An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges
As the world moves towards industrialization, optimization problems become more
challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms …
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
Maximizing the classification accuracy and minimizing the number of selected features are
two primary objectives in feature selection, which is inherently a multiobjective task …
two primary objectives in feature selection, which is inherently a multiobjective task …
A survey on evolutionary computation for complex continuous optimization
Complex continuous optimization problems widely exist nowadays due to the fast
development of the economy and society. Moreover, the technologies like Internet of things …
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
Constrained multiobjective optimization problems (CMOPs) involve multiple objectives to be
optimized and various constraints to be satisfied, which challenges the evolutionary …
optimized and various constraints to be satisfied, which challenges the evolutionary …
A review on evolutionary multitask optimization: Trends and challenges
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 …
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
In this article, a novel multi-strategy adaptive selection-based dynamic multiobjective
optimization algorithm (MSAS-DMOA) is proposed, which adopts the non-inductive transfer …
optimization algorithm (MSAS-DMOA) is proposed, which adopts the non-inductive transfer …
A meta-knowledge transfer-based differential evolution for multitask optimization
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 …
(MTOPs). Many existing methods transfer task-specific knowledge, such as the high-quality …
Evolutionary computation for intelligent transportation in smart cities: A survey
As the population in cities continues to increase, large-city problems, including traffic
congestion and environmental pollution, have become increasingly serious. The …
congestion and environmental pollution, have become increasingly serious. The …
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 …
A bi-objective knowledge transfer framework for evolutionary many-task optimization
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 …
more than three tasks. Two significant issues in solving MaTOPs are measuring intertask …