Improved continuous Ant Colony Optimization algorithms for real-world engineering optimization problems

The Ant Colony Optimization (ACO) algorithm is a well-known optimization method that has been successfully applied to solve many difficult discrete optimization problems. A decade ago, a variant of ACO, called ACO(R), was developed for continuous search spaces. This work proposes two new variants of ACO(R); namely, IACO(R) and LIACO(R), with improved performance in solving real-world engineering optimization problems. The IACO(R) uses a success-based random-walk selection that chooses between Brownian motion and Levy flights. Thus, trying to balance exploitation and exploration, respectively. The LIACO(R), on the other hand, is a memetic version of IACO(R) where a local search is used to enhance solutions in the colony. Furthermore, the ACO(R) is tested on the 22 real-world engineering optimization problems of the IEEE CEC 2011. The proposed variants are also tested on the same set of problems against five state-of-the-art optimization methods. The proposed IACO(R) and LIACO(R) outperform the original ACO(R) on most problems. In addition, the results of the comparative analysis show the superiority of LIACO(R) compared to the other tested algorithms.