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  • Open Access


    Synthesis Optimization of Piezo Driven Four Bar Mechanism Using Genetic Algorithm

    Laith Sawaqed1, Khaled S. Hatamleh1,2, Mohammad A. Jaradat1,2, Qais Khasawneh1,3

    Intelligent Automation & Soft Computing, Vol.24, No.3, pp. 507-515, 2018, DOI:10.31209/2018.100000039

    Abstract Over the past few years, there has been a growing demand to develop efficient precision mechanisms for fine moving applications. Therefore, several piezoelectric driven mechanisms have been proposed for such applications. In this work an optimal synthesis of a four-bar mechanism with three PEAs is proposed. Two evolutionary multi-objective Genetic Algorithms (GAs) are formulated and applied; A Genetic Algorithm Synthesis method (GAS) is first used to obtain a synthesis solution for the mechanism regardless of power consumption. Then another Genetic Algorithm Minimum Power Synthesis method (GAMPS) is used to obtain the synthesis solution of minimum More >

  • Open Access


    An Efficient Hybrid Algorithm for a Bi-objectives Hybrid Flow Shop Scheduling

    S. M. Mousavia, M. Zandiehb

    Intelligent Automation & Soft Computing, Vol.24, No.1, pp. 9-16, 2018, DOI:10.1080/10798587.2016.1261956

    Abstract This paper considers the problem of scheduling n independent jobs in g-stage hybrid flow shop environment. To address the realistic assumptions of the proposed problem, two additional traits were added to the scheduling problem. These include setup times, and the consideration of maximum completion time together with total tardiness as objective function. The problem is to determine a schedule that minimizes a convex combination of objectives. A procedure based on hybrid the simulated annealing; genetic algorithm and local search so-called HSA-GA-LS are proposed to handle this problem approximately. The performance of the proposed algorithm is More >

  • Open Access


    Identification of Crop Diseases Based on Improved Genetic Algorithm and Extreme Learning Machine

    Linguo Li1, 2, Lijuan Sun1, Jian Guo1, Shujing Li2, *, Ping Jiang3

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 761-775, 2020, DOI:10.32604/cmc.2020.010158

    Abstract As an indispensable task in crop protection, the detection of crop diseases directly impacts the income of farmers. To address the problems of low crop-disease identification precision and detection abilities, a new method of detection is proposed based on improved genetic algorithm and extreme learning machine. Taking five different typical diseases with common crops as the objects, this method first preprocesses the images of crops and selects the optimal features for fusion. Then, it builds a model of crop disease identification for extreme learning machine, introduces the hill-climbing algorithm to improve the traditional genetic algorithm, More >

  • Open Access


    Smart Contract Fuzzing Based on Taint Analysis and Genetic Algorithms

    Zaoyu Wei1, *, Jiaqi Wang2, Xueqi Shen1, Qun Luo1

    Journal of Quantum Computing, Vol.2, No.1, pp. 11-24, 2020, DOI:10.32604/jqc.2020.010815

    Abstract Smart contract has greatly improved the services and capabilities of blockchain, but it has become the weakest link of blockchain security because of its code nature. Therefore, efficient vulnerability detection of smart contract is the key to ensure the security of blockchain system. Oriented to Ethereum smart contract, the study solves the problems of redundant input and low coverage in the smart contract fuzz. In this paper, a taint analysis method based on EVM is proposed to reduce the invalid input, a dangerous operation database is designed to identify the dangerous input, and genetic algorithm More >

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