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 …

Prompt examination increases the chances of effective treatment of Diabetic Eye Disease
(DED) and reduces the likelihood of permanent deterioration of vision. A key tool commonly …

There is a new nature-inspired algorithm called salp swarm algorithm (SSA), due to its
simple framework, it has been widely used in many fields. But when handling some …

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 …

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

The key challenge of expensive optimization problems (EOP) is that evaluating the true
fitness value of the solution is computationally expensive. A common method to deal with …

By efficiently building and exploiting surrogates, data-driven evolutionary algorithms
(DDEAs) can be very helpful in solving expensive and computationally intensive problems …

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