Building Blocks Driven Self-Adaptation in Ant Colony Optimisation

Abstract

This paper presents the implementation and evaluation of the ant colony optimisation with adaptive building blocks, an approach designed to improve the performance of the ant colony optimisation algorithm by selecting self-adaptive building blocks through Elitist, Roulette, and Hybrid (combination between Elitist and Roulette) selection strategies. Ant colony optimisation's performance is heavily dependent on the selection of its parameters, including the building blocks, which determine the search space.The proposed approach aims to enhance the ant colony optimisation algorithm's performance by adapting the building blocks during the optimisation process, with the goal of improving the exploration and exploitation of the search space. The approach selects the most promising building blocks in a dynamic process based on their performance in previous iterations and updates them accordingly. The proposed algorithm was evaluated using the CEC'2013 benchmark functions, which are commonly used to evaluate optimisation algorithms. The results of the experiments demonstrate that Elitist selection outperformed all other algorithms, including the standard ant colony optimisation, Roulette, and Hybrid. Elitist selection of building blocks showed the best performance in terms of computational time and the number of iterations required to reach the optimal fitness value.