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Multi-objective optimization methods and application in energy saving
Y Cui, Z Geng, Q Zhu, Y Han - Energy, 2017 - Elsevier
Multi-objective optimization problems are difficult to solve in that the optimized objectives are
usually conflicting with each other. It is usually hard to find an optimal solution that satisfies …
usually conflicting with each other. It is usually hard to find an optimal solution that satisfies …
Variable surrogate model-based particle swarm optimization for high-dimensional expensive problems
Many industrial applications require time-consuming and resource-intensive evaluations of
suitable solutions within very limited time frames. Therefore, many surrogate-assisted …
suitable solutions within very limited time frames. Therefore, many surrogate-assisted …
[КНИГА][B] Surrogate-model-based design and optimization
Surrogate-Model-Based Design and Optimization | SpringerLink Skip to main content
Advertisement Springer Nature Link Account Menu Find a journal Publish with us Track your …
Advertisement Springer Nature Link Account Menu Find a journal Publish with us Track your …
Machine learning enhancing metaheuristics: a systematic review
During the optimization process, a large number of data are generated through the search.
Machine learning techniques and algorithms can be used to handle the generated data to …
Machine learning techniques and algorithms can be used to handle the generated data to …
A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms
Evolutionary algorithms are widely used for solving multiobjective optimization problems but
are often criticized because of a large number of function evaluations needed …
are often criticized because of a large number of function evaluations needed …
Adaptive differential evolution with ensembling operators for continuous optimization problems
Differential evolution is one of the most popular evolutionary algorithms for continuous
optimization. In this paper, we introduce a new algorithm named the adaptive differential …
optimization. In this paper, we introduce a new algorithm named the adaptive differential …
A fast kriging-assisted evolutionary algorithm based on incremental learning
D Zhan, H ** and sparse Gaussian modeling
Gaussian processes (GPs) are widely employed in surrogate-assisted evolutionary
algorithms (SAEAs) because they can estimate the level of uncertainty in their predictions …
algorithms (SAEAs) because they can estimate the level of uncertainty in their predictions …