Multi-strategy competitive-cooperative co-evolutionary algorithm and its application
X Zhou, X Cai, H Zhang, Z Zhang, T Jin, H Chen… - Information …, 2023 - Elsevier
In order to effectively solve multi-objective optimization problems (MOPs) and fully balance
uniformity and convergence, a multi-strategy competitive-cooperative co-evolutionary …
uniformity and convergence, a multi-strategy competitive-cooperative co-evolutionary …
A survey of evolutionary algorithms for multi-objective optimization problems with irregular Pareto fronts
Evolutionary algorithms have been shown to be very successful in solving multi-objective
optimization problems (MOPs). However, their performance often deteriorates when solving …
optimization problems (MOPs). However, their performance often deteriorates when solving …
A hybrid multi-objective evolutionary algorithm for solving an adaptive flexible job-shop rescheduling problem with real-time order acceptance and condition-based …
Production scheduling and maintenance planning are two of the most important tasks in the
modern manufacturing workshop. Meanwhile, due to the dynamic order arrival and real-time …
modern manufacturing workshop. Meanwhile, due to the dynamic order arrival and real-time …
Process knowledge-guided autonomous evolutionary optimization for constrained multiobjective problems
Various real-world problems can be attributed to constrained multiobjective optimization
problems (CMOPs). Although there are various solution methods, it is still very challenging …
problems (CMOPs). Although there are various solution methods, it is still very challenging …
[HTML][HTML] Secure Federated Evolutionary Optimization—A Survey
With the development of edge devices and cloud computing, the question of how to
accomplish machine learning and optimization tasks in a privacy-preserving and secure way …
accomplish machine learning and optimization tasks in a privacy-preserving and secure way …
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 …
A survey on knee-oriented multiobjective evolutionary optimization
Conventional multiobjective optimization algorithms (MOEAs) with or without preferences
are successful in solving multi-and many-objective optimization problems. However, a …
are successful in solving multi-and many-objective optimization problems. However, a …
A dual distance dominance based evolutionary algorithm with selection-replacement operator for many-objective optimization
W Zhang, J Liu, J Liu, Y Liu, S Tan - Expert Systems with Applications, 2024 - Elsevier
Most existing dominance relations give higher priority to convergence than diversity and
cannot offer reasonable selection pressure according to the evolution status. This easily …
cannot offer reasonable selection pressure according to the evolution status. This easily …
Robust Multi-objective optimal dispatching model for a novel island micro energy grid incorporating biomass waste energy conversion system, desalination and power …
L Ju, L Liu, Y Han, S Yang, G Li, X Lu, Y Liu, H Qiao - Applied Energy, 2023 - Elsevier
To realize renewable and self-sustainable energy supply in island region, based on
geographical characteristics with abundant renewable resources, an optimal model for …
geographical characteristics with abundant renewable resources, an optimal model for …