Engineering design studies can often be cast in terms of optimization problems. However, for such an approach to be worthwhile, designers must be content that the optimization approaches employed is fast convergence. Usefulness of heuristic algorithm as the search method for diverse optimization problems is examined. Evolutionary algorithms (EAs) are stochastic search methods that mimic the natural biological evolution and/or the social behavior of species. Such algorithms have been developed to arrive at near-optimum solutions to large-scale optimization problems, for which traditional mathematical techniques may fail. This paper compares the formulation and results of three evolutionary-based algorithms: genetic algorithm, clonal selection algorithm and particle swarm optimization. A brief description of each algorithm is presented. Benchmark comparisons among these algorithms are presented optimization problems, in terms of processing time, convergence speed, and quality of the results. The simulation results show that compared with genetic algorithm and clonal selection algorithm, the proposed particle swarm optimization based algorithm can improve the quality of the solution while speeding up the convergence process. Three words can summarize the main features of the proposed approach: faster, cheaper, and simpler.
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