A Hybrid Marine Predator Sine Cosine Algorithm for Parameter Selection of Hybrid Active Power Filter

Ali, Shoyab; Bhargava, Annapurna; Saxena, Akash; Kumar, Pavan

doi:10.3390/math11030598

Cited by 28 publications

(10 citation statements)

References 59 publications

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“…Constraint (8) guarantees that each retailer is served by only one vehicle for each product in each period. Constraint (9) implies that whenever a vehicle arrives at a retailer location, it must leave again. Constraint (10) is adopted in the proposed model for subtour elimination.…”

Section: Mathematical Modelmentioning

confidence: 99%

“…Since the LIRP is characterized by its size, complexity, and nonlinear nature, it is not always feasible to obtain an optimal solution using exact methods. Nowadays, metaheuristic algorithms have been commonly used globally for their simple structure and other advantageous qualities, such as adaptability and flexibility [9]. GA has the advantage of providing feasible solutions that are close to optimal, even in scenarios where the problem's constraints or objective function are non-continuous and do not necessitate a linear formulation.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing Perishable Product Supply Chain Network Using Hybrid Metaheuristic Algorithms

Li-hong

Shan

2023

Sustainability

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This paper focuses on optimizing the long- and short-term planning of the perishable product supply chain network (PPSCN). It addresses the integration of strategic location, tactical inventory, and operational routing decisions. Additionally, it takes into consideration the specific characteristics of perishable products, including their shelf life, inventory management, and transportation damages. The main objective is to minimize the overall supply chain cost. To achieve this, a nonlinear mixed integer programming model is developed for the multi-echelon, multi-product, and multi-period location-inventory-routing problem (LIRP) in the PPSCN. Two hybrid metaheuristic algorithms, namely genetic algorithm (GA) and multiple population genetic algorithm (MPGA), are hybridized with variable neighborhood search (VNS) and proposed to solve this NP-hard problem. Moreover, a novel coding method is devised to represent the complex structure of the LIRP problem. The input parameters are tuned using the Taguchi experimental design method, considering the sensitivity of meta-heuristic algorithms to these parameters. Through experiments of various scales, the hybrid MPGA with VNS indicates superior performance, as evidenced by the experimental results. Sensitivity analysis is conducted to examine the influence of key model parameters on the optimal objective, providing valuable management implications. The results clearly validate the efficacy of the proposed model and solution method as a reliable tool for optimizing the design problem of the PPSCN.

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Section: Mathematical Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimizing Perishable Product Supply Chain Network Using Hybrid Metaheuristic Algorithms

Li-hong

Shan

2023

Sustainability

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“…The hyperparameters of the EfficientNet model can be chosen by the SCO algorithm at this stage. In 2016, Seyedali Mirjalili proposed an SCO, a newly established metaheuristic technique that is used to resolve real-time engineering problems [30]. The mathematical model is based primarily on the sine and cosine rules, and SCO changes many initial random solutions to the best possible region of convergence.…”

Section: Hyperparameter Tuningmentioning

confidence: 99%

Artificial Intelligence Based COVID-19 Detection and Classification Model on Chest X-ray Images

2023

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Diagnostic and predictive models of disease have been growing rapidly due to developments in the field of healthcare. Accurate and early diagnosis of COVID-19 is an underlying process for controlling the spread of this deadly disease and its death rates. The chest radiology (CT) scan is an effective device for the diagnosis and earlier management of COVID-19, meanwhile, the virus mainly targets the respiratory system. Chest X-ray (CXR) images are extremely helpful in the effective diagnosis of COVID-19 due to their rapid outcomes, cost-effectiveness, and availability. Although the radiological image-based diagnosis method seems faster and accomplishes a better recognition rate in the early phase of the epidemic, it requires healthcare experts to interpret the images. Thus, Artificial Intelligence (AI) technologies, such as the deep learning (DL) model, play an integral part in developing automated diagnosis process using CXR images. Therefore, this study designs a sine cosine optimization with DL-based disease detection and classification (SCODL-DDC) for COVID-19 on CXR images. The proposed SCODL-DDC technique examines the CXR images to identify and classify the occurrence of COVID-19. In particular, the SCODL-DDC technique uses the EfficientNet model for feature vector generation, and its hyperparameters can be adjusted by the SCO algorithm. Furthermore, the quantum neural network (QNN) model can be employed for an accurate COVID-19 classification process. Finally, the equilibrium optimizer (EO) is exploited for optimum parameter selection of the QNN model, showing the novelty of the work. The experimental results of the SCODL-DDC method exhibit the superior performance of the SCODL-DDC technique over other approaches.

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“…The design of hybrid active power filters (HAPFs) was a difficult task and therefore a meta-heuristic algorithm was used to handle this nonlinear optimization issue. Shoyab Ali et al [17] proposed a novel hybrid metaheuristic algorithm (Marine Predator Algorithm and Sine Cosine Algorithm) for choosing the optimal parameters for HAPF. Different algorithms for obtaining HAPF parameters were also compared to display the proposed hybrid algorithm's case efficacy.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization of Curtain Wall Production Line Balance Based on Improved Genetic Algorithm

Wang,

Xu,

et al. 2023

Mathematics

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In recent years, construction engineering technology has been developing rapidly, and the application of curtain walls inside modern buildings is also increasing. However, with the increasing number of curtain wall orders, most factories are facing challenges in the market due to low productivity caused by a low balance rate of curtain wall production lines. This paper is useful in improving the balance rate of the curtain wall production line; firstly, we use the improved genetic algorithm to obtain the optimal sequencing scheme of the curtain wall production line. Then, the optimization plan is validated through FLEXSIM simulation, and the results show that the device utilization rate of each workstation reaches 80%. Finally, this paper designs an intelligent production factory for curtain walls, and then builds an intelligent production line for curtain wall columns for experiments. The experimental results show that the workstation operation time has been reduced from 360 s to 300 s; the production line balance rate has increased from 57.04% to 91.60%. Therefore, it can be concluded that the modified genetic algorithm is valid in improving the balance rate of curtain wall production lines and raising the production efficiency of enterprises.

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A Hybrid Marine Predator Sine Cosine Algorithm for Parameter Selection of Hybrid Active Power Filter

Cited by 28 publications

References 59 publications

Optimizing Perishable Product Supply Chain Network Using Hybrid Metaheuristic Algorithms

Optimizing Perishable Product Supply Chain Network Using Hybrid Metaheuristic Algorithms

Artificial Intelligence Based COVID-19 Detection and Classification Model on Chest X-ray Images

Optimization of Curtain Wall Production Line Balance Based on Improved Genetic Algorithm

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