CFO Algorithm Using Niche and Opposition-Based Learning

Abstract

The Central Force Optimization (CFO) algorithm is a new multi-dimensional search-determined heuristic optimization algorithm. The results obtained by using CFO algorithm are unstable and easy to fall into local optimum. To solve this shortcoming, we propose a new algorithm for central gravity optimization using Niche and Opposition-Based Learning. Based on the extension theory, the algorithm model is constructed. The niche is divided into groups, we settled shared area for these groups. The adaptive sharing of the particles in the shared area can effectively prevent the algorithm from prematurely converging and enhance the global search ability. The optimal particle is introduced into the elite reverse learning strategy to enhance the development of the solution space and improve the accuracy of the algorithm. The performance of the algorithm is evaluated by the test function, and the results show that the optimization performance of the algorithm is significantly improved.