Geyser inspired algorithm: a new geological-inspired meta-heuristic for real-parameter and constrained engineering optimization
Over the past years, many efforts have been accomplished to achieve fast and accurate
meta-heuristic algorithms to optimize a variety of real-world problems. This study presents a …
meta-heuristic algorithms to optimize a variety of real-world problems. This study presents a …
Dynamic auxiliary task-based evolutionary multitasking for constrained multiobjective optimization
When solving constrained multiobjective optimization problems (CMOPs), the utilization of
infeasible solutions significantly affects algorithm's performance because they not only …
infeasible solutions significantly affects algorithm's performance because they not only …
A self-adaptive evolutionary multi-task based constrained multi-objective evolutionary algorithm
Constrained multi-objective optimization problems (CMOPs) are difficult to solve since they
involve the optimization of multiple objectives and the satisfaction of various constraints …
involve the optimization of multiple objectives and the satisfaction of various constraints …
Optimizing deep transfer networks with fruit fly optimization for accurate diagnosis of diabetic retinopathy
M Wang, Q Gong, H Chen, G Gao - Applied Soft Computing, 2023 - Elsevier
It is crucial to develop a smart analytics system capable of accurately diagnosing diabetic
retinopathy. This research uses a new deep transfer network framework to diagnose …
retinopathy. This research uses a new deep transfer network framework to diagnose …
Constrained multiobjective optimization via multitasking and knowledge transfer
Solving constrained multiobjective optimization problems (CMOPs) with various features
and challenges via evolutionary algorithms is very popular. Existing methods usually adopt …
and challenges via evolutionary algorithms is very popular. Existing methods usually adopt …
A multipopulation evolutionary algorithm using new cooperative mechanism for solving multiobjective problems with multiconstraint
In science and engineering, multiobjective optimization problems (MOPs) usually contain
multiple complex constraints, which poses a significant challenge in obtaining the optimal …
multiple complex constraints, which poses a significant challenge in obtaining the optimal …
Dynamic mechanism-assisted artificial bee colony optimization for image segmentation of COVID-19 chest X-ray
J Chen, Z Cai, AA Heidari, L Liu, H Chen, J Pan - Displays, 2023 - Elsevier
The artificial bee colony optimization (ABC) algorithm operates efficiently and converges
well but still suffers from the problem of easily falling into local optimum, and there is room …
well but still suffers from the problem of easily falling into local optimum, and there is room …
Evolutionary constrained multiobjective optimization: Scalable high-dimensional constraint benchmarks and algorithm
Evolutionary constrained multiobjective optimization has received extensive attention and
research in the past two decades, and a lot of benchmarks have been proposed to test the …
research in the past two decades, and a lot of benchmarks have been proposed to test the …
Adaptive auxiliary task selection for multitasking-assisted constrained multi-objective optimization [feature]
Solving constrained multi-objective optimization problems (CMOPs) is one of the most
popular research topics in the multi-objective optimization community. Various approaches …
popular research topics in the multi-objective optimization community. Various approaches …
Multitasking optimization via an adaptive solver multitasking evolutionary framework
Multitasking optimization (MTO) aims to solve multiple tasks simultaneously in a single run.
Many multitasking evolutionary algorithms (MTEAs) have been developed in recent years for …
Many multitasking evolutionary algorithms (MTEAs) have been developed in recent years for …