Solving nonconvex climate control problems: Pitfalls and algorithm performances

Carmen G. Moles, Julio R. Banga, Klaus Keller

Research output: Contribution to journalArticlepeer-review

34 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)35-44
Number of pages10
JournalApplied Soft Computing Journal
Issue number1
StatePublished - Dec 2004

All Science Journal Classification (ASJC) codes

  • Software


Dive into the research topics of 'Solving nonconvex climate control problems: Pitfalls and algorithm performances'. Together they form a unique fingerprint.

Cite this