@article{Radhika_Chaparala_2018, title={Optimization using evolutionary metaheuristic techniques: a brief review}, volume={15}, url={https://bjopm.org.br/bjopm/article/view/425}, DOI={10.14488/BJOPM.2018.v15.n1.a17}, abstractNote={<p>Optimization 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 few selected 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.</p>}, number={1}, journal={Brazilian Journal of Operations & Production Management}, author={Radhika, Sajja and Chaparala, Aparna}, year={2018}, month={May}, pages={44–53} }