Parallel Performance of Multi-Objective Evolutionary Algorithms in Climate-Economy Modelling
Exploring the Scalability and Convergence Properties of MOEAs for Climate-Economy Decision Support
W.M. van der Linden (TU Delft - Technology, Policy and Management)
JH Kwakkel – Mentor (TU Delft - Policy Analysis)
ME Warnier – Graduation committee member (TU Delft - Multi Actor Systems)
P. Biswas – Graduation committee member (TU Delft - Policy Analysis)
Jazmin Salazar – Graduation committee member (TU Delft - Policy Analysis)
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Abstract
This thesis investigates the comparative performance of three Multi-Objective Evolutionary Algorithms, Epsilon-NSGA-II, Borg, and Generational Borg, within the Evolutionary Multi-Objective Direct Policy Search framework, applied to the optimisation of the JUSTICE Integrated Assessment Model. By combining benchmark problems (DTLZ2 and DTLZ3) with the high-dimensional, multi-modal JUSTICE model, the study evaluates convergence dynamics, computational efficiency, and solution quality across varying levels of parallelisation. Results show that Borg consistently outperforms the other algorithms, particularly under high problem complexity, due to its asynchronous, steady-state architecture and adaptive operator features. Generational Borg performs better than Epsilon-NSGA-II on complex problems, but both suffer from scalability limitations due to synchronous execution. These findings underscore the importance of algorithm selection in climate-economy policy modelling, offering guidance on how MOEA design and computational resources interact to affect optimisation outcomes.