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Modified swarm intelligence algorithms for the pharmacy duty scheduling problem

EXPERT SYSTEMS WITH APPLICATIONS 2022年 202卷 1页

作者： Kilic, Fatih Uncu, Nusin Adana Alparslan Turkes Sci & Technol Univ Dept Comp Engn Adana Turkey Adana Alparslan Turkes Sci & Technol Univ Dept Ind Engn Adana Turkey

Assigning duties to pharmacies to serve the public on weekends, nights, and public holidays is known as the pharmacy duty scheduling problem (PDSP). In this study, a..-median model is proposed with a casespecific constraint that provides the distance restriction between on-duty pharmacies. We adapted Binary and Random-Key versions of the most recent and popular swarm intelligence (SI) algorithms, which are Grey Wolf Optimizer (GWO), Particle swarm Optimization (PSO), Dragonfly Algorithm (DA), and Harris Hawks Optimization (HHO) to solve it in a reasonable amount of time since the PDSP is known to be an NP-Hard problem. We also proposed several enhancements to the algorithms and conducted computational tests on real cases generated instances using Geographic Information System (GIS) tools to compare the performances of the proposed algorithms with the state-of-the-art general-purpose solver. Two real-world datasets, called DS1 and DS2, are built considering the demand points. To validate the proposed algorithms, the CPLEX results, obtained with the exact solution for the DS1 dataset, are used. Compared to the MIP results for 800 meters distance constraint as an instance, maximum relative errors for Binary and Random-Key GWO, PSO, DA, and HHO are 0.07, 0.08, 0.08, and 0.08, respectively. The success of the algorithms is similar to the other distance criteria as well. Moreover, the results for the large DS2 dataset show that the proposed Binary GWO is more effective and offers efficient solutions compared to the other swarm intelligence algorithms and CPLEX solvers in terms of convergence to optimization and solution time.

关键词： Pharmacy duty scheduling swarm intelligence algorithms p-median problem Location covering Distance restriction

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A survey of dynamic parameter setting methods for nature-inspired swarm intelligence algorithms

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NEURAL COMPUTING & APPLICATIONS 2020年第2期32卷 567-588页

作者： Phan, Han Duy Ellis, Kirsten Barca, Jan Carlo Dorin, Alan Monash Univ Fac IT Clayton Vic 3800 Australia Deakin Univ Fac Sci Engn & Built Environm Sch IT Burwood 3125 Australia

Parameter settings for nature-inspired optimization algorithms are essential for their effective performance. Evolutionary algorithms and swarm intelligence algorithms are prominent types of nature-inspired optimization. There are comprehensive reviews of parameter setting techniques for evolutionary algorithms. Counterparts providing an overview of parameter setting techniques for swarm intelligence algorithms are needed also. Therefore, in this paper, we provide a critical and comprehensive review, focusing in particular on dynamic parameter setting techniques. The paper describes a variety of swarm intelligence algorithms and parameter setting approaches that have been applied to them. This review simplifies the selection of parameter setting techniques for each algorithm by collecting them in a single document and classifying them under a taxonomy. Recommendations for parameter setting approach selection are provided in this review. We explore the open problems related to dynamic parameter setting techniques for swarm intelligence optimization and discuss the trade-off between run-time computation and flexibility of these algorithms.

关键词： swarm intelligence algorithms Dynamic parameter setting Parameter control

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Optimal Estimation of Schottky Diode Parameters Using Advanced swarm intelligence algorithms

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SEMICONDUCTORS 2020年第11期54卷 1398-1405页

作者： Rabehi, A. Nail, B. Helal, H. Douara, A. Ziane, A. Amrani, M. Akkal, B. Benamara, Z. Univ Djillali Liabes Sidi Bel Abbes Lab Microelect Appl BP 89 Sidi Bel Abbes 22000 Algeria Tissemsilt Univ Ctr Inst Sci & Technol Tissemsilt 38000 Algeria

This work deals with estimation of the Schottky diode (Au|GaN|GaAs) optimal parameters. For this purpose, advanced swarm intelligence (SI) algorithms have been applied, i.e., Harris hawks optimization, ant lion optimizer (ALO), grey wolf optimizer, and whale optimization algorithm. The performance of the SI algorithms has been investigated by a comparative study following the analytical methods developed by Kaminski I, Cheung and Cheung, Norde, and Mikhelashvili. The comparative results show that the ALO algorithm gives minimum RMSE criteria, with best parameters estimation against all the SI optimizers and the analytical techniques.

关键词： Schottky diodes Au GaN GaAs swarm intelligence algorithms parameters estimation barrier height

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Prediction of carbonation depth for recycled aggregate concrete using ANN hybridized with swarm intelligence algorithms

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CONSTRUCTION AND BUILDING MATERIALS 2021年 301卷 124382-124382页

作者： Liu, Kaihua Alam, M. Shahria Zhu, Jiang Zheng, Jiakai Chi, Lin Guangdong Univ Technol Sch Civil & Transportat Engn Guangzhou 510006 Peoples R China Univ British Columbia Sch Engn Kelowna BC V1V 1V7 Canada Univ Shanghai Sci & Technol Sch Environm & Architecture Shanghai 200093 Peoples R China

This paper investigates the prediction of carbonation depth for recycled aggregate concrete (RAC) with machine learning models. Nine parameters including RAC intrinsic properties and environmental conditions were considered as input variables. A dataset comprising 593 test data was used to train, validate, and test machine learning models. Results show that the Random forest model shows superior performance than the Gaussian progress regression model and standalone artificial neural network (ANN) model. All ANN models hybridized with swarm intelligence algorithms outperform the standalone ANN model, especially for the ANN model hybridized with the whale optimization algorithm. All machine learning models show higher accuracy than the existing code models and statistical models. The variable importance analysis shows that the carbonation resistance for RAC was determined by both internal and external factors. Based on the parametric analysis, the robustness of the proposed machine learning models was further confirmed.

关键词： Recycled aggregate concrete Carbonation depth Machine learning Hybrid model Artificial neural network swarm intelligence algorithms

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A comparative energy and exergy optimization of a supercritical-CO2 Brayton cycle and Organic Rankine Cycle combined system using swarm intelligence algorithms

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HELIYON 2020年第6期6卷 e04136页

作者： Ochoa, Guillermo Valencia Forero, Jorge Duarte Rojas, Jhan Piero Univ Atlantico Programa Ingn Mecan Carrera 30 Numero 8-49 Barranquilla 080007 Colombia Univ Francisco Paula Santander Engn Fac Ave Gran Colombia 12E-96 Cucuta 540003 Colombia

This article presents a multivariable optimization of the energy and exergetic performance of a power generation system, which is integrated by a supercritical Brayton Cycle using carbon dioxide, and a Simple Organic Rankine Cycle (SORC) using toluene with reheater (S - CO2RH - SORC), and without reheater (S - CO2NRH - SORC) using the PSO algorithm. A thermodynamic model of the integrated system was developed from the application of mass, energy and exergy balances to each component, which allowed the calculation exergy destroyed a fraction of each equipment, the power generated, the thermal and exergetic efficiency of the system. In addition, through a sensitivity analysis, the effect of the main operational and design variables on thermal efficiency and total exergy destroyed was studied, which were the objective functions selected in the proposed optimization. The results show that the greatest exergy destruction occurs at the thermal source, with a value of 97 kW for the system without Reheater (NRH), but this is reduced by 92.28% for the system with Reheater (RH). In addition, by optimizing the integrated cycle for a particle number of 25, the maximum thermal efficiency of 55.53% (NRH) was achieved, and 56.95% in the RH system. Likewise for a particle number of 15 and 20 in the PSO algorithm, exergy destruction was minimized to 60.72 kW (NRH) and 112.06 kW (RH), respectively. Comparative analyses of some swarm intelligence optimization algorithms were conducted for the integrated S - CO2 - SORC system, evaluating performance indicators, where the PSO optimization algorithm was favorable in the analyses, guar-anteeing that it is the ideal algorithm to solve this case study.

关键词： Energy Mechanical engineering Thermodynamics Mathematical optimization Energy conservation swarm intelligence algorithms Brayton supercritical CO2 cycle Organic Rankine cycle Exergetic analysis Energy analysis

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A Voting Approach for Comparing Several swarm intelligence algorithms 20

A Voting Approach for Comparing Several Swarm Intelligence A...

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20th International Conference on System Controls and Computer Science 2015

作者： Mogos, Andrei-Horia Mogos, Bianca Florea, Adina Magda Univ Politehn Bucuresti Fac Automat Control & Comp Bucharest Romania Univ Bucharest Fac Math & Comp Sci Bucharest Romania

ISBN: (纸本)9781479917808

In the last decades, social choice theory has gained a significant popularity. Its main application areas are social sciences, political sciences, economic sciences and computer science. Computational social choice is a new research area situated at the intersection of social choice theory and computer science. Another popular and relatively new research area is swarm intelligence that aims to propose and use bio-inspired algorithms for solving optimization problems. In this paper we propose a methodology of comparing various swarm intelligence algorithms using voting methods (an important topic in social choice theory). Also, as a case study, we use our methodology to compare three swarm intelligence algorithms (Particle swarm Optimization, Cat swarm Optimization, and Artificial Bee Colony) on several minimization functions. For the interpretation of our comparison results, we use two important theorems: No Free Lunch Theorem (from optimization theory) and Arrow's Impossibility Theorem (from voting theory).

关键词： swarm intelligence algorithms Voting Methods No Free Lunch Theorem Arrow's Impossibility Theorem

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Optimal band selection and transfer in drone-based hyperspectral images for plant-level vegetable crops identification using statistical-swarm intelligence (SSI) hybrid algorithms

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ECOLOGICAL INFORMATICS 2025年 86卷

作者： Sarma, Anagha S. Nidamanuri, Rama Rao Indian Inst Space Sci & Technol Dept Earth & Space Sci Thiruvananthapuram 695547 Kerala India

Hyperspectral imagery from drones are excellent sources of high-resolution plant-level data that helps in efficient crop identification for smart agriculture practices. However, the plant-level crop identification is still challenging, owing to only minute differences among the vegetation spectra and enhanced background diversity. Though the inherent spectral redundancy in hyperspectral data is dealt with various band reduction techniques for distinguishing different land use land cover (LULC) structures at broader level, band reduction at finer level has not been explored, especially for drone-based hyperspectral imagery. In this work, we propose a statisticalswarm intelligence (SSI) hybrid approach for optimal band selection in hyperspectral images for identification of three crops, cabbage, eggplant and tomato, using drone-based imagery acquired over experimental plots of University of Agricultural Sciences, Bengaluru, India. We ranked the spectral bands based on their separability among the ground truth classes that follows a Weibull distribution and applied swarm-intelligence algorithms for the optimal band set. The optimal band set is further tested for its capability to distinguish crops from images acquired at different flight heights of drone, and at different points of time. The results suggest that only 15 bands (462 nm, 470 nm, 498 nm, 502 nm, 514 nm, 534 nm, 542 nm, 550 nm, 582 nm, 590 nm, 614 nm, 762 nm, 902 nm, 914 nm, and 930 nm), spread at specific wavelengths in 450-650 nm and 850-950 nm range are sufficient to distinguish crops at finer spatial resolution and offer classification accuracy of over 93 %, supported by equivalent levels of F1 (0.93) and kappa coefficients (0.9). The specific wavelengths corresponding to optimal bands are capable of detecting crops in images captured at different heights and taken at a different time period, showing the space-time transfer potential of the selected bands for crop detection. The specific wavelengths are

关键词： Precision agriculture Weibull distribution Band selection swarm intelligence algorithms Hyperspectral imagery Drones

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swarm intelligence algorithms for Circles Packing Problem with equilibrium Constraints

Swarm intelligence algorithms for Circles Packing Problem wi...

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第12届分布式计算及其应用国际学术研讨会

作者： Peng Wang Shuai Huang ZhouQuan Zhu College of Marine Northwestern Polytechnical University College of Marine Northwestern Polytechnical University

Circles packing problem with equilibrium constraints is difficult to solve due to its NP-hard nature. Aiming at this NP-hard problem, three swarm intelligence algorithms are employed to solve this problem. Particle swarm Optimization and Ant Colony Optimization has been used for the circular packing problem with equilibrium constraints. In this paper, Artificial Bee Colony Algorithm(ABC) for equilibrium constraints circular packing problem is presented. Then we compare the performances of well-known swarm intelligence algorithms(PSO, ACO, ABC) for this problem. The results of experiment show that ABC is comparatively satisfying because of its stability and applicability.

关键词： Circles Packing swarm intelligence algorithms Equilibrium Constraints

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Recent swarm intelligence techniques for optimal spur gear design

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2025年

作者： Abderazek, Hammoudi Laouissi, Aissa Nouioua, Mourad Atanasovska, Ivana Mhamed Bougara Univ UR MPE Independence Ave Boumerdes 35000 Algeria Setif 1 Univ Inst Opt & Precis Mech Appl Precis Mech Lab Setif Algeria Univ Mohamed El Bachir El Ibrahimi Fac Sci & Technol Dept Mech Engn Bordj Bou Arreridj Algeria 1945 Univ 8 May Guelma Mech Engn Dept Mech & Struct Res Lab LMS Guelma Algeria King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Intelligent Mfg & Robot Dhahran Saudi Arabia Serbian Acad Arts & Sci Math Inst Belgrade Serbia

The gear tooth profile significantly influences the key operating parameters of gear pairs. By appropriately modifying the tooth profile, the vibration and noise of the gear system can be greatly reduced. In this study, four advanced optimization techniques are applied to determine the optimal geometric parameters of a cylindrical spur gear. The optimization problem is formulated with three mixed design variables: the profile shift coefficients and the normal pressure angle. The three objectives include minimizing the maximum specific sliding, sliding velocity, and nominal stresses at the pinion tooth root. Kinematic and geometric constraints, such as contact ratio, tooth thickness, and interference, are also considered to ensure an optimal spur gear design. The simulation results demonstrate that the algorithms employed are highly competitive for precision gear design optimization.

关键词： Multi-objective optimization tooth profile profile shift spur gear meta-heuristics swarm intelligence algorithms

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swarm intelligence algorithms for Circles Packing Problem with equilibrium Constraints

Swarm intelligence algorithms for Circles Packing Problem wi...

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12th International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES)

作者： Wang, Peng Huang, Shuai Zhu, ZhouQuan Northwestern Polytech Univ Coll Marine Xian 710072 Peoples R China

ISBN: (纸本)9780769550602

Circles packing problem with equilibrium constraints is difficult to solve due to its NP-hard nature. Aiming at this NP-hard problem, three swarm intelligence algorithms are employed to solve this problem. Particle swarm Optimization and Ant Colony Optimization has been used for the circular packing problem with equilibrium constraints. In this paper, Artificial Bee Colony Algorithm (ABC) for equilibrium constraints circular packing problem is presented. Then we compare the performances of well-known swarm intelligence algorithms (PSO, ACO, ABC) for this problem. The results of experiment show that ABC is comparatively satisfying because of its stability and applicability.

关键词： Circles Packing swarm intelligence algorithms Equilibrium Constraints

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