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  • Open Access


    Hybrid Optimization Based PID Controller Design for Unstable System

    Saranya Rajeshwaran1,*, C. Agees Kumar2, Kanthaswamy Ganapathy3

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1611-1625, 2023, DOI:10.32604/iasc.2023.029299

    Abstract PID controllers play an important function in determining tuning parameters in any process sector to deliver optimal and resilient performance for nonlinear, stable and unstable processes. The effectiveness of the presented hybrid metaheuristic algorithms for a class of time-delayed unstable systems is described in this study when applicable to the problems of PID controller and Smith PID controller. The Direct Multi Search (DMS) algorithm is utilised in this research to combine the local search ability of global heuristic algorithms to tune a PID controller for a time-delayed unstable process model. A Metaheuristics Algorithm such as, SA (Simulated Annealing), MBBO (Modified… More >

  • Open Access


    THD Reduction for Permanent Magnet Synchronous Motor Using Simulated Annealing

    R. Senthil Rama1, C. R. Edwin Selva Rex2, N. Herald Anantha Rufus3,*, J. Annrose4

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2325-2336, 2023, DOI:10.32604/iasc.2023.028930

    Abstract Any nonlinear behavior of the system is analyzed by a useful way of Total Harmonic Distortion (THD) technique. Reduced THD achieves lower peak current, higher efficiency and longer equipment life span. Simulated annealing (SA) is applied due to the effectiveness of locating solutions that are close to ideal and to challenge large-scale combinatorial optimization for Permanent Magnet Synchronous Machine (PMSM). The parameters of direct torque controllers (DTC) for the drive are automatically adjusted by the optimization algorithm. Advantages of the PI-Fuzzy-SA algorithm are retained when used together. It also improves the rate of system convergence. Speed response improvement and harmonic… More >

  • Open Access


    A Truck Scheduling Problem for Multi-Crossdocking System with Metaheuristics

    Phan Nguyen Ky Phuc1, Nguyen Van Thanh2,*, Duong Bao Tram1

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5165-5178, 2022, DOI:10.32604/cmc.2022.027967

    Abstract The cross-docking is a very important subject in logistics and supply chain managements. According to the definition, cross-docking is a process dealing with transhipping inventory, in which goods and products are unloaded from an inbound truck and process through a flow-center to be directly loaded onto an outbound truck. Cross-docking is favored due to its advantages in reducing the material handing cost, the needs to store the product in warehouse, as well decreasing the labor cost by eliminating packaging, storing, pick-location and order picking. In cross-docking, products can be consolidated and transported as a full load, reducing overall distribution costs.… More >

  • Open Access


    Annealing Harmony Search Algorithm to Solve the Nurse Rostering Problem

    Mohammed Hadwan1,2,3,*

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5545-5559, 2022, DOI:10.32604/cmc.2022.024512

    Abstract A real-life problem is the rostering of nurses at hospitals. It is a famous nondeterministic, polynomial time (NP) -hard combinatorial optimization problem. Handling the real-world nurse rostering problem (NRP) constraints in distributing workload equally between available nurses is still a difficult task to achieve. The international shortage of nurses, in addition to the spread of COVID-19, has made it more difficult to provide convenient rosters for nurses. Based on the literature, heuristic-based methods are the most commonly used methods to solve the NRP due to its computational complexity, especially for large rosters. Heuristic-based algorithms in general have problems striking the… More >

  • Open Access


    Sustainable Waste Collection Vehicle Routing Problem for COVID-19

    G. Niranjani1,*, K. Umamaheswari2

    Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 457-472, 2022, DOI:10.32604/iasc.2022.024264

    Abstract COVID-19 pandemic has imposed many threats. One among them is the accumulation of waste in hospitals. Waste should be disposed regularly and safely using sustainable methods. Sustainability is self development with preservation of society and its resources. The main objective of this research is to achieve sustainability in waste collection by minimizing the cost factor. Minimization of sustainable-cost involves minimization of three sub-components – total travel-cost representing economical component, total emission-cost representing environmental component and total driver-allowance-cost representing social component. Most papers under waste collection implement Tabu search algorithm and fail to consider the environmental and social aspects involved. We… More >

  • Open Access


    A New Optimized Wrapper Gene Selection Method for Breast Cancer Prediction

    Heyam H. Al-Baity*, Nourah Al-Mutlaq

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3089-3106, 2021, DOI:10.32604/cmc.2021.015291

    Abstract Machine-learning algorithms have been widely used in breast cancer diagnosis to help pathologists and physicians in the decision-making process. However, the high dimensionality of genetic data makes the classification process a challenging task. In this paper, we propose a new optimized wrapper gene selection method that is based on a nature-inspired algorithm (simulated annealing (SA)), which will help select the most informative genes for breast cancer prediction. These optimal genes will then be used to train the classifier to improve its accuracy and efficiency. Three supervised machine-learning algorithms, namely, the support vector machine, the decision tree, and the random forest… More >

  • Open Access


    An Improved Algorithm of K-means Based on Evolutionary Computation

    Yunlong Wang1,2,3, Xiong Luo1,2,4,*, Jing Zhang1,2,3, Zhigang Zhao1, Jun Zhang5

    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 961-971, 2020, DOI:10.32604/iasc.2020.010128

    Abstract K-means is a simple and commonly used algorithm, which is widely applied in many fields due to its fast convergence and distinctive performance. In this paper, a novel algorithm is proposed to help K-means jump out of a local optimum on the basis of several ideas from evolutionary computation, through the use of random and evolutionary processes. The experimental results show that the proposed algorithm is capable of improving the accuracy of K-means and decreasing the SSE of K-means, which indicates that the proposed algorithm can prevent K-means from falling into the local optimum to some extent. More >

  • Open Access


    Robust Hybrid Artificial Fish Swarm Simulated Annealing Optimization Algorithm for Secured Free Scale Networks against Malicious Attacks

    Ganeshan Keerthana1,*, Panneerselvam Anandan2, Nandhagopal Nachimuthu3

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 903-917, 2021, DOI:10.32604/cmc.2020.012255

    Abstract Due to the recent proliferation of cyber-attacks, highly robust wireless sensor networks (WSN) become a critical issue as they survive node failures. Scale-free WSN is essential because they endure random attacks effectively. But they are susceptible to malicious attacks, which mainly targets particular significant nodes. Therefore, the robustness of the network becomes important for ensuring the network security. This paper presents a Robust Hybrid Artificial Fish Swarm Simulated Annealing Optimization (RHAFS-SA) Algorithm. It is introduced for improving the robust nature of free scale networks over malicious attacks (MA) with no change in degree distribution. The proposed RHAFS-SA is an enhanced… More >

  • Open Access


    Self-Organizing Gaussian Mixture Map Based on Adaptive Recursive Bayesian Estimation

    He Ni1,*, Yongqiao Wang1, Buyun Xu2

    Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 227-236, 2020, DOI:10.31209/2019.100000068

    Abstract The paper presents a probabilistic clustering approach based on self-organizing learning algorithm and recursive Bayesian estimation. The model is built upon the principle that the market data space is multimodal and can be described by a mixture of Gaussian distributions. The model parameters are approximated by a stochastic recursive Bayesian learning: searches for the maximum a posterior solution at each step, stochastically updates model parameters using a “dualneighbourhood” function with adaptive simulated annealing, and applies profile likelihood confidence interval to avoid prolonged learning. The proposed model is based on a number of pioneer works, such as Mixture Gaussian Autoregressive Model,… 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 compared with a genetic algorithm… More >

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