Multi-objective particle swarm optimization with adaptive strategies for feature selection
F Han, WT Chen, QH Ling, H Han - Swarm and Evolutionary Computation, 2021 - Elsevier
Feature selection is a multi-objective optimization problem since it has two conflicting
objectives: maximizing the classification accuracy and minimizing the number of the …
objectives: maximizing the classification accuracy and minimizing the number of the …
Adaptive multi-task optimization strategy for wastewater treatment process
HG Han, X Bai, Y Hou, JF Qiao - Journal of Process Control, 2022 - Elsevier
It is challenging to introduce an optimization strategy for enhancing the operational
performance of wastewater treatment process (WWTP) on account of its multi-task …
performance of wastewater treatment process (WWTP) on account of its multi-task …
Surrogate-assisted MOEA/D for expensive constrained multi-objective optimization
In this paper, an adaptive surrogate-assisted MOEA/D framework (ASA-MOEA/D) is
proposed for solving computationally expensive constrained multi-objective optimization …
proposed for solving computationally expensive constrained multi-objective optimization …
Dynamic transfer reference point-oriented MOEA/D involving local objective-space knowledge
The decomposition-based evolutionary algorithm (MOEA/D) has attained excellent
performance in solving optimization problems involving multiple conflicting objectives …
performance in solving optimization problems involving multiple conflicting objectives …
动态多目标优化进化算法研究进展
马永杰， 陈敏， 龚影， 程时升， 王甄延 - 自动化学报, 2020 - aas.net.cn
动态多目标优化问题(Dynamic multi-objective optimization problems, DMOPs)
已成为工程优化的研究热点, 其目标函数, 约束函数和相关参数都可能随时间不断变化 …
已成为工程优化的研究热点, 其目标函数, 约束函数和相关参数都可能随时间不断变化 …
A binary dandelion algorithm using seeding and chaos population strategies for feature selection
Y Zhao, J Dong, X Li, H Chen, S Li - Applied Soft Computing, 2022 - Elsevier
Feature selection (FS) is an important pre-processing step in data mining and pattern
recognition. It can effectively compress the dimensionality of the feature space to reduce …
recognition. It can effectively compress the dimensionality of the feature space to reduce …
A MOEA/D with global and local cooperative optimization for complicated bi-objective optimization problems
Q Wang, Q Gu, L Chen, Y Guo, N Xiong - Applied Soft Computing, 2023 - Elsevier
Multi-objective evolutionary algorithm based on decomposition has been well-recognized in
addressing bi-objective optimization problems. However, it is a serious challenge for …
addressing bi-objective optimization problems. However, it is a serious challenge for …
A reference vector based multiobjective evolutionary algorithm with Q-learning for operator adaptation
Maintaining a balance between convergence and diversity is a challenge for multiobjective
evolutionary optimization. As crossover operators can affect the offspring distribution, an …
evolutionary optimization. As crossover operators can affect the offspring distribution, an …
An enhanced decomposition-based multi-objective evolutionary algorithm with a self-organizing collaborative scheme
Y Zhu, Y Qin, D Yang, H Xu, H Zhou - Expert Systems with Applications, 2023 - Elsevier
The multi-objective evolutionary algorithm based on decomposition (MOEA/D) decomposes
a multi-objective optimization problem (MOP) into multiple single-objective subproblems …
a multi-objective optimization problem (MOP) into multiple single-objective subproblems …
Optimization of chemotherapy using hybrid optimal control and swarm intelligence
This study aimed to minimize the tumor cell population using minimal medicine for
chemotherapy treatment, while maintaining the effector-immune cell population at a healthy …
chemotherapy treatment, while maintaining the effector-immune cell population at a healthy …