Multi-objective optimisation using evolutionary algorithms constitutes a powerful computational framework that addresses complex problems involving conflicting objectives.

Other algorithms, such as the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), break the optimization problem into smaller sub-problems, each representing a weighted combination ...

Second, the conversion to a single-objective optimization problem involves additional constraints. Third, since most real-world goal programming problems involve nonlinear criterion functions, the ...

Xiaoseng Zhang, Multi-objective Optimization Design in Construction Period Considering the Influence of Marine Climate, Journal of Coastal Research, SPECIAL ISSUE NO. 115. Advances in Water Resources, ...

Informing Building Retrofits Using Surrogates of Physics-Based Simulation Models: A Comparison of Multi-Objective Optimization Algorithms Abstract: Surrogate models are increasingly used to reduce the ...

Computational optics integrates optical hardware and algorithms, enhancing imaging capabilities through joint optimization ...

Determining crop-production functions using multi-objective evolutionary algorithms Abstract: The determination of crop production functions which describe the relationship between irrigation water ...

Machine learning algorithms are gaining popularity in the hydrologic sciences. These algorithms often require tuning hyperparameters to tailor their performance to a specific purpose. Often these ...