A new meta-heuristic method is proposed to enhance current meta-heuristic methods for global optimization and test scheduling for three-dimensional (3D) stacked system-on-chip (SoC) by hybridizing grey wolf optimization with differential evolution (HGWO). Because basic grey wolf optimization (GWO) is easy to fall into stagnation when it carries out the operation of attacking prey, and differential evolution (DE) is integrated into GWO to update the previous best position of grey wolf Alpha, Beta and Delta, in order to force GWO to jump out of the stagnation with DE's strong searching ability. The proposed algorithm can accelerate the convergence speed of GWO and improve its performance. Twenty-three well-known benchmark functions and an NP hard problem of test scheduling for 3D SoC are employed to verify the performance of the proposed algorithm. Experimental results show the superior performance of the proposed algorithm for exploiting the optimum and it has advantages in terms of exploration.

We propose a new meta-heuristic algorithm named Levy flights multi-verse optimizer (LFMVO), which incorporates Levy flights into multi-verse optimizer (MVO) algorithm to solve numerical and engineering optimization problems. The Original MVO easily falls into stagnation when wormholes stochastically re-span a number of universes (solutions) around the best universe achieved over the course of iterations. Since Levy flights are superior in exploring unknown, large-scale search space, they are integrated into the previous best universe to force MVO out of stagnation. We test this method on three sets of 23 well-known benchmark test functions and an NP complete problem of test scheduling for Network-on-Chip (NoC). Experimental results prove that the proposed LFMVO is more competitive than its peers in both the quality of the resulting solutions and convergence speed.

No abstract

No abstract

No abstract

An optical router is an essential component of a photonic network-on-chip (PNoC). Normally, an optical router consists of traditional optical elements such as the micro-ring resonator (MRR) and the Mach-Zehnder interferometer (MZI). This type of router has many disadvantages, such as a large size, lack of thermal stability and low speed, although their manufacturing technologies are mature. In this paper, we propose a full duplex 5×5 optical router based on a hybrid photonic-plasmonic switch (HPPS). The HPPS has the advantages of compactness, thermal stability and high speed, which can effectively solve the problems of traditional optical routers. In this work, each optical communication link in the optical router is independent, and each optical communication link no longer shares the same switch, which avoids blocking between channels and achieves full-duplex communication. The modelling of the optical router using the HPPS is performed through MATLAB as well as by a finite-difference-time-domain (FDTD) simulation. The maximum and average insertion losses (ILs) of the router are 5.4 dB and 3.5 dB, respectively, and the router has a fast switching time (100 ps). The results show that this optical router has the advantages of low loss and low energy consumption and provides a 5×5 full-duplex optical router for the PNoC.

In order to solve the existing problems of time‐interleaved data acquisition system's poor scalability, limited acquisition channels, and complicated clock system based on System on Chip(SoC), this work presents a novel method of high‐speed data acquisition based on Network on Chip (NoC) communication architecture and time‐interleaved principle. Six analog‐to‐digital data acquisition resource nodes are hooked up to the NoC according to the unified features of NoC router interface. The data acquisition controller controls the time‐interleaved acquisition's timing sequences and realizes the remote transmission of data through two Gigabit Ethernet resource nodes. Adding timestamp to the collected data of each channel can recover the waveform signal accurately. The experiment results show that the combination of NoC communication architecture and time‐interleaved data acquisition has a certain innovative significance.

Political optimizer (PO) is a relatively state-of-the-art meta-heuristic optimization technique for global optimization problems, as well as real-world engineering optimization, which mimics the multi-staged process of politics in human society. However, due to a greedy strategy during the election phase, and an inappropriate balance of global exploration and local exploitation during the party switching stage, it suffers from stagnation in local optima with a low convergence accuracy. To overcome such drawbacks, a sequence of novel PO variants were proposed by integrating PO with Quadratic Interpolation, Advance Quadratic Interpolation, Cubic Interpolation, Lagrange Interpolation, Newton Interpolation, and Refraction Learning (RL). The main contributions of this work are listed as follows. (1) The interpolation strategy was adopted to help the current global optima jump out of local optima. (2) Specifically, RL was integrated into PO to improve the diversity of the population. (3) To improve the ability of balancing exploration and exploitation during the party switching stage, a logistic model was proposed to maintain a good balance. To the best of our knowledge, PO combined with the interpolation strategy and RL was proposed here for the first time. The performance of the best PO variant was evaluated by 19 widely used benchmark functions and 30 test functions from the IEEE CEC 2014. Experimental results revealed the superior performance of the proposed algorithm in terms of exploration capacity.