Optimization Using Evolutionary Metaheuristic Techniques - A Brief Review
AbstractOptimization is necessary for finding appropriate solutions to a range of real life problems. Evolutionary approach based meta-heuristics have gained prominence in recent years for solving Multi Objective Optimization Problems (MOOP). Multi Objective Evolutionary Approaches (MOEA) has substantial success across a variety of real-world engineering applications. The present paper attempts to provide a general overview of a select few algorithms including genetic algorithms, ant colony optimization, particle swarm optimization and simulated annealing techniques. Additionally, the review is extended to present differential evolution and teaching-learning based optimization. Few applications of the said algorithms are also presented. This review intends to serve as a reference for further work in this domain.
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