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Research Article Open Access
In this paper, an efficient ant colony optimization (EACO) algorithm is proposed based on efficient sampling method for solving combinatorial, continuous and mixed-variable optimization problems. In EACO algorithm, Hammersley Sequence Sampling (HSS) is introduced to initialize the solution archive and to generate multidimensional random numbers. The capabilities of the proposed algorithm are illustrated through 9 benchmark problems. The results of the benchmark problems from EACO algorithm and the conventional ACO algorithm are compared. More than 99% of the results from the EACO show efficiency improvement and the computational efficiency improvement range from 3% to 71%. Thus, this new algorithm can be a useful tool for large-scale and wide range of optimization problems. Moreover, the performance of the EACO is also tested using the five variants of ant algorithms for combinatorial problems.
Efficient ant colony optimization (EACO), Hamersley sequence sampling (HSS), Metaheuristics, Oracle penalty method, Efficient ant colony optimization (EACO), Hamersley sequence sampling (HSS), Metaheuristics, Oracle penalty method