Comparison of genetic algorithm to particle swarm for constrained simulation-based optimization of a geothermal power plant

被引：35

作者：

Clarke, Joshua ^{[1
]}

McLay, Laura ^{[2
]}

McLeskey, James T., Jr. ^{[1
]}

机构：

[1] Virginia Commonwealth Univ, Richmond, VA USA

[2] Univ Wisconsin, Madison, WI USA

来源：

ADVANCED ENGINEERING INFORMATICS | 2014年 / 28卷 / 01期

关键词：

Genetic algorithm; Particle swarm optimization; Simulation-based optimization; Energy system optimization; Geothermal power plant; CYCLE; ENERGY;

D O I：

10.1016/j.aei.2013.12.003

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The performance of a genetic algorithm is compared with that of particle swarm optimization for the constrained, non-linear, simulation-based optimization of a double flash geothermal power plant. Particle swarm optimization converges to better (higher) objective function values. The genetic algorithm is shown to converge more quickly and more tightly, resulting in a loss of solution diversity. Particle swarm optimization obtains solutions within 0.1% and 0.5% of the best known optimum in significantly fewer objective function evaluations than the genetic algorithm. (C) 2014 Elsevier Ltd. All rights reserved.

引用

页码：81 / 90

页数：10

共 29 条

[1]

[Anonymous], 2002, Computational Intelligence an Introduction

[2] Optimization methods applied to renewable and sustainable energy: A review [J].