Optimization of the sizing of a solar thermal electricity plant: mathematical programming versus genetic algorithms

Cabello, J. M. ; Cejudo, J. M. ; Luque, M. ; Ruiz, F. ; Deb, K. ; Tewari, R. (2009) Optimization of the sizing of a solar thermal electricity plant: mathematical programming versus genetic algorithms Proceedings of the Congress on Evolutionary Computation (CEC-2009), Piscatway, NJ: IEEE Press . pp. 1193-1200.

[img]
Preview
PDF - Author Version
1MB

Official URL: http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arn...

Related URL: http://dx.doi.org/10.1109/CEC.2009.4983081

Abstract

Genetic algorithms (GAs) have been argued to constitute a flexible search thereby enabling to solve difficult problems which classical optimization methodologies may find hard to solve. This paper is intended towards this direction and show a systematic application of a GA and its modification to solve a real-world optimization problem of sizing a solar thermal electricity plant. Despite the existence of only three variables, this problem exhibits a number of other common difficulties-black-box nature of solution evaluation, massive multi-modality, wide and non-uniform range of variable values, and terribly rugged function landscape-which prohibits a classical optimization method to find even a single acceptable solution. Both GA implementations perform well and a local analysis is performed to demonstrate the optimality of obtained solutions. This study considers both classical and genetic optimization on a fairly complex yet typical real-world optimization problems and demonstrates the usefulness and future of GAs in applied optimization activities in practice.

Item Type:Article
Source:Copyright of this article belongs to Proceedings of the Congress on Evolutionary Computation (CEC-2009), Piscatway, NJ: IEEE Press.
ID Code:81637
Deposited On:07 Feb 2012 03:50
Last Modified:18 May 2016 23:06

Repository Staff Only: item control page