In this paper, we have proposed a new algorithm which combines PSO and GA in such a way that the new algorithm is more effective and efficient.The particle swarm optimization (PSO) algorithm has shown rapid convergence during the initial stages of a global search but ar More
In this paper, we have proposed a new algorithm which combines PSO and GA in such a way that the new algorithm is more effective and efficient.The particle swarm optimization (PSO) algorithm has shown rapid convergence during the initial stages of a global search but around global optimum, the search process will become very slow. On the other hand, genetic algorithm is very sensitive to the initial population. In fact, the random nature of the GA operators makes the algorithm sensitive to the initial population. This dependence to the initial population is in such a manner that the algorithm may not converge if the initial population is not well selected. This new algorithm can perform faster and does not depend on initial population and can find optimal solutions with acceptable accuracy. Optimal capacitor placement and sizing have been found using this hybrid PSO-GA algorithm. We have also found the optimal place and size of capacitors using GA and PSO separately and compared the results.
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