An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

K Rajwar, K Deep, S Das - Artificial Intelligence Review, 2023 - Springer
As the world moves towards industrialization, optimization problems become more
challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms …

A survey on evolutionary multiobjective feature selection in classification: approaches, applications, and challenges

R Jiao, BH Nguyen, B Xue… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Maximizing the classification accuracy and minimizing the number of selected features are
two primary objectives in feature selection, which is inherently a multiobjective task …

A survey on evolutionary computation for complex continuous optimization

ZH Zhan, L Shi, KC Tan, J Zhang - Artificial Intelligence Review, 2022 - Springer
Complex continuous optimization problems widely exist nowadays due to the fast
development of the economy and society. Moreover, the technologies like Internet of things …

Utilizing the relationship between unconstrained and constrained Pareto fronts for constrained multiobjective optimization

J Liang, K Qiao, K Yu, B Qu, C Yue… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Constrained multiobjective optimization problems (CMOPs) involve multiple objectives to be
optimized and various constraints to be satisfied, which challenges the evolutionary …

A review on evolutionary multitask optimization: Trends and challenges

T Wei, S Wang, J Zhong, D Liu… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Evolutionary algorithms (EAs) possess strong problem-solving abilities and have been
applied in a wide range of applications. However, they still suffer from a high computational …

A novel dynamic multiobjective optimization algorithm with non-inductive transfer learning based on multi-strategy adaptive selection

H Li, Z Wang, C Lan, P Wu… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
In this article, a novel multi-strategy adaptive selection-based dynamic multiobjective
optimization algorithm (MSAS-DMOA) is proposed, which adopts the non-inductive transfer …

A meta-knowledge transfer-based differential evolution for multitask optimization

JY Li, ZH Zhan, KC Tan, J Zhang - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Knowledge transfer plays a vastly important role in solving multitask optimization problems
(MTOPs). Many existing methods transfer task-specific knowledge, such as the high-quality …

Evolutionary computation for intelligent transportation in smart cities: A survey

ZG Chen, ZH Zhan, S Kwong… - IEEE Computational …, 2022 - ieeexplore.ieee.org
As the population in cities continues to increase, large-city problems, including traffic
congestion and environmental pollution, have become increasingly serious. The …

Constrained multiobjective optimization via multitasking and knowledge transfer

F Ming, W Gong, L Wang, L Gao - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Solving constrained multiobjective optimization problems (CMOPs) with various features
and challenges via evolutionary algorithms is very popular. Existing methods usually adopt …

A bi-objective knowledge transfer framework for evolutionary many-task optimization

Y Jiang, ZH Zhan, KC Tan… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Many-task problem (MaTOP) is a kind of challenging multitask optimization problem with
more than three tasks. Two significant issues in solving MaTOPs are measuring intertask …