Global optimization of climate control problems using evolutionary and stochastic algorithms

Abstract

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 misconverged ultimately.

Quantum technologies for climate change: Preliminary assessment

“…• Climate mitigation and adaptation: climate [69] and weather [70] modeling and control [71], adaption [72], biodiversity and ecosystems [73], reducing carbon emissions [74].…”