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Job Shop Scheduling Problem (JSSP) is a well-known NP-hard combinatorial optimization problem. In recent years, many scholars have proposed various metaheuristic algorithms to solve JSSP, playing an important role in solving small-scale JSSP. However, when the size of the problem increases, the algorithms usually take too much time to converge. In this paper, we propose a hybrid algorithm, namely EOSMA, which mixes the update strategy of Equilibrium Optimizer (EO) into Slime Mould Algorithm (SMA), adding Centroid Opposition-based Computation (COBC) in some iterations. The hybridization of EO with SMA makes a better balance between exploration and exploitation. The addition of COBC strengthens the exploration and exploitation, increases the diversity of the population, improves the convergence speed and convergence accuracy, and avoids falling into local optimum. In order to solve discrete problems efficiently, a Sort-Order-Index (SOI)-based coding method is proposed. In order to solve JSSP more efficiently, a neighbor search strategy based on a two-point exchange is added to the iterative process of EOSMA to improve the exploitation capability of EOSMA to solve JSSP. Then, it is utilized to solve 82 JSSP benchmark instances; its performance is evaluated compared to that of EO, Marine Predators Algorithm (MPA), Aquila Optimizer (AO), Bald Eagle Search (BES), and SMA. The experimental results and statistical analysis show that the proposed EOSMA outperforms other competing algorithms. [ABSTRACT FROM AUTHOR]

Blockchain has become one of the key techniques for the security of the industrial internet. However, the blockchain is vulnerable to FAW (Fork after Withholding) attacks. To protect the industrial internet from FAW attacks, this paper proposes a novel FAW attack protection algorithm (FAWPA) based on the behavior of blockchain miners. Firstly, FAWPA performs miner data preprocessing based on the behavior of the miners. Then, FAWPA proposes a behavioral reward and punishment mechanism and a credit scoring model to obtain cumulative credit value with the processed data. Moreover, we propose a miner's credit classification mechanism based on fuzzy C-means (FCM), which combines the improved Aquila optimizer (AO) with strong solving ability. That is, FAWPA combines the miner's accumulated credit value and multiple attack features as the basis for classification, and optimizes cluster center selection by simulating Aquila's predation behavior. It can improve the solution update mechanism in different optimization stages. FAWPA can realize the rapid classification of miners' credit levels by improving the speed of identifying malicious miners. To evaluate the protective effect of the target mining pool, FAWPA finally establishes a mining pool and miner revenue model under FAW attack. The simulation results show that FAWPA can thoroughly and efficiently detect malicious miners in the target mining pool. FAWPA also improves the recall rate and precision rate of malicious miner detection, and it improves the cumulative revenue of the target mining pool. The proposed algorithm performs better than ND, RSCM, AWRS, and ICRDS. [ABSTRACT FROM AUTHOR]

Currently, the world is still facing a COVID-19 (coronavirus disease 2019) classified as a highly infectious disease due to its rapid spreading. The shortage of X-ray machines may lead to critical situations and delay the diagnosis results, increasing the number of deaths. Therefore, the exploitation of deep learning (DL) and optimization algorithms can be advantageous in early diagnosis and COVID-19 detection. In this paper, we propose a framework for COVID-19 images classification using hybridization of DL and swarm-based algorithms. The MobileNetV3 is used as a backbone feature extraction to learn and extract relevant image representations as a DL model. As a swarm-based algorithm, the Aquila Optimizer (Aqu) is used as a feature selector to reduce the dimensionality of the image representations and improve the classification accuracy using only the most essential selected features. To validate the proposed framework, two datasets with X-ray and CT COVID-19 images are used. The obtained results from the experiments show a good performance of the proposed framework in terms of classification accuracy and dimensionality reduction during the feature extraction and selection phases. The Aqu feature selection algorithm achieves accuracy better than other methods in terms of performance metrics. [ABSTRACT FROM AUTHOR]