Data aggregation involves the integration of correlated data generated by various wireless
sensors and devices in WSN and IoT networks, in order to arrive at meaningful interpretation …

This article proposes utilizing a single deep reinforcement learning model to solve
combinatorial multiobjective optimization problems. We use the well-known multiobjective …

In many-objective optimization problems (MaOPs), forming sound tradeoffs between
convergence and diversity for the environmental selection of evolutionary algorithms is a …

Locating more peaks and refining the solution accuracy on the found peaks are two
challenging issues in solving multimodal optimization problems (MMOPs). To deal with …

Nowadays, fog computing as a complementary facility of cloud computing has attracted
great attentions in research communities because it has extraordinary potential to provide …

Surrogate-assisted evolutionary algorithms (SAEAs) have proven to be effective in solving
computationally expensive optimization problems (EOPs). However, the performance of …

Recent years, the multi-objective evolutionary algorithm based on decomposition (MOEA/D)
has been researched and applied for numerous optimization problems. In this study, we …

Ratio error (RE) estimation of the voltage transformers (VTs) plays an important role in
modern power delivery systems. Existing RE estimation methods mainly focus on periodical …

With the popularity of “flipped classrooms,” teachers pay more attention to cultivating
students' autonomous learning ability while imparting knowledge. Inspired by this, this paper …

In dealing with the expensive multiobjective optimization problem, some algorithms convert
it into a number of single-objective subproblems for optimization. At each iteration, these …