Jian Gao¹, Hongliang Hao², Caifeng Wen^1,*, Yongsheng Wang³, Zhanhua Han⁴, Edwin E. Nykilla², Yuwen Zhang²

Energy Engineering, Vol.122, No.3, pp. 1175-1198, 2025, DOI:10.32604/ee.2025.061083 - 07 March 2025

Abstract Power quality is a crucial area of research in contemporary power systems, particularly given the rapid proliferation of intermittent renewable energy sources such as wind power. This study investigated the relationships between power quality indices of system output and PSD by utilizing theories related to spectra, PSD, and random signal power spectra. The relationship was derived, validated through experiments and simulations, and subsequently applied to multi-objective optimization. Various optimization algorithms were compared to achieve optimal system power quality. The findings revealed that the relationships between power quality indices and PSD were influenced by variations in More >

Sudipta Debnath¹, Zahir Uddin Ahmed², Muhammad Ikhlaq^3,4,*, Md. Tanvir Khan⁵, Avneet Kaur⁶, Kuljeet Singh Grewal¹

Frontiers in Heat and Mass Transfer, Vol.23, No.1, pp. 71-94, 2025, DOI:10.32604/fhmt.2024.059734 - 26 February 2025

Abstract Impinging jet arrays are extensively used in numerous industrial operations, including the cooling of electronics, turbine blades, and other high-heat flux systems because of their superior heat transfer capabilities. Optimizing the design and operating parameters of such systems is essential to enhance cooling efficiency and achieve uniform pressure distribution, which can lead to improved system performance and energy savings. This paper presents two multi-objective optimization methodologies for a turbulent air jet impingement cooling system. The governing equations are resolved employing the commercial computational fluid dynamics (CFD) software ANSYS Fluent v17. The study focuses on four… More > Graphic Abstract

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 - 30 January 2024

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… 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 - 08 October 2023

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… More >

Caiyun Liu, Peng Liu^*

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3221-3242, 2023, DOI:10.32604/iasc.2023.035114 - 15 March 2023

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 More >

Lei Xing¹, Yakui Liu^2,3,*

Energy Engineering, Vol.120, No.4, pp. 1023-1043, 2023, DOI:10.32604/ee.2023.025335 - 13 February 2023

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… 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 - 03 May 2022

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… 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 - 09 December 2021

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… 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 - 08 October 2021

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 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 - 24 August 2021

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… More >