Learning to Accelerate Evolutionary Search for Large-Scale Multiobjective Opt…

Most existing evolutionary search strategies are not so efficient when directly handling the decision space of large-scale multiobjective optimization problems (LMOPs). To enhance the efficiency of…

Reducing Negative Transfer Learning via Clustering for Dynamic Multiobjective…

Dynamic multiobjective optimization problems (DMOPs) aim to optimize multiple (often conflicting) objectives that are changing over time. Recently, there are a number of promising algorithms propos…

Multimodal Multiobjective Evolutionary Optimization With Dual Clustering in D…

This article suggests a multimodal multiobjective evolutionary algorithm with dual clustering in decision and objective spaces. One clustering is run in decision space to gather nearby solutions, w…

An Ensemble Surrogate-Based Framework for Expensive Multiobjective Evolutiona…

Surrogate-assisted evolutionary algorithms (SAEAs) have become very popular for tackling computationally expensive multiobjective optimization problems (EMOPs), as the surrogate models in SAEAs can…

Forecasting Wind Speed Time Series Via Dendritic Neural Regression

Wind energy is considered one of the fastest growing renewable ('green') energy resources. Precise wind power forecasting is imperative to ensure reliable power system planning and wind farm operat…

A Constrained Multiobjective Evolutionary Algorithm With Detect-and-Escape St…