Promoting objective knowledge transfer: a cascaded fuzzy system for solving dynamic Multiobjective optimization problems
In this article, a novel dynamic multiobjective optimization algorithm (DMOA) with a
cascaded fuzzy system (CFS) is developed, which aims to promote objective knowledge …
cascaded fuzzy system (CFS) is developed, which aims to promote objective knowledge …
Solving dynamic multiobjective optimization problems via feedback-guided transfer and trend manifold prediction
Y Wang, K Li, GG Wang, D Gong… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Solving dynamic multiobjective optimization problems (DMOPs) is very challenging due to
the requirements to respond rapidly and precisely to changes in an environment. Many …
the requirements to respond rapidly and precisely to changes in an environment. Many …
A novel preference-driven evolutionary algorithm for dynamic multi-objective problems
Most studies in dynamic multi-objective optimization have predominantly focused on rapidly
and accurately tracking changes in the Pareto optimal front (POF) and Pareto optimal set …
and accurately tracking changes in the Pareto optimal front (POF) and Pareto optimal set …
Niche Center Identification Differential Evolution for Multimodal Optimization Problems
Niching techniques are commonly incorporated into evolutionary computation (EC)
algorithms to address multimodal optimization problems (MMOPs). Nevertheless, identifying …
algorithms to address multimodal optimization problems (MMOPs). Nevertheless, identifying …
Dynamic multi-objective optimization based on classification response of decision variables
J Li, R Liu, R Wang - Information Sciences, 2025 - Elsevier
In recent years, many dynamic multi-objective optimization algorithms (DMOAs) have been
proposed to address dynamic multi-objective optimization problems (DMOPs). Most existing …
proposed to address dynamic multi-objective optimization problems (DMOPs). Most existing …
Combining kernelised autoencoding and centroid prediction for dynamic multi‐objective optimisation
Evolutionary algorithms face significant challenges when dealing with dynamic multi‐
objective optimisation because Pareto optimal solutions and/or Pareto optimal fronts …
objective optimisation because Pareto optimal solutions and/or Pareto optimal fronts …
An Evolutionary Network Architecture Search Framework with Adaptive Multimodal Fusion for Hand Gesture Recognition
Hand gesture recognition (HGR) based on multimodal data has attracted considerable
attention owing to its great potential in applications. Various manually designed multimodal …
attention owing to its great potential in applications. Various manually designed multimodal …
An accelerate Prediction Strategy for Dynamic Multi-Objective Optimization
R Lei, L Li, R Stolkin, B Feng - arXiv preprint arXiv:2410.05787, 2024 - arxiv.org
This paper addresses the challenge of dynamic multi-objective optimization problems
(DMOPs) by introducing novel approaches for accelerating prediction strategies within the …
(DMOPs) by introducing novel approaches for accelerating prediction strategies within the …
A NOVEL APPROACH TO MULTI-OBJECTIVE OPTIMIZATION USING SIMILARITY MEASURES AND ENSEMBLE LEARNING
D Beeram - … OF ADVANCED RESEARCH IN ENGINEERING AND …, 2024 - mylib.in
Most multi-objective optimization faces multiple-objective problems, such that more than one
goal will only be achieved concurrently. Consequently, multi-objective optimization can be …
goal will only be achieved concurrently. Consequently, multi-objective optimization can be …