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Path planning of Uninhabited Aerial Vehicle(UAV) is a complicated global optimum problem.In the paper,an improved Gravitational Search Algorithm(GSA) was proposed to solve the path planning problem.Gravitational Search Algorithm(GSA) is a newly presented under the inspiration of the Newtonian gravity,and it is easy to fall local best.On the basis of introducing the idea of memory and social information of Particle Swarm Optimization(PSO),a novel moving strategy in the searching space was designed,which can improve the quality of the optimal solution.Subsequently,a weighted value was assigned to inertia mass of every agent in each iteration process to accelerate the convergence speed of the search.Particle position was updated according to the selection rules of survival of the fittest.In this way,the population is always moving in the direction of the optimal solution.The feasibility and effectiveness of our improved GSA approach was verified by comparative experimental results with PSO,basic GSA and two other GSA models.
Path planning of Uninhabited Aerial Vehicle (UAV) is a complicated global optimum problem. In the paper, an improved Gravitational Search Algorithm (GSA) was proposed to solve the path planning problem. Gravitational Search Algorithm (GSA) is a newly presented under the inspiration of the Newtonian gravity, and it is easy to fall local best. On the basis of introducing the idea of memory and social information of Particle Swarm Optimization (PSO), a novel moving strategy in the searching space was designed, which can improve the quality of the optimal solution. Substituted, a weighted value was assigned to inertia mass of every agent in each iteration process to accelerate the convergence speed of the search. Part position was updated according to the selection rules of survival of the fittest. In this way, the population is always moving in the direction of the optimal solution. The feasibility and effectiveness of our improved GSA approach was verified by comparative experimental results with PS O, basic GSA and two other GSA models.