Colliding Bodies Optimization And Its Application

Colliding Bodies Optimization is a novel meta-heuristic optimization algorithm.The algorithm is based on a physical principle.A moving object crashes into a stationary object.After collisions,the positions and velocities of both objects will be changed so that the objects move to a better place.The Colliding Bodies Optimization has the characteristics that are simple structure and processed eassily.The algorithm has been widely operated by scholars at home and abroad.At present,Colliding Bodies Optimization has been applied to various complex combinational optimization problems,such as optimum structural design problem,construction site layout planning problem.However,the algorithm has some weak performanceto get better precision of the algorithm and the weak local search ability.In this paper,some improved methods are proposed to improve the accuracy of the algorithm and the search ability,and modified versions are applied to some classical optimization problems.The mainly achievements in scientific research are as fellows:1.The elite opposition based learning strategy is apllied to Colliding Bodies Optimization to improve the exploration capacity of the algorithm.When the collision occurs,the speed of each body will be changed.The location of each body will be updated.After the updating,The elite opposition based learning strategy runs and calculates a opposite solution in the search space.The fitness value of each body and its inverse solution are compared.The optimal fitness value and the corresponding positions are retained.The improved algorithm is applied to the function optimization problem.The experimental results show that the Colliding Bodies Optimization based on the elite opposition based learning strategy has the better ability and performance.2.In order to solve emergency materials transshipment model,a hybrid Genetic Algorithm and Colliding Bodies Optimization algorithm is proposed.This hybrid algorithm utilize selection operator,crossover operator and mutation operator of Genetic Algorithm to improve the local search ability.For improving the ablity of algorithm to solve the discrete problem,encoding methods are changed based on the scheduling of emergency materials transshipment model.Experimental results show that the improved algorithm can solve the dispatch problem.3.Colliding Bodies Optimization with cue ball is proposed.The enhanced algorithm regards the object with the best fitness value in each generation as a cue ball.Cue ball need to collide with other objects.This idea is inspired by the snooker which rule is using cue ball to hit others to the hole.The proposed algorithm can effectively improve the local optimization ability.The improved algorithm is applied to the two-echelon logistics distribution routing optimization problem.Experimental results show that the improved algorithm can find the best distribution routing.