Gravitation field algorithm with optimal detection for unconstrained optimization
Lan Huang, Xuemei Hu, Yan Wang, Fang Zhang, Zhendong Liu, Wei Pang · 2017
Gravitation field algorithm (GFA) is a novel optimization algorithm derived from the Solar Nebular Disk Model (SNDM) in astronomy, based on the formation of planets, in recent years. In this research, an improved GFA with Optimal Detection (GFA-OD) is proposed for unconstrained optimization problems. Optimal Detection can efficiently locate the space that more likely contains the optimal solution(s) by initializing part of dust population randomly in the search space of a given problem, and then improves the accuracy of solutions. The comparison of results on four classical unconstrained optimization problems with varying dimensions demonstrates that the proposed GFA-OD outperforms many other classical heuristic optimization algorithms in accuracy, efficiency and running time in lower dimensions, such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).