[HTML][HTML] Large-scale evolutionary optimization: A review and comparative study
Large-scale global optimization (LSGO) problems have widely appeared in various real-
world applications. However, their inherent complexity, coupled with the curse of …
world applications. However, their inherent complexity, coupled with the curse of …
Solving nonlinear equation systems based on evolutionary multitasking with neighborhood-based speciation differential evolution
Q Gu, S Li, Z Liao - Expert Systems with Applications, 2024 - Elsevier
Locating multiple roots of nonlinear equation systems (NESs) remains a challenging and
meaningful task in the numerical optimization community. Although a large number of NES …
meaningful task in the numerical optimization community. Although a large number of NES …
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 …
Multiobjective Many-Tasking Evolutionary Optimization using Diversified Gaussian-Based Knowledge Transfer
Multiobjective multitasking evolutionary algorithms have shown promising performance for
tackling a set of multiobjective optimization tasks simultaneously, as the optimization …
tackling a set of multiobjective optimization tasks simultaneously, as the optimization …
A survey on learnable evolutionary algorithms for scalable multiobjective optimization
Recent decades have witnessed great advancements in multiobjective evolutionary
algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these …
algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these …
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 …
An evolutionary multitasking algorithm with multiple filtering for high-dimensional feature selection
Recently, evolutionary multitasking (EMT) has been successfully used in the field of high-
dimensional classification. However, the generation of multiple tasks in the existing EMT …
dimensional classification. However, the generation of multiple tasks in the existing EMT …
Learning-aided evolutionary search and selection for scaling-up constrained multiobjective optimization
The existing constrained multiobjective evolutionary algorithms (CMOEAs) still have great
room for improvement in balancing populations convergence, diversity and feasibility on …
room for improvement in balancing populations convergence, diversity and feasibility on …
An evolutionary multitasking memetic algorithm for multi-objective distributed heterogeneous welding flow shop scheduling
The decomposable feature of operations in the welding shop scheduling scenario results in
a vast search space, posing challenges for the design of traditional optimization algorithms …
a vast search space, posing challenges for the design of traditional optimization algorithms …
Evolutionary Multitask Optimization with Lower Confidence Bound-based Solution Selection Strategy
Evolutionary multitasking (EMT) is an emerging research direction within the evolutionary
computation community, attempting to concurrently solve multiple optimization tasks by …
computation community, attempting to concurrently solve multiple optimization tasks by …