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Asian Journal of Engineering, Sciences and Technology - Volume 5, Issue 2 2015
By Muhammad Zubair, Jawwad Ahmad, Syed Sajjad Hussain Rizvi, Muhammad Moinuddin
Keywords: Evolutionary algorithms, genetic algorithm, heuristic optimization, particle swam optimization algorithm, hybrid genetic and particle swam algorithm
It is a well-known fact that the performance of classical hard computing is found to be constrained in finding the optimized solution which may not be in the search space. This issue has been resolved by employing various soft computing approaches. A soft computing approach evolves new solutions from within the given search space and then finds the optimal solution based on the given objective(s) and constraint(s). Genetic algorithm (GA) and particle swam optimization (PSO) algorithm are the most employed evolutionary algorithms for solving multi-objective complex problems. Moreover, researchers have proposed three different types of their hybrid combination for extra improved performance. This paper proposes a new hybrid genetic particle swarm optimization (HGPSO) algorithm and evaluates it on a few of the standard testing functions. The evaluation results prove that the proposed hybridization is much better than the three existing hybrid approaches given in the literature.