Dynamic multi-objective evolutionary algorithm based on knowledge transfer
L Wu, D Wu, T Zhao, X Cai, L Xie - Information Sciences, 2023 - Elsevier
Dynamic multi-objective optimization problems (DMOPs) are mainly reflected in objective
changes with changes in the environment. To solve DMOPs, a transfer learning (TL) …
changes with changes in the environment. To solve DMOPs, a transfer learning (TL) …
Review of the research landscape of multi-criteria evaluation and benchmarking processes for many-objective optimization methods: coherent taxonomy, challenges …
RT Mohammed, R Yaakob, AA Zaidan… - … Journal of Information …, 2020 - World Scientific
Evaluation and benchmarking of many-objective optimization (MaOO) methods are
complicated. The rapid development of new optimization algorithms for solving problems …
complicated. The rapid development of new optimization algorithms for solving problems …
A multi-objective multi-verse optimizer algorithm to solve environmental and economic dispatch
W Xu, X Yu - Applied Soft Computing, 2023 - Elsevier
The combustion and emission of coal have always been a concern. A class of multi-
objective Environmental Economic Dispatch (EED) problems has been widely studied to …
objective Environmental Economic Dispatch (EED) problems has been widely studied to …
Does preference always help? A holistic study on preference-based evolutionary multiobjective optimization using reference points
The ultimate goal of multiobjective optimization is to help a decision maker (DM) identify
solution (s) of interest (SOI) achieving satisfactory tradeoffs among multiple conflicting …
solution (s) of interest (SOI) achieving satisfactory tradeoffs among multiple conflicting …
DeepSQLi: Deep semantic learning for testing SQL injection
Security is unarguably the most serious concern for Web applications, to which SQL
injection (SQLi) attack is one of the most devastating attacks. Automatically testing SQLi …
injection (SQLi) attack is one of the most devastating attacks. Automatically testing SQLi …
Batch Bayesian optimization with adaptive batch acquisition functions via multi-objective optimization
Bayesian optimization (BO) is a powerful method for solving expensive black-box
optimization problems, and it determines the candidate solutions for expensive evaluation …
optimization problems, and it determines the candidate solutions for expensive evaluation …
BiLO-CPDP: Bi-level programming for automated model discovery in cross-project defect prediction
Cross-Project Defect Prediction (CPDP), which borrows data from similar projects by
combining a transfer learner with a classifier, have emerged as a promising way to predict …
combining a transfer learner with a classifier, have emerged as a promising way to predict …
Batched data-driven evolutionary multiobjective optimization based on manifold interpolation
Multiobjective optimization problems are ubiquitous in real-world science, engineering, and
design optimization problems. It is not uncommon that the objective functions are as a black …
design optimization problems. It is not uncommon that the objective functions are as a black …
Maximum angle evolutionary selection for many-objective optimization algorithm with adaptive reference vector
Z Xiong, J Yang, Z Zhao, Y Wang, Z Yang - Journal of Intelligent …, 2023 - Springer
How to maintain a good balance between convergence and diversity is particularly
important for the performance of the many-objective evolutionary algorithms. Especially, the …
important for the performance of the many-objective evolutionary algorithms. Especially, the …
A data-driven evolutionary transfer optimization for expensive problems in dynamic environments
Many real-world problems are computationally costly and the objective functions evolve over
time. Data-driven, aka surrogate-assisted, evolutionary optimization has been recognized as …
time. Data-driven, aka surrogate-assisted, evolutionary optimization has been recognized as …