Thumbnail
Access Restriction
Open

Author Jadon, Ramlakhan Singh ♦ Dutta, Unmukh
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Self-adaptive Approach ♦ Uniform Mutation ♦ Modified Ant Colony Optimization Algorithm ♦ Ant Colony Optimization ♦ General Term ♦ Strong Robustness ♦ Novel Metaheuristic Algorithm ♦ Mutation Operator ♦ Different Combinational Optimization Problem ♦ Optimal Final Solution ♦ Real Ant Colony ♦ Local Optimum ♦ Algorithm Escape ♦ Experimental Result ♦ Efficient Ant Colony Optimization Algorithm ♦ Effective Sub-solutions ♦ Uniform Mutation Operator
Abstract Ant Colony Optimization (ACO) algorithm is a novel metaheuristic algorithm that has been widely used for different combinational optimization problem and inspired by the foraging behavior of real ant colonies. Ant Colony Optimization has strong robustness and easy to combine with other methods in optimization. In this paper, an efficient ant colony optimization algorithm with uniform mutation operator using self-adaptive approach has been proposed. Here mutation operator is used for enhancing the algorithm escape from local optima. The algorithm converges to the optimal final solution, by gathering the most effective sub-solutions. Experimental results show that the proposed algorithm is better than the algorithm previously proposed. General terms
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study