The Effect of the Annealing Schedule on Simulated Annealing for Function Optimization and Fuel Cell Design

Modeling is an integral part of fuel cell design and development. This paper identifies a long-standing inaccuracy in the fuel cell modeling literature. Specifically, it discusses an inexact insertion, in popular models, of cell/stack current into Nernst’s equation in the derivation of output (load) voltage. The origin of the inaccuracy is traced to the nature of reversible and irreversible potentials (equilibrium and non-equilibrium states) in the cell. The significance of the inaccuracy is explained in the context of the electrochemistry and thermodynamics of the fuel cell.

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Evaluation of planar free-breathing polymer electrolyte membrane fuel cell design

Journal of Power Sources ◽

10.1016/j.jpowsour.2003.11.012 ◽

2004 ◽

Vol 129 (1) ◽

pp. 68-72 ◽

Cited By ~ 48

Author(s):

Tero Hottinen ◽

Mikko Mikkola ◽

Peter Lund

Keyword(s):

Fuel Cell ◽

Polymer Electrolyte ◽

Polymer Electrolyte Membrane ◽

Free Breathing ◽

Cell Design ◽

Electrolyte Membrane

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Hybrid Genetic Algorithm and Simulated Annealing for Function Optimization

Journal of Information Technology and Computer Science ◽

10.25126/jitecs.20161215 ◽

2017 ◽

Vol 1 (2) ◽

pp. 82 ◽

Cited By ~ 1

Author(s):

Tirana Noor Fatyanosa ◽

Andreas Nugroho Sihananto ◽

Gusti Ahmad Fanshuri Alfarisy ◽

M Shochibul Burhan ◽

Wayan Firdaus Mahmudy

Keyword(s):

Genetic Algorithm ◽

Simulated Annealing ◽

Optimization Problems ◽

Linear Optimization ◽

Hybrid Genetic Algorithm ◽

Optimal Solution ◽

Function Optimization ◽

Non Linear ◽

Speed Up ◽

Linear Problems

The optimization problems on real-world usually have non-linear characteristics. Solving non-linear problems is time-consuming, thus heuristic approaches usually are being used to speed up the solution’s searching. Among of the heuristic-based algorithms, Genetic Algorithm (GA) and Simulated Annealing (SA) are two among most popular. The GA is powerful to get a nearly optimal solution on the broad searching area while SA is useful to looking for a solution in the narrow searching area. This study is comparing performance between GA, SA, and three types of Hybrid GA-SA to solve some non-linear optimization cases. The study shows that Hybrid GA-SA can enhance GA and SA to provide a better result

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