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

Abstract

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 ACOR, was developed for continuous search spaces.This work proposes two new variants of ACOR; namely, IACOR and LIACOR, with improved performance in solving real-world engineering optimization problems. The IACOR uses a success-based random-walk selection that chooses between Brownian motion and Lévy flights. Thus, trying to balance exploitation and exploration, respectively. The LIACOR, on the other hand, is a memetic version of IACOR where a local search is used to enhance solutions in the colony. Furthermore, the ACOR 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 IACOR and LIACOR outperform the original ACOR on most problems. In addition, the results of the comparative analysis show the superiority of LIACOR compared to the other tested algorithms.