Ant Colony Optimization with Different Crossover Schemes for Continuous Optimization

In this paper we present three ant colony optimization (ACO\(_R\)) with different crossover operations to solve the continuous optimization problems. Crossover operations in the genetic algorithm are employed to generate some new probability density function set (PDFs) of ACO\(_R\) in the promising space, which is aimed at improving the global exploration ability of ACO\(_R\), and avoiding falling into the local minima and exploiting the correlation information among the design variables. The proposed algorithm is evaluated on some benchmark functions and the simulation results show that the proposed algorithm performs quite well and outperforms other algorithms.