Global optimization of climate control problems using evolutionary and stochastic algorithms

  • Carmen G. Moles
  • Adam S. Lieber
  • Julio R. Banga
  • Klaus Keller
Conference paper

DOI: 10.1007/978-1-4471-3744-3_32

Cite this paper as:
Moles C.G., Lieber A.S., Banga J.R., Keller K. (2003) Global optimization of climate control problems using evolutionary and stochastic algorithms. In: Benítez J.M., Cordón O., Hoffmann F., Roy R. (eds) Advances in Soft Computing. Springer, London

Summary

Global optimization can be used as the main component for reliable decision support systems. In this contribution, we explore numerical solution techniques for nonconvex and nondifferentiable economic optimal growth models. As an illustrative example, we consider the optimal control problem of choosing the optimal greenhouse gas emissions abatement to avoid or delay abrupt and irreversible climate damages. We analyze a number of selected global optimization methods, including adaptive stochastic methods, evolutionary computation methods and deterministic/hybrid techniques.

Differential evolution (DE) and one type of evolution strategy (SRES) arrived to the best results in terms of objective function, with SRES showing the best convergence speed. Other simple adaptive stochastic techniques were faster than those methods in obtaining a local optimum close to the global solution, but mis-converged ultimately.

Key words

global optimization optimal control climate thresholds climate change detection 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag London 2003

Authors and Affiliations

  • Carmen G. Moles
    • 1
  • Adam S. Lieber
    • 2
  • Julio R. Banga
    • 1
  • Klaus Keller
    • 3
  1. 1.Process Engineering GroupInstituto de Investigaciones Marinas (CSIC)VigoSpain
  2. 2.Mission VenturesSan DiegoUSA
  3. 3.Department of GeosciencesThe Pennsylvania State UniversityUniversity ParkUSA

Personalised recommendations