Liang Zeng^1,2, Junyang Shi¹, Yanyan Li¹, Shanshan Wang^1,2,*, Weigang Li³

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 375-392, 2024, DOI:10.32604/cmc.2023.045803

Abstract The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing systems. It involves determining the optimal execution sequences for a set of jobs on various machines to maximize production efficiency and meet multiple objectives. The Non-dominated Sorting Genetic Algorithm III (NSGA-III) is an effective approach for solving the multi-objective job shop scheduling problem. Nevertheless, it has some limitations in solving scheduling problems, including inadequate global search capability, susceptibility to premature convergence, and challenges in balancing convergence and diversity. To enhance its performance, this paper introduces a strengthened dominance relation NSGA-III algorithm based on differential evolution… More >

Yinxue Ao¹, Jian Lv^1,*, Qingsheng Xie¹, Zhengming Zhang²

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3049-3074, 2023, DOI:10.32604/cmc.2023.040088

Abstract A second-generation fast Non-dominated Sorting Genetic Algorithm product shape multi-objective imagery optimization model based on degradation (DNSGA-II) strategy is proposed to make the product appearance optimization scheme meet the complex emotional needs of users for the product. First, the semantic differential method and K-Means cluster analysis are applied to extract the multi-objective imagery of users; then, the product multidimensional scale analysis is applied to classify the research objects, and again the reference samples are screened by the semantic differential method, and the samples are parametrized in two dimensions by using elliptic Fourier analysis; finally, the fuzzy dynamic evaluation function is… More >

Caiyun Liu, Peng Liu^*

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3221-3242, 2023, DOI:10.32604/iasc.2023.035114

Abstract Shared manufacturing is recognized as a new point-to-point manufacturing mode in the digital era. Shared manufacturing is referred to as a new manufacturing mode to realize the dynamic allocation of manufacturing tasks and resources. Compared with the traditional mode, shared manufacturing offers more abundant manufacturing resources and flexible configuration options. This paper proposes a model based on the description of the dynamic allocation of tasks and resources in the shared manufacturing environment, and the characteristics of shared manufacturing resource allocation. The execution of manufacturing tasks, in which candidate manufacturing resources enter or exit at various time nodes, enables the dynamic… More >

Lei Xing¹, Yakui Liu^2,3,*

Energy Engineering, Vol.120, No.4, pp. 1023-1043, 2023, DOI:10.32604/ee.2023.025335

Abstract The optimal allocation of integrated energy system capacity based on the heuristic algorithms can reduce economic costs and achieve maximum consumption of renewable energy, which has attracted many attentions. However, the optimization results of heuristic algorithms are usually influenced by the choice of hyperparameters. To solve the above problem, the particle swarm algorithm is introduced to find the optimal hyperparameters of the heuristic algorithms. Firstly, an integrated energy system consisting of the photovoltaic, wind turbine, electrolysis cell, hydrogen storage tank, and energy storage is established. Meanwhile, the minimum economic cost, the maximum wind and PV power consumption rate, and the… More >

P. Anitha¹, K. Karthik Kumar^2,*, M. Ravindran², A. Saravanaselvan²

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1377-1397, 2022, DOI:10.32604/iasc.2022.025396

Abstract In this paper, efficient charging of electric vehicle battery from a considered renewable solar photovoltaic source with the help of a modified Z source with efficient boosting topology. Adapting this Z-source converter to act as a voltage gainer with a boosting function allows a solar Photovoltaic (PV) input voltage of 25VDC (Volts Direct Current) to be increased to a designed output voltage of 75VDC at a low duty ratio, resulting in minimal switching loss. The closed-loop steady-state and transient parameters at the output were analyzed and compared using modern evolutionary algorithms. The power range upheld throughout the circuit is around… More >

Mahmoud Awad¹, Mohamed Abouhawwash^1,2,*, H. N. Agiza¹

Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1893-1904, 2022, DOI:10.32604/iasc.2022.023510

Abstract To solve single and multi-objective optimization problems, evolutionary algorithms have been created. We use the non-dominated sorting genetic algorithm (NSGA-II) to find the Pareto front in a two-objective portfolio query, and its extended variant NSGA-III to find the Pareto front in a three-objective portfolio problem, in this article. Furthermore, in both portfolio problems, we quantify the Karush-Kuhn-Tucker Proximity Measure (KKTPM) for each generation to determine how far we are from the effective front and to provide knowledge about the Pareto optimal solution. In the portfolio problem, looking for the optimal set of stock or assets that maximizes the mean return… More >

N. Ganesh¹, Uvaraja Ragavendran², Kanak Kalita^3,*, Paras Jain⁴, Xiao-Zhi Gao⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.2, pp. 443-464, 2021, DOI:10.32604/cmes.2021.014960

Abstract Optimizing the performance of composite structures is a real-world application with significant benefits. In this paper, a high-fidelity finite element method (FEM) is combined with the iterative improvement capability of metaheuristic optimization algorithms to obtain optimized composite plates. The FEM module comprises of ninenode isoparametric plate bending element in conjunction with the first-order shear deformation theory (FSDT). A recently proposed memetic version of particle swarm optimization called RPSOLC is modified in the current research to carry out multi-objective Pareto optimization. The performance of the MO-RPSOLC is found to be comparable with the NSGA-III. This work successfully highlights the use of… More >

Kalaipriyan Thirugnansambandam¹, Debnath Bhattacharyya², Jaroslav Frnda³, Dinesh Kumar Anguraj², Jan Nedoma^4,*

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3629-3644, 2021, DOI:10.32604/cmc.2021.018939

Abstract In Wireless Sensor Network (WSN), coverage and connectivity are the vital challenges in the target-based region. The linear objective is to find the positions to cover the complete target nodes and connectivity between each sensor for data forwarding towards the base station given a grid with target points and a potential sensor placement position. In this paper, a multiobjective problem on target-based WSN (t-WSN) is derived, which minimizes the number of deployed nodes, and maximizes the cost of coverage and sensing range. An Evolutionary-based Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) is incorporated to tackle this multiobjective problem efficiently. Multiobjective problems are… More >

Kamlendra Vikram¹, Uvaraja Ragavendran², Kanak Kalita^1,*, Ranjan Kumar Ghadai³, Xiao-Zhi Gao⁴

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1771-1784, 2021, DOI:10.32604/cmc.2020.013946

Abstract In this work, diamond-like carbon (DLC) thin film coatings are deposited on silicon substrates by using plasma-enhanced chemical vapour deposition (PECVD) technique. By varying the hydrogen (H₂) flow rate, CH₄−Argon (Ar) flow rate and deposition temperature (T_d) as per a Box-Behnken experimental design (BBD), 15 DLC deposition experiments are carried out. The Young’s modulus (E) and the coefficient of friction (COF) for the DLCs are measured. By using a second-order polynomial regression approach, two metamodels are built for E and COF, that establish them as functions of H₂ flow rate, CH₄-Ar flow rate and T_d. A non-dominated sorting genetic algorithm… More >

Xiaobin Shu, Miaomiao Ren^*

FDMP-Fluid Dynamics & Materials Processing, Vol.16, No.5, pp. 1019-1029, 2020, DOI:10.32604/fdmp.2020.09564

Abstract In order to address typical problems due to the huge demand of oil for consumption in traditional internal combustion engines, a new more efficient combustion mode is proposed and studied in the framework of Computational Fluid Dynamics (CFD). Moreover, a Non-dominated Sorting Genetic Algorithm (NSGA-II) is applied to optimize the related parameters, namely, the engine methanol ratio, the fuel injection time, the initial temperature, the Exhaust Gas Re-Circulation (EGR) rate, and the initial pressure. The so-called Conventional Diesel Combustion (CDC), Homogeneous Charge Compression Ignition (HCCI) and the Reactivity Controlled Compression Ignition (RCCI) combustion modes are compared. The results show that… More >