Optimization Using Evolutionary Metaheuristic Techniques - A Brief Review

  • Sajja Radhika Dept of ME, 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 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.

Author Biography

Sajja Radhika, Dept of ME, RVR&JC College of Engineering (A), Guntur, AP
Dept of ME, RVR&JC College of Engineering (A), Guntur, AP
Published
2018-04-02
How to Cite
Radhika, S., & Chaparala, A. (2018). Optimization Using Evolutionary Metaheuristic Techniques - A Brief Review. Brazilian Journal of Operations & Production Management, 15(1). Retrieved from https://bjopm.emnuvens.com.br/bjopm/article/view/425
Section
Articles