Optimization using evolutionary metaheuristic techniques: a brief review

  • Sajja Radhika Dept of CSE, RVR&JC College of Engineering (A), Guntur, AP
  • Aparna Chaparala Dept of ME, RVR&JC College of Engineering (A), Guntur, AP
Keywords: Optimization, Evolutionary algorithms, Meta-heuristic techniques, Applications.

Abstract

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.

Author Biography

Sajja Radhika, Dept of CSE, RVR&JC College of Engineering (A), Guntur, AP

Dept of ME, RVR&JC College of Engineering (A), Guntur, AP

Published
2018-05-10
How to Cite
Radhika, S., & Chaparala, A. (2018). Optimization using evolutionary metaheuristic techniques: a brief review. Brazilian Journal of Operations & Production Management, 15(1), 44-53. https://doi.org/https://doi.org/10.14488/BJOPM.2018.v15.n1.a17
Section
Articles