Context: Routing algorithms play a crucial role in the performance of Network-on-Chip (NoC)-based Multi-Processor Systems-on-Chip (MPSoC). However, the selection of appropriate and effective routing algorithms poses a challenge for designers, given the multitude of assessment criteria, data fluctuations, and varying criteria importance. Objective: In this study, we propose a comprehensive assessment of various routing algorithms, aiming to identify the most suitable and effective routing algorithm that satisfies designers’ system-level requirements and assessment criteria. Method: This research integrates the Fuzzy-Weighted Zero-Inconsistency (FWZIC) method and the Fuzzy Decision by Opinion Score Method (FDOSM). The utilisation of the Z-Cloud Rough Numbers (ZCRNs) environment addresses the challenge of two types of uncertainty, providing a framework for managing ambiguity in the data and achieving a higher level of data freedom. Our methodology consists of two main phases. Firstly, the decision matrix is constructed based on the performance assessment criteria and routing algorithms. Secondly, we employ the ZCR-FWZIC method to derive the weights for each criterion and subsequently employ the ZCR-FDOSM-BM approach to rank the routing algorithms. Result: The analysis reveals that Adaptive Dimensional Bubble Routing (ADBR), Message-based Congestion-Aware Routing (MCAR), and Dynamic and Adaptive Routing Algorithm (DyAd) are ranked as the top three routing algorithms, respectively. Conclusion: This research presents essential implications for designers and system engineers involved in NoC-based MPSoC, offering insights to enhance decision-making processes and facilitate the selection of an appropriate routing algorithm.

Context: Network-on-Chips (NoCs) serve as essential interconnection infrastructures in Multi-processor System-on-Chip (MPSoC) designs, emphasizing flexibility, extensibility, and low power consumption. The effectiveness of communication within NoCs relies heavily on the routing algorithm employed. However, the routing process faces significant challenges, such as deadlock, livelock, congestion, and faults, which impact the Design Space Exploration (DSE) process. Objective: In this research, we propose a prediction model-based ANN with a Metaheuristic Optimization approach for predicting the utilized routing algorithm by the NoC-based MPSoC platform during the DSE in order to reduce the time required to specify the NoC-based MPSoC platform configurations. Method: The main idea of the proposed method is to develop a prediction model, specifically an Artificial Neural Network (ANN) optimized using the Guaranteed Convergence Arithmetic Optimization Algorithm (GCAOA-ANN), for predicting the utilized routing algorithm in NoC-based MPSoC platform during the DSE process. The methodology consists of two phases. Firstly, an automated strategy is proposed to execute and control the NOXIM simulator, enabling the simulation and collection of NoC-based MPSoC scenarios under different constraints, rules of NoC design (including routing protocols), and performance metrics. Consequently, NoC traffic data is gathered. Secondly, building upon the work of previous researchers, who have suggested the development of hybrid models to improve accuracy, this research introduces a novel methodology incorporating an ANN optimized using the GCAOA-ANN to predict the routing algorithm employed by the NoC-based MPSoC. Results: The hybrid GCAOA-ANN model demonstrated superior performance compared to other models, including PSOGW-ANN, SMA-ANN, AOA-ANN, Random Forest, and ANN. Where the learning rate and the number of nodes in the first and second hidden layers were: (0.0023, 1, 20) for the GCAOA-ANN model. In addition, the evaluation metrics of the GCAOA-ANN model showed excellent results, with an R2 value of (99.80%), an RMSE of (0.0000135 m3/s), an MSE of (0.0000118 m3/s), an SI of (0.000077), an MBE of (0.0000028), and a MAR of (0.000187616 m3/s). Conclusion: The outcomes of this research are anticipated to offer a guide for individuals interested in conducting research on On-Chip communication routing protocols and DSE in NoC-based MPSoCs in the context of IoT using ML approaches, while the GCAOA-ANN has the potential to generalize to new problems.

A hybrid machine learning (ML) model is becoming a common trend in predicting reference evapotranspiration (ETo) research. This study aims to systematically review ML models that are integrated with meta-heuristic algorithms (i.e., parameter optimisation-based hybrid models, OBH) for predicting ETo data. Over five years, from 2018–2022, the articles published in three reliable databases, including Web of Science, ScienceDirect, and IEEE Xplore, were considered. According to the protocol search, 1485 papers were selected. After three filters were applied, the final set contained 33 papers related to the nominated topic. The final set of papers was categorised into five groups. The first group, swarm intelligence-based algorithms, had the highest proportion of papers, (23/33) and was superior to all other algorithms. The second group (evolution computation-based algorithms), third group (physics-based algorithms), fourth group (hybrid-based algorithms), and fifth group (reviews and surveys) had (4/33), (1/33), (2/33), and (3/33), respectively. However, researchers have not treated OBH models in much detail, and there is still room for improvement by investigating both newly single and hybrid meta-heuristic algorithms. Finally, this study hopes to assist researchers in understanding the options and gaps in this line of research.

Determining criteria weights is a frequent issue in multi-criteria decision-making (MCDM) techniques. The weight given to various criteria can have a significant impact on a decision’s outcome. As a result, researchers developed numerous methods for determining the criteria’s weights. Weighting methods can be objective, subjective, or integrated. In this study, the “Opinion Weight Criteria Method” (OWCM) has been proposed as a new weighting method to specify the criteria weights by integrating the concepts of objective and subjective weighing. OWCM is keen to extract the weight based on the decision-maker preferences with zero inconsistency. Some computational analyses were presented to confirm the efficiency of the OWCM. First, an example of the OWCM’s procedure for calculating the weights for each criterion is provided. Second, the validity of the new method is provided. Finally, the evaluation and validation are presented to show the power of the new method. The management implications of the OWCM method highlight its capacity to improve decision-making by eliminating inconsistencies, aligning with decision-maker preferences, and fostering transparency. The conducted analyses demonstrate that the OWCM is efficient in determining the criteria weights.