A new imperialist competitive algorithm with spiral rising mechanism for solving path optimization problems.

Abstract

Intelligent optimization algorithms have now become important means for solving global optimization problems. The imperialist competitive algorithm (ICA) is a nature-inspired meta-heuristic algorithm that imitates social behavior. ICA has been widely used in optimization problems, however, ICA tends to fall into a local optimal solution because of its fast convergence speed, which may lead to premature convergence when solving optimization problems. To solve these problems, a new improved ICA algorithm is proposed. Based on the original ICA algorithm, the theory of spiral rising is introduced to enlarge the search space and enhance the global search ability of the algorithm based on ensuring the necessary speed of convergence. In this paper, the improved optimization algorithm is applied to 19 classical benchmark functions, and the improved ICA is applied to the robot path optimization problems to solve the optimal path. The improved ICA algorithm improves the optimization ability and algorithm stability.