relation: https://khub.utp.edu.my/scholars/8024/
title: Collaborative simulated annealing genetic algorithm for geometric optimization of thermo-electric coolers
creator: Khanh, D.V.K.
creator: Vasant, P.M.
creator: Elamvazuthi, I.
creator: Dieu, V.N.
description: Thermo-electric Coolers (TECs) nowadays are applied in a wide range of thermal energy systems. This is due to its superior features where no refrigerant and dynamic parts are needed. TECs generate no electrical or acoustical noise and are environment friendly. Over the past decades, many researches were employed to improve the efficiency of TECs by enhancing the material parameters and design parameters. The material parameters are the most significant, but they are restricted by currently available materials and module fabricating technologies. Therefore, the main objective of TECs design is to determine a set of design parameters such as leg area, leg length, and the number of legs. Two elements that play an important role when considering the suitability of TECs in applications are rated of refrigeration (ROR) and coefficient of performance (COP). In this chapter, the technical issues of TECs were discussed. After that, a new method of optimizing the dimension of TECs using collaborative simulated annealing genetic algorithm (CSAGA) to maximize the rate of refrigeration (ROR) was proposed. Equality constraint and inequality constraint were taken into consideration. The results of optimization obtained by using CSAGA were validated by comparing with those obtained by using stand-alone genetic algorithm and simulated annealing optimi-zation technique. This work revealed that CSAGA was more robust and more reliable than stand-alone genetic algorithm and simulated annealing. © 2016, Springer India.
publisher: Springer Verlag
date: 2016
type: Article
type: PeerReviewed
identifier:   Khanh, D.V.K. and Vasant, P.M. and Elamvazuthi, I. and Dieu, V.N.  (2016) Collaborative simulated annealing genetic algorithm for geometric optimization of thermo-electric coolers.  Studies in Computational Intelligence, 611.  pp. 155-183.  ISSN 1860949X     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84940207292&doi=10.1007%2f978-81-322-2544-7_5&partnerID=40&md5=0c6b6426c993f883faec3ffb3a4b24ed
relation: 10.1007/978-81-322-2544-7₅
identifier: 10.1007/978-81-322-2544-7₅