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Improved material generation algorithm by opposition-based learning and laplacian crossover for global optimization and advances in real-world engineering problems

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materialS TESTING 2025年第4期67卷 737-746页

作者： Mehta, Pranav Kumar, Sumit Sait, Sadiq M. Yildiz, Betuel S. Yildiz, Ali Riza Bursa Uludag Univ Dept Mech Engn Bursa Turkiye Dharmsinh Desai Univ Dept Mech Engn Nadiad Gujarat India Univ Tasmania Launceston TAS Australia King Fahd Univ Petr & Minerals Dhahran Saudi Arabia

The current study aims to utilize a unique hybrid optimizer called oppositional-based learning and laplacian crossover augmented material generation algorithm (MGA-OBL-LP) to solve engineering design problems. The oppositional-based learning and laplacian crossover approaches are used to address the local optima trap weakness of a recently discovered MGA algorithm that has been added to the fundamental MGA structure. The proposed hybridization strategy aimed to make it easier to improve the exploration-exploitation behavior of the MGA algorithm. The performance of the proposed hybridized algorithm was compared with other notable metaheuristics collected from the literature for four constrained engineering design problems in order to determine whether it would be practical in real-world applications. A comparison analysis is undertaken to confirm the MGA-OBL-LP algorithm's competence in terms of solution quality and stability, and it is discovered to be robust in addressing difficult practical problems.

关键词： material generation algorithm opposition-based learning spring design spur gear design optimization

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An Improved Bio-Inspired material generation algorithm for Engineering Optimization Problems Including PV Source Penetration in Distribution Systems

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APPLIED SCIENCES-BASEL 2025年第2期15卷 603-603页

作者： Gafar, Mona Sarhan, Shahenda Ginidi, Ahmed R. Shaheen, Abdullah M. Prince Sattam Bin Abdulaziz Univ Coll Engn Wadi Alddawasir Dept Comp Engn & Informat Kharj 16278 Saudi Arabia Kafrelsheikh Univ Machine Learning & Informat Retrieval Dept Artificial Intelligence Kafrelsheikh 33516 Egypt Mansoura Univ Fac Comp & Informat Comp Sci Dept Mansoura 35516 Egypt Univ Econ & Human Sci PL-01043 Warsaw Poland Suez Univ Fac Engn Dept Elect Engn POB 43221 Suez Egypt

The material generation Optimization (MGO) algorithm is an innovative approach inspired by material chemistry which emulates the processes of chemical compound formation and stabilization to thoroughly explore and refine the parameter space. By simulating the bonding processes-such as the formation of ionic and covalent bonds-MGO generates new solution candidates and evaluates their stability, guiding the algorithm toward convergence on optimal parameter values. To improve its search efficiency, this paper introduces an Enhanced material generation Optimization (IMGO) algorithm, which integrates a Quadratic Interpolated Learner Process (QILP). Unlike conventional random selection, QILP strategically selects three distinct chemical compounds, resulting in increased diversity, a more thorough exploration of the solution space, and improved resistance to local optima. The adaptable and non-linear adjustments of QILP's quadratic function allow the algorithm to traverse complex landscapes more effectively. This innovative IMGO, along with the original MGO, is developed to support applications across three phases, showcasing its versatility and enhanced optimization capabilities. Initially, both the original and improved MGO algorithms are evaluated using several mathematical benchmarks from the CEC 2017 test suite and benchmarks to measure their optimization capabilities. Following this, both algorithms are applied to the following three well-known engineering optimization problems: the welded beam design, rolling element bearing design, and pressure vessel design. The simulation results are then compared to various established bio-inspired algorithms, including Artificial Ecosystem Optimization (AEO), Fitness-Distance-Balance AEO (FAEO), Chef-Based Optimization algorithm (CBOA), Beluga Whale Optimization algorithm (BWOA), Arithmetic-Trigonometric Optimization algorithm (ATOA), and Atomic Orbital Searching algorithm (AOSA). Moreover, MGO and IMGO are tested on a real Egy

关键词： material generation algorithm quadratic interpolated learner process benchmarks PV source penetration distribution systems

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Chemical-Inspired material generation algorithm (MGA) of Single- and Double-Diode Model Parameter Determination for Multi-Crystalline Silicon Solar Cells

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APPLIED SCIENCES-BASEL 2024年第18期14卷 8549页

作者： Alsaggaf, Wafaa Gafar, Mona Sarhan, Shahenda Shaheen, Abdullah M. Ginidi, Ahmed R. King Abdulaziz Univ Fac Comp & Informat Technol Dept Informat Technol Jeddah 21589 Saudi Arabia Prince Sattam Bin Abdulaziz Univ Coll Engn Wadi Ad Dwaser Dept Comp Engn & Informat Al Kharj 16278 Saudi Arabia Artificial Intelligence Kafrelsheikh Univ Machine Learning & Informat Retrieval Dept Kafrelsheikh 33516 Egypt Mansoura Univ Fac Comp & Informat Comp Sci Dept Mansoura 35516 Egypt Univ Econ & Human Sci PL-01043 Warsaw Poland Suez Univ Fac Engn Dept Elect Engn POB 43221 Suez Egypt

The optimization of solar photovoltaic (PV) cells and modules is crucial for enhancing solar energy conversion efficiency, a significant barrier to the widespread adoption of solar energy. Accurate modeling and estimation of PV parameters are essential for the optimal design, control, and simulation of PV systems. Traditional optimization methods often suffer from limitations such as entrapment in local optima when addressing this complex problem. This study introduces the material generation algorithm (MGA), inspired by the principles of material chemistry, to estimate PV parameters effectively. The MGA simulates the creation and stabilization of chemical compounds to explore and optimize the parameter space. The algorithm mimics the formation of ionic and covalent bonds to generate new candidate solutions and assesses their stability to ensure convergence to optimal parameters. The MGA is applied to estimate parameters for two different PV modules, RTC France and Kyocera KC200GT, considering their manufacturing technologies and solar cell models. The significant nature of the MGA in comparison to other algorithms is further demonstrated by experimental and statistical findings. A comparative analysis of the results indicates that the MGA outperforms the other optimization strategies that previous researchers have examined for parameter estimation of solar PV systems in terms of both effectiveness and robustness. Moreover, simulation results demonstrate that MGA enhances the electrical properties of PV systems by accurately identifying PV parameters under varying operating conditions of temperature and irradiance. In comparison to other reported methods, considering the Kyocera KC200GT module, the MGA consistently performs better in decreasing RMSE across a variety of weather situations;for SD and DD models, the percentage improvements vary from 8.07% to 90.29%.

关键词： material generation algorithm PV parameters estimation single-diode model double-diode model various weather conditions

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Multi-Objective material generation algorithm (MOMGA) for Optimization Purposes

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IEEE ACCESS 2022年 10卷 107095-107115页

作者： Nouhi, Behnaz Khodadadi, Nima Azizi, Mahdi Talatahari, Siamak Gandomi, Amir H. Univ Tabriz Dept Math Sci Tabriz 51666 Iran Florida Int Univ Coll Engn & Comp Dept Civil & Environm Engn Miami FL 33199 USA Univ Tabriz Dept Civil Engn Tabriz 51666 Iran Univ Technol Sydney Fac Engn & Informat Technol Ultimo NSW 2007 Australia Obuda Univ Univ Res & Innovat Ctr EKIK H-1034 Budapest Hungary RMIT Univ Sch Engn Melbourne Vic 3001 Australia

Optimization is a process of decision-making in which some iterative procedures are conducted to maximize or minimize a predefined objective function representing the overall behavior of a considered system problem. Most of the time, one specific function cannot represent the overall behavior of a system with particular levels of complexity, so the multiple objective functions should be determined for this purpose which requires an algorithm with adaptability to this situation. Multi-objective optimization is a process of decision making in which maximization or minimization of multiple objective functions is considered for reaching the acceptable levels of performance for the considered system problem. In this paper, the multi-objective version of the material generation algorithm (MGA) is proposed as MOMGA, one of the recently developed metaheuristic algorithms for single-objective optimization. To evaluate the overall performance of the MOMGA, the benchmark multi-objective optimization problems of the Competitions on Evolutionary Computation (CEC) are considered alongside the real-world engineering problems. Based on the results, the MOMGA is capable of providing very acceptable results in dealing with multi-objective optimization problems.

关键词： Metaheuristics Evolutionary computation Chemical reactions Chemical compounds Heuristic algorithms algorithm design and analysis Machine learning algorithms material generation algorithm multi-objective optimization real-world engineering problems competitions on evolutionary computation

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Parametric appraisal of strength & hardness of resin compacted sand castings using hybrid Taguchi-WASPAS-material generation algorithm 2nd

Parametric appraisal of strength & hardness of resin compact...

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2nd International Conference on Functional material, Manufacturing and Performances (ICFMMP)

作者： Behera, Nirmal Chandra Jeet, Siddharth Nayak, Chandan Kumar Bagal, Dilip Kumar Panda, Surya Narayan Barua, Abhishek Govt Coll Engn Dept Mech Engn Bhawanipatna Odisha India BPUT Dept Mech Engn Ctr Adv Post Grad Studies Rourkela Odisha India Birsa Inst Technol Dept Prod Engn Dhanbad Jharkhand India

When compared to green sand moulds, resin bound sand moulds and cores have higher mechanical characteristics and create more dimensionally exact castings, and are thus increasingly preferred for near net form metal components. The effect of binder %, sand particle size and curing time on the mechanical characteristics of no-bake resin bonded mould core was studied in this study using lab testing. Their mechanical characteristics were discovered to rise with the addition of binder amount and curing time, along with dropping with increasing grain fineness number. Microscopic study of cross-linked resin bridges between sand grains also supports this. To get the best blend of mould characteristics, the results were optimized using the WASPAS technique and the material generation algorithm. Tests were used to successfully validate the model and its findings. This study lays the groundwork for optimizing the moulding parameters of resin reinforced sand mould cores in order to achieve the best quality. Copyright (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 2nd International Conference on Functional material, Manufacturing and Performances

关键词： No-Bake Resin Bonded Mould Mechanical characterization Taguchi Method WASPAS Method material generation algorithm

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Parametric appraisal of strength & hardness of resin compacted sand castings using hybrid Taguchi-WASPAS-material generation algorithm

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materials Today: Proceedings 2022年 50卷 1226-1233页

作者： Nirmal Chandra Behera Siddharth Jeet Chandan Kumar Nayak Dilip Kumar Bagal Surya Narayan Panda Abhishek Barua Department of Mechanical Engineering Government College of Engineering Kalahandi Bhawanipatna Odisha India Department of Mechanical Engineering Centre for Advanced Post Graduate Studies BPUT Rourkela Odisha India Department of Production Engineering Birsa Institute of Technology Sindri Dhanbad Jharkhand India

关键词： No-Bake Resin Bonded Mould Mechanical characterization Taguchi Method WASPAS Method material generation algorithm

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Efficiency analysis of mechanical reducer equipment of material handling industry using Sunflower Optimization algorithm and material generation algorithm

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materials Today: Proceedings 2022年 50卷 1113-1122页

作者： Sourav Jena Siddharth Jeet Dilip Kumar Bagal Asini Kumar Baliarsingh Dillip Ranjan Nayak Abhishek Barua Department of Mechanical Engineering Government College of Engineering Kalahandi Bhawanipatna Odisha India Department of Mechanical Engineering Centre for Advanced Post Graduate Studies BPUT Rourkela Odisha India Department of Electrical Engineering Government College of Engineering Kalahandi Bhawanipatna Odisha India Department of Computer Science Engineering Government College of Engineering Kalahandi Bhawanipatna Odisha India

Gear reducers are commonly used in cross-industrial applications. These include a range of advanced and basic processes, requiring the delivery of a controlled torque output. Mainly, industrial reducers are used in material handling units as it controls the speed of the machineries such as conveyors, cranes, hoist, mixers etc. For effective operation and to reduce the downtime due to any fault, optimal operating conditions are needed to define. In this paper the effectiveness and yield power are thought of, as the main attributes of the Industrial reducer, and their optimization was performed. As the impacting factors, the viscosity of lubricant, the initial parametric no. of revolutions and the current force intensity on the control unit, were considered. Test tests were performed based on the L27 Taguchi orthogonal array. For maximizing the output power and efficiency of mechanical reducer, recently formulate bio-inspired meta -heuristic algorithms i.e. material generation algorithm and Sunflower Optimization algorithm were employed besides Taguchi technique for optimization. It was found that current intensity played a significant role in maximizing the output power and efficiency of industrial reducer in accordance to analysis of variance results. Also both material generation algorithm and Sunflower Optimization algorithm resulted to be precise in providing better output as compared to Taguchi method for maximizing the output power and efficiency of the industrial reducer gearbox.

关键词： Mechanical Reducer Taguchi Method material generation algorithm Sunflower Optimization algorithm

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Optimum design of truss structures by material generation algorithm with discrete variables

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Decision Analytics Journal 2022年 3卷

作者： Mahdi Azizi Milad Baghalzadeh Shishehgarkhaneh Mahla Basiri Department of Civil Engineering University of Tabriz Tabriz Iran Department of Civil Engineering Islamic Azad University of Tabriz Tabriz Iran

Optimization is an act of decision-making for reaching a point in which the overall behaviour of the considered system is acceptable by the experts. In this paper, the optimum design of truss structures is considered utilizing the material generation algorithm (MGA) as one of the recently developed metaheuristic algorithms in which the basic and advanced principles of chemistry have been used as an inspirational concept. For numerical investigations, the benchmark problems in the truss optimization field, including the 25-bar, 72-bar and 200-bar truss structures, are used while a penalty method is utilized accordingly for constraint handling purposes. Multiple optimization runs are conducted for statistical purposes, and the results are compared to the results of other metaheuristic algorithms. The obtained results proved that the MGA could provide very acceptable and optimal design sections for the considered problems, which results in the lowest possible weight.

关键词： material generation algorithm Truss structure Metaheuristic algorithm Optimization

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Efficiency analysis of mechanical reducer equipment of material handling industry using Sunflower Optimization algorithm and material generation 2nd

Efficiency analysis of mechanical reducer equipment of mater...

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2nd International Conference on Functional material, Manufacturing and Performances (ICFMMP)

作者： Jena, Sourav Jeet, Siddharth Bagal, Dilip Kumar Baliarsingh, Asini Kumar Nayak, Dillip Ranjan Barua, Abhishek Govt Coll Engn Dept Mech Engn Bhawanipatna Odisha India BPUT Dept Mech Engn Ctr Adv Post Grad Studies Rourkela Odisha India Govt Coll Engn Dept Elect Engn Bhawanipatna Odisha India Govt Coll Engn Dept Comp Sci Engn Bhawanipatna Odisha India

ABSTR A C T Gear reducers are commonly used in cross-industrial applications. These include a range of advanced and basic processes, requiring the delivery of a controlled torque output. Mainly, industrial reducers are used in material handling units as it controls the speed of the machineries such as conveyors, cranes, hoist, mixers etc. For effective operation and to reduce the downtime due to any fault, optimal operating con-ditions are needed to define. In this paper the effectiveness and yield power are thought of, as the main attributes of the Industrial reducer, and their optimization was performed. As the impacting factors, the viscosity of lubricant, the initial parametric no. of revolutions and the current force intensity on the con-trol unit, were considered. Test tests were performed based on the L27 Taguchi orthogonal array. For maximizing the output power and efficiency of mechanical reducer, recently formulate bio-inspired meta-heuristic algorithms i.e. material generation algorithm and Sunflower Optimization algorithm were employed besides Taguchi technique for optimization. It was found that current intensity played a significant role in maximizing the output power and efficiency of industrial reducer in accordance to analysis of variance results. Also both material generation algorithm and Sunflower Optimization algorithm resulted to be precise in providing better output as compared to Taguchi method for maximiz-ing the output power and efficiency of the industrial reducer gearbox. (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 2nd International Con-ference on Functional material, Manufacturing and Performances

关键词： Mechanical Reducer Taguchi Method material generation algorithm Sunflower Optimization algorithm

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