Expensive optimization problem (EOP) widely exists in various significant real-world
applications. However, EOP requires expensive or even unaffordable costs for evaluating …

Complex continuous optimization problems widely exist nowadays due to the fast
development of the economy and society. Moreover, the technologies like Internet of things …

As an advanced artificial intelligence technique for solving learning problems, deep learning
(DL) has achieved great success in many real-world applications and attracted increasing …

In this article, a dynamic-neighborhood-based switching PSO (DNSPSO) algorithm is
proposed, where a new velocity updating mechanism is designed to adjust the personal …

Multimodal multiobjective problems (MMOPs) commonly arise in real-world situations where
distant solutions in decision space share a very similar objective value. Traditional …

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 …

Differential evolution (DE) is one of the most efficient evolutionary algorithms for solving
numerical optimization problems; however, it still suffers from premature convergence and …

Distributed differential evolution (DDE) is an efficient paradigm that adopts multiple
populations for cooperatively solving complex optimization problems. However, how to …

Due to the increasing complexity of optimization problems, distributed differential evolution
(DDE) has become a promising approach for global optimization. However, similar to the …

Large-scale optimization has become a significant and challenging research topic in the
evolutionary computation (EC) community. Although many improved EC algorithms have …