Continuous optimisation, Anc colony Optimisation algorithm, diversity of solutions, local search optimisation, experimental comparison

This paper proposes two new variants of the Continuous Ant Colony Opti-mization algorithm, ACOR. The first variant, called the Adaptive ACOR (AACOR), uses the relative diversity of the solutions in the algorithm's ar-chive to adapt its parameters. The second variant, called the memetic AACOR (MAACOR), uses a local search operator to improve the perfor-mance of AACOR. Both variants were tested on the 22 IEEE CEC 2011 real-world optimization problems and compared with ACOR and two state-of-the-art optimization methods. The results demonstrate the merits of the proposed approaches.