Abstract
Ant colony optimization (ACO) algorithms have been used successfully to solve a wide variety of combinatorial optimization problems. In the recent past many modifications have been proposed in ACO algorithms to solve continuous optimization problems. However, most of the ACO variants to solve continuous optimization problems lack ability of efficient exploration of the search space and suffer from the problem of premature convergence. In this work a new ACO algorithm (ACO–LD) is proposed that incorporates Laplace distribution based interaction scheme among the ants. Also, in order to avoid the problem of stagnation, an additional diversification mechanism is introduced. The proposed ACO–LD is tested on benchmark test functions taken from Congress on Evolutionary Computation 2014 (CEC2014) and the results are compared with four state-of-the-art algorithms reported in CEC2014. ACO–LD is also applied to solve six real life problems and the results are compared with the results of six other algorithms reported in the literature. The analysis of the results shows that the overall performance of ACO–LD is found to be better than the other algorithms included in the present study.
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