Population topology of particle swarm optimization (PSO) will directly affect the dissemination of optimal information during the evolutionary process and will have a significant impact on the performance of PSO. Classic static population topologies are usually used in PSO, such as fully connected topology, ring topology, star topology, and square topology. In this paper, the performance of PSO...

The article addresses combinatorial optimization schemes for design of multi-layer communication network topology. The paper consists of three parts: (a) description of system issues as basic requirements, trends (e.g., evolution, upgradeability), and a ”system space” for contemporary network design; (b) brief description of some basic combinatorial optimization problems as follows: multicriter...

We study the problem of how to distribute the training of large-scale deep learning models in the parallel computing environment. We propose a new distributed stochastic optimization method called Elastic Averaging SGD (EASGD). We analyze the convergence rate of the EASGD method in the synchronous scenario and compare its stability condition with the existing ADMM method in the round-robin sche...

In this paper, we propose a ooding method, called Switch-Aided Flooding (SAF), for use in ATM networks. SAF-based protocols take advantage of hardware-supported cell relay and cell duplication, characteristic of such networks, in order to reduce the time needed to disseminate changes in network topology and resource availability. SAF protocols use a spanning multipoint connection (SMC), which i...

Abstract The behavior of a Vehicle Ad hoc Network (VANETs) is extremely unpredictable due to the high mobility and random network topology inherent nature VANETs. Several problems, including frequent connection failures, scalability, multi-hop data transfer, loss, impact performance Transmission Control Protocols (TCP) in such wireless ad networks. This study proposes using zone-based routing w...

Modern high performance computers connect hundreds of thousands of endpoints and employ thousands of switches. This allows for a great deal of freedom in the design of the network topology. At the same time, due to the sheer numbers and complexity involved, it becomes more challenging to easily distinguish between promising and improper designs. With ever increasing line rates and advances in o...

We study the problem of how to distribute the training of large-scale deep learning models in the parallel computing environment. We propose a new distributed stochastic optimization method called Elastic Averaging SGD (EASGD). We analyze the convergence rate of the EASGD method in the synchronous scenario and compare its stability condition with the existing ADMM method in the round-robin sche...

Topology optimization is a powerful tool for global and multiscale design of structures, microstructures, and materials. The computational bottleneck of topology optimization is the solution of a large number of extremely ill-conditioned linear systems arising in the finite element analysis. Adaptive mesh refinement (AMR) is one efficient way to reduce the computational cost. We propose a new A...