premature convergence and limited search capability when solving complex optimization problems. To address these limitations, this paper proposes a GSA with a self-adaptive attenuation factor and a distance-fitness evaluation strategy, termed SDGSA. The self-adaptive attenuation factor regulates the decay rate of the gravity constant, thereby improving the balance between exploration and exploitation. The distance-fitness evaluation strategy assesses both the spatial distances and fitness values of individuals, allowing the algorithm to adapt its search to the population distribution. This strategy increases population diversity and reduces the likelihood of becoming trapped in local optima. SDGSA is evaluated against several GSA variants and other metaheuristic algorithms on 58 benchmark functions and an unmanned aerial vehicle (UAV) path-planning problem. The benchmark results show that SDGSA outperforms its competitors. It also achieves high accuracy on challenging optimization problems.