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 …

Cloud workflow scheduling is a significant topic in both commercial and industrial
applications. However, the growing scale of workflow has made such a scheduling problem …

Niching techniques have been widely incorporated into evolutionary algorithms (EAs) for
solving multimodal optimization problems (MMOPs). However, most of the existing niching …

The generation and updating of solutions, eg, crossover and mutation, of many existing
evolutionary algorithms directly operate on decision variables. The operators are very time …

Computational intelligence (CI), including artificial neural network, fuzzy logic, and
evolutionary computation (EC), has rapidly developed nowadays. Especially, EC is a kind of …

This article deals with a practical distributed hybrid differentiation flowshop scheduling
problem (DHDFSP) for the first time, where manufacturing products to minimize makespan …

Artificial bee colony (ABC) algorithm is a relatively new paradigm of swarm intelligence
based optimization technique, which has attracted a lot of attention for its simple structure …

Convolution neural network (CNN) is a kind of powerful and efficient deep learning
approach that has obtained great success in many real-world applications. However, due to …

Multimodal optimization problems (MMOPs) require algorithms to locate multiple optima
simultaneously. When using evolutionary algorithms (EAs) to deal with MMOPs, an intuitive …