Colliding bodies algorithm with adaptive parameter adjustment strategy

Abstract

Colliding Bodies Optimization (CBO) is a new meta-heuristic algorithm that uses collisions between objects to move to a better position so that the solution tends to be an optimal solution. Aiming at the shortcomings of CBO algorithm's poor optimization accuracy and prone to evolutionary stagnation in the later stage of iterations, this paper proposes a new Colliding Bodies Optimization algorithm with adaptive adjustment strategy. Firstly, a set of initial solutions of colliding bodies are generated by the good-point set strategy to improve the stability of the algorithm solution. Secondly, we adjust the linear strategy of the control parameters to a nonlinear strategy and combine it with the fitness value to form an adaptive parameter adjustment strategy. Finally, in order to make the algorithm escape from the local optimal, a mutation operation is performed on the position of the object. In order to test the effectiveness of the proposed algorithm, experiments were conducted on 23 benchmark functions with it and the comparison algorithm. The experimental results show that the performance of the proposed algorithm is better than other experimental algorithms.