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Stability Enhancement of Transfer Function by Ant Colony Optimization and Particle Swarm Optimization Algorithm


Author : Prabha Singh, Jaya Mishra, Chaman Yadav, Kushal Tiwari and Shashank Singh

Pages : 774-777, DOI: https://doi.org/10.14741/ijcet/v.8.3.35
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Abstract

This paper presents the concepts of three evolutionary algorithms i.e, ant colony optimization and particle swarm optimization algorithm. An evolutionary algorithm copies the way how evolution occurs in the nature. There are various types of evolutionary algorithms. This paper focuses on ACO and PSO algorithms. ACO provides solution to various optimization problems. It follows the principle of survival of the fittest. Various problems such as knapsack problem, TSP(travelling salesman problem) can be solved using genetic algorithm. Ant colony optimization is a heuristic algorithm which follows the behaviour of ants i.e., the way ants seek food in their environment by starting from their nest. Particle swarm optimization algorithm (PSO) is also an optimization algorithm which also uses a method of searching using some heuristics.

Keywords: Ant colony optimization, Evolutionary Algorithm, Particle swarm optimization algorithm, Swarm intelligence.

Article published in International Journal of Current Engineering and Technology, Vol.8, No.3 (May/June 2018)

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