Strategies in Hybrid Evolutionary Algorithms for Optimization

Authors

  • VISHAL JAIN ASSOCIATE PROFESSOR & HEAD DEPARTMENT OF ELECTRICAL & ELECTRONICS ENGINEERING. B.M.I.E.T SONEPAT, HARYANA, INDIA
  • Dr. DEVENDER SAINI
  • SALONI .

DOI:

https://doi.org/10.37591/joarb.v4i3.1203

Abstract

Abstract

Evolutionary computing has grown to be a significant methodology in the field of research. Robustness and adaptation are some of the prime features of evolutionary algorithms as compared to other global optimization techniques. Even though evolutionary computation is popularly used for solving several important practical problems in engineering, business, commerce, etc., there is scope for fine-tuning its performance. It is not easy to find any one best algorithm for solving all optimization problems. Hence there is need for a hybrid algorithm which is capable of handling several real world challenges such as, noise, imprecision and uncertainty. This paper presents a review on the methodologies adopted for hybrid evolutionary Algorithms.

Keywords: Evolutionary algorithms, genetic algorithm, particle swarm optimization, ant colony optimization, bacterial foraging optimization

Cite this Article

Vishal Jain, Devender Saini, Saloni. Strategies in Hybrid Evolutionary Algorithms for Optimization. Journal of Advancements in Robotics. 2017; 4(3):  29–32p.


Author Biography

  • VISHAL JAIN, ASSOCIATE PROFESSOR & HEAD DEPARTMENT OF ELECTRICAL & ELECTRONICS ENGINEERING. B.M.I.E.T SONEPAT, HARYANA, INDIA
    ASSOCIATE PROF. EEE DEPARTMENT

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Published

2018-01-30

Issue

Vol. 4 No. 3 (2017)

Section

Research Articles

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