Publication Type:

Journal Article

Source:

ARPN Journal of Engineering and Applied Sciences, Volume 11, Issue 19, p.11675 – 11683 (2017)

URL:

http://www.arpnjournals.org/jeas/research_papers/rp_2016/jeas_1016_5128.pdf

Keywords:

Dynamic programming, Heuristic algorithms, lagrangian relaxation (LR), multi stage decision process, search algorithms

Abstract:

This paper mainly deals with the review on the various advanced optimization techniques. Optimization reveals significance advances in computing systems and it has become the most promising techniques for a variety of engineering applications. This paper highlights the various techniques such as evolutionary techniques, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) to enhance the search process by improving the diversity, and the convergence toward the preferred solution have been analysed. A comparative study between the single and multi-objective based Optimization techniques including GA, PSO and Hybrid are presented. This comparison will be very helpful for industries to determine the optimal parameters and improve the process and quality of products.  A variety of objective functions and its formulations are presented. Lagrangian relaxation is a tool to find upper bounds on a given arbitrary maximization problem. The main theme of this review is that the LD is naturally applied for a wide class of combinatorial algorithms which leads to get a significant solution.  Among all the traditional optimization techniques, in recent years, heuristic algorithms are mostly applied to solve most of the combinatorial problems.  Optimization  algorithms  can  lead  to appropriate solution for the real time applications.

Cite this Research Publication

R. Subramani and Vijayalakshmi, C., “A Review on Advances Optimization Techniques”, ARPN Journal of Engineering and Applied Sciences, vol. 11, no. 19, pp. 11675 – 11683, 2017.