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Multi-Strategy Golden Jackal Optimization for engineering design

JOURNAL OF SUPERCOMPUTING 2025年第4期81卷 1-60页

作者： Yang, Wenbiao Lai, Tingfeng Fang, Yuhui Guangzhou Univ Sch Elect & Commun Engn Guangzhou 510006 Peoples R China Nanyang Technol Univ Sch Mech & Aerosp Engn Nanyang 639798 Singapore

The Golden Jackal Optimization algorithm (GJO) is a Swarm Intelligence (SI) algorithm utilized for tackling arduous problems in undefined search spaces. In order to address the issues of slow convergence and difficulty in evading local optima encountered by GJO, the Multi-Strategy Golden Jackal Optimization (MSGJO) algorithm is proposed. Three strategies were implemented to enhance the original GJO. Firstly, the utilization of a Good Point Set, as opposed to random numbers, improves the quality of the initial population. Secondly, the introduction of a Quasi-Opposition-Based Learning strategy facilitates the balancing of algorithmic exploration and exploitation capabilities, leading to enhanced convergence efficiency. Lastly, the maintenance of population diversity during the iterative process is achieved through the adoption of the Cauchy mutation and chaotic circle perturbation strategies. A comparative analysis was conducted on 23 benchmark functions using fourteen SI algorithms. Additionally, MSGJO was applied to the CEC2019 competition suite, successfully solving two distinct engineering design problems. The results demonstrate that MSGJO exhibits significant superiority not only in solving problems involving global optimal solutions but also in addressing challenging constrained problems. The code for this study will be available at https://***/ProfYangPaperCode/MSGJO-code.

关键词： Swarm Intelligence algorithm Golden Jackal Optimization algorithm Quasi-Opposition-Based Learning strategy benchmark test function Engineering design

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Direct methanol fuel cells parameter identification with enhanced algorithmic technique

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ENERGY CONVERSION AND MANAGEMENT-X 2025年 26卷

作者： Singla, Manish Kumar Ali, S. A. Muhammed Kumar, Ramesh Zeinoddini-Meymand, Hamed Shahnia, Farhad Univ Kebangsaan Malaysia Fuel Cell Inst Bangi 43600 Selangor Malaysia Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Biosci Chennai 602105 India Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan Chitkara Univ Chitkara Univ Inst Engn & Technol Dept Interdisciplinary Courses Engn Rajpura Punjab India Grad Univ Adv Technol Dept Elect & Comp Engn Kerman Iran Murdoch Univ Sch Engn & Energy Perth Australia

The parameter of a direct methanol fuel cell (DMFC) can be identified using optimization techniques to determine the optimal unknown parameter values that are needed for creating an accurate fuel cell performance prediction model. This research is motivated by the fact that parameter identification process is necessary since manufacturers' datasheets may not always provide these parameters to users. To address this, the study investigates five optimization techniques with the proposed algorithm for estimating these parameters in DMFCs. A number of optimization techniques have been adopted: Particle Swarm Optimization (PSO), Dragonfly Algorithm (DA), Harris Hawk Optimizer (HHO), Rhinoceros Search Algorithm (RSA), Artificial Hummingbird Algorithm (AHA), and its enhanced version, Enhanced artificial hummingbird algorithm (EAHA). The objective of each approach is to minimize the error between the predicted and measured voltages of the cell and the six unknown parameters. The numerical results support the improvement in the performance and robustness of the proposed approach over the existing methods and the state-of-the-art optimizers. The study shows that the EAHA significantly outperforms other optimization algorithms with a maximum estimation error of 6.34 x 10- 10. This demonstrates superior performance in finding the global minimum, consistent results with limited variability, and the ability to consistently provide near-optimal solutions. EAHA also exhibits the least standard deviation (2.10 x 10-10), highlighting its reliability and predictability due to EAHA's efficient balance of exploration and exploitation phases, enabling it to effectively locate and converge on optimal solutions.

关键词： Direct Methanol Fuel Cell Optimization benchmark test function Non-Parametric test

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A novel opposition-based gravitational search algorithm for combined economic and emission dispatch problems of power systems

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2012年第1期35卷 21-33页

作者： Shaw, Binod Mukherjee, V. Ghoshal, S. P. Asansol Engn Coll Dept Elect Engn Asansol W Bengal India Indian Sch Mines Dept Elect Engn Dhanbad 826004 Jharkhand India Natl Inst Technol Dept Elect Engn Durgapur W Bengal India

Gravitational search algorithm (GSA) is based on the law of gravity and interaction between masses. In GSA, the searcher agents are a collection of masses, and their interactions are based on the Newtonian laws of gravity and motion. This paper proposes a novel algorithm to accelerate the performance of the GSA. The proposed opposition-based GSA (OGSA) of the present work employs opposition-based learning for population initialization and also for generation jumping. In the present work, opposite numbers have been utilized to improve the convergence rate of the GSA. For the experimental verification of the proposed algorithm, a comprehensive set of 23 complex benchmark test functions including a wide range of dimensions is employed. Additionally, four standard power systems problems of combined economic and emission dispatch (CEED) are solved by the OGSA to establish the optimizing efficacy of the proposed algorithm. The results obtained confirm the potential and effectiveness of the proposed algorithm compared to some other algorithms surfaced in the recent state-of-the art literatures. Both the near-optimality of the solution and the convergence speed of the proposed algorithm are promising. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： benchmark test function Combined economic emission dispatch Gravitational search algorithm Opposite numbers Optimization

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Mathematical Modeling for Solar Cell Optimization: Evaluating Sustainability With Different Diode Configurations

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IEEE ACCESS 2024年 12卷 93802-93822页

作者： Kumar Singla, Manish Gupta, Jyoti Safaraliev, Murodbek Zeinoddini-Meymand, Hamed Ghanizadeh, Ahmad Javid Chitkara Univ Chitkara Univ Inst Engn & Technol Dept Interdisciplinary Courses Engn Chandigarh Punjab India Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan KR Mangalam Univ Sch Engn & Technol Gurugram 122103 Haryana India Ural Fed Univ Dept Automated Elect Syst Ekaterinburg 620002 Russia Grad Univ Adv Technol Dept Elect & Comp Engn Kerman *** Iran Danial Javid Elect Engn DJEE Co Kabul 3001 Afghanistan

It is widely acknowledged within the scientific community that solar cells serve as an environmentally friendly and sustainable source of renewable energy, making them highly versatile in their applications within both the industrial and residential sectors. Consequently, existing algorithms for solar cell design primarily focus on meeting the energy demands of domestic settings, necessitating the development of precise mechanisms that can cater to the needs of both domestic and industrial environments. In light of this, the author of this study proposes an enhanced version of the crow optimization algorithm by incorporating opposition-based learning, thereby ensuring improved exploration of the search space. The primary objective of this algorithm is to estimate the parameters of solar cells, which can be achieved by employing three distinct diode models: the single diode model, the double diode model, and the three-diode model. It is of utmost importance to accurately estimate the internal parameters in these models in order to optimize the performance of solar cells, particularly in industrial applications. An algorithm that incorporates the three-diode model is better suited for industrial use due to its superior ability to accurately represent the behavior of solar cells. In order to validate the efficiency of the proposed approach, RMSE and computational time is been recorded of each algorithm using similar approaches and datasets from solar cells and photovoltaic modules. Furthermore, the algorithm's exploration capability across complex optimization problems was assessed by subjecting it to various benchmark optimization functions. Based on the outcomes of these experiments and comparisons, it can be confidently affirmed that the proposed algorithm is not only effective but also efficient in effectively addressing the challenges associated with this specific problem domain.

关键词： Photovoltaic cells Optimization methods Computational modeling Mathematical models Accuracy Numerical models Integrated circuit modeling Diodes Sustainable development One diode two diodes three diodes benchmark test function sustainability solar cell optimization mathematical modeling

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An enhanced efficient optimization algorithm (EINFO) for accurate extraction of proton exchange membrane fuel cell parameters

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SOFT COMPUTING 2023年第14期27卷 9619-9638页

作者： Singla, Manish Kumar Hassan, Mohamed H. Gupta, Jyoti Jurado, Francisco Nijhawan, Parag Kamel, Salah Chitkara Univ Inst Engn & Technol Dept Interdisciplinary Courses Engn Rajpura Punjab India Aswan Univ Fac Engn Dept Elect Engn Aswan 81542 Egypt Thapar Inst Engn & Technol Elect & Instrumentat Engn Dept Patiala India Univ Jaen Dept Elect Engn EPS Linares Jaen 23700 Spain

In order to assure accurate modelling, this study presents a new technique for appropriately modelling and simulating a proton exchange membrane fuel cell (PEMFC) system. The PEMFC is a cleaner and more sustainable energy source as compared to fossil fuels. The fundamental idea is to minimize the sum of squared error (SSE) between the estimated and measured output voltage for the Ballard Mark V model in order to identify the model parameters of PEMFC stacks as efficiently as possible using a newly developed meta-heuristic called enhanced efficient optimization algorithm (EINFO). The proposed optimizer is considered an enhanced version of the original INFO algorithm. By balancing the exploration and exploitation phases better, the EINFO algorithm is intended to improve the performance of the original INFO approach and prevent local optima. The new method was tested on 23 benchmark functions and compared to the original INFO algorithm as well as other recently evolved optimizers. The algorithm is examined and compared with some literature meta-heuristics, including the particle swarm optimization, sine cosine algorithm, dragonfly algorithm, atom search optimization, Harris hawks optimization, and efficient optimization algorithm, using 50 independent runs, in terms of convergence speed and least SSE. When compared to other methods, the final findings show that, the suggested technique achieves the fastest convergence speed.

关键词： Proton exchange membrane fuel cell Sustainable development benchmark test function Optimization Sum of square error Parameter estimation Nonparametric test

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Optimizing proton exchange membrane fuel cell parameter identification using enhanced hummingbird algorithm

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2024年 89卷 342-352页

作者： Singla, Manish Kumar Safaraliev, Murodbek Gupta, Jyoti Aljaidi, Mohammad Odinaev, Ismoil Kumar, Ramesh Menaem, Amir Abdel Chitkara Univ Inst Engn & Technol Dept Interdisciplinary Courses Engn Rajpura Punjab India Ural Fed Univ Dept Automated Elect Syst Ekaterinburg 620002 Russia KR Mangalam Univ Sch Engn & Technol Gurgaon 122003 Haryana India Zarqa Univ Dept Comp Sci Zarqa 13110 Jordan Mansoura Univ Elect Engn Dept Mansoura 35516 Egypt Jadara Univ Jadara Univ Res Ctr Irbid Jordan Appl Sci Private Univ Appl Sci Res Ctr Amman Jordan

Fuel cells (FCs) have attracted significant interest due to their versatile applications, but modeling their nonlinear behavior is challenging. This research proposes an Enhanced Artificial Hummingbird Algorithm (EAHA) to identify the seven unknown parameters of proton exchange membrane fuel cell (PEMFC) stacks using their experimental data. The goal is to accurately predict the current/voltage (I/V) curves by minimizing a cost function defined as the sum of squared differences between measured data points and model estimates. The EAHA combines several territorial foraging techniques with a linear regulation mechanism. Its performance is compared to the conventional Artificial Hummingbird Algorithm (AHA) using three common PEMFC modules. Additionally, a comparative analysis is performed against previously published methods and newly developed optimizers like Particle Swarm Optimizer (PSO), Grasshopper Optimization Algorithm (GOA), Atom Search Optimization (ASO), Grey Wolf Optimizer (GWO), and parental algorithm i.e., Artificial Hummingbird Algorithm (AHA). The findings showcase the proposed approach's efficacy relative to existing methods and state-of-the-art optimizers. The two models are taken for the checking of reliability and performance of the PEMFC. The results are also compared with the Non-Parametric tests and it is concluded that the proposed algorithm is far better than the rest of the compared algorithms in both the models.

关键词： Proton exchange membrane fuel cell Optimization Hydrogen Enhanced algorithm Sum of square error benchmark test function Non-parametric test

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A multi-model evaluation of Enhanced Tunicate Swarm Optimization for parameter identification

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ENERGY REPORTS 2024年 12卷 2049-2061页

作者： Singla, Manish Kumar Gupta, Jyoti Kumar, Ramesh Safaraliev, Murodbek Alsharif, Mohammed H. Aly, Ayman A. Kim, Mun-Kyeom Chitkara Univ Inst Engn & Technol Dept Interdisciplinary Courses Engn Rajpura Punjab India Jadara Univ Res Ctr Irbid Jordan KR Mangalam Univ Sch Engn & Technol Gurgaon 122003 Haryana India Ural Fed Univ Dept Automated Elect Syst Ekaterinburg 620002 Russia Sejong Univ Coll Elect & Informat Engn Dept Elect Engn 209 Neungdong Ro Seoul 05006 South Korea Taif Univ Coll Engn Dept Mech Engn Taif 21944 Saudi Arabia Chung Ang Univ Sch Energy Syst Engn 84 Heukseok Ro Seoul 06974 South Korea

Parameter identification for a proton exchange membrane fuel cell (PEMFC) entails employing optimisation techniques to discover the best unknown parameter values required to generate an accurate fuel cell performance prediction model. This technique, known as parameter identification, is important since manufacturers' data- sheets do not usually disclose these values. To address this, the manuscript examines five optimisation strategies, including the suggested algorithm, Enhanced Tunicate Swarm Optimizer (ETSO), for predicting these parameters in PEMFCs. Each technique uses the six unknown parameters as decision variables, aiming to reduce the sum squared error (SSE) between anticipated and observed cell voltages. The data reveal that the suggested strategy outperforms existing approaches and cutting-edge optimizers. The two models are used to assess the dependability and performance of the PEMFC. The results are also compared to the non-parametric tests, and it is found that the suggested method outperforms the other algorithms in both suggested models.

关键词： Proton Exchange Membrane Fuel Cell Optimization Enhanced Algorithm Sum of Square Error benchmark test function Non-Parametric test

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An opposition-based harmony search algorithm for engineering optimization problems

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AIN SHAMS ENGINEERING JOURNAL 2014年第1期5卷 85-101页

作者： Banerjee, Abhik Mukherjee, V. Ghoshal, S. P. Asansol Engn Coll Dept Elect Engn Asansol W Bengal India Indian Sch Mines Dept Elect Engn Dhanbad Jharkhand India Natl Inst Technol Dept Elect Engn Durgapur W Bengal India

Harmony search (HS) is a derivative-free real parameter optimization algorithm. It draws inspiration from the musical improvisation process of searching for a perfect state of harmony. The proposed opposition-based HS (OHS) of the present work employs opposition-based learning for harmony memory initialization and also for generation jumping. The concept of opposite number is utilized in OHS to improve the convergence rate of the HS algorithm. The potential of the proposed algorithm is assessed by means of an extensive comparative study of the numerical results on sixteen benchmark test functions. Additionally, the effectiveness of the proposed algorithm is tested for reactive power compensation of an autonomous power system. For real-time reactive power compensation of the studied model, Takagi Sugeno fuzzy logic (TSFL) is employed. Time-domain simulation reveals that the proposed OHS-TSFL yields on-line, off-nominal model parameters, resulting in real-time incremental change in terminal voltage response profile. (C) 2013 Production and hosting by Elsevier B.V. on behalf of Ain Shams University.

关键词： benchmark test function Harmony search Opposite numbers Optimization Reactive power compensation

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Fault Tolerance through Automatic Cell Isolation Using Three-Dimensional Cellular Genetic Algorithms

Fault Tolerance through Automatic Cell Isolation Using Three...

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2010 IEEE World Congress on Computational Intelligence

作者： Al-Naqi, Asmaa Erdogan, Ahmet T. Arslan, Tughrul Univ Edinburgh Sch Engn & Elect Edinburgh EH9 3JL Midlothian Scotland

ISBN: (纸本)9781424481262

In this paper we propose a new algorithmic approach to achieve fault tolerance based on three dimensional cellular genetic algorithms (3D-cGAs). Herein, 3D architecture is targeted due to its amenability to implementation with current advanced custom silicon chip technology. The proposed approach is designed to exploit the inherent features of a cGA in which the genetic diversity is used as the key factor in identifying and isolating faulty individuals. A new migration schema is proposed as a mitigation technique. Several configurations concerning migration and selection intensity are considered. The approach is tested using four benchmark test functions and two real world problems which present different levels of difficulty. The overall results show that the proposed approach is able to cope with up to 40% soft errors (SEUs).

关键词： 3D architecture advanced custom silicon chip technology automatic cell isolation benchmark test function fault tolerance fault tolerant computing genetic algorithms migration schema mitigation technique selection intensity statistical analysis three dimensional cellular genetic algorithm

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An improved Big Bang-Big Crunch algorithm for structural damage detection

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STRUCTURAL ENGINEERING AND MECHANICS 2018年第6期68卷 735-745页

作者： Yin, Zhiyi Liu, Jike Luo, Weili Lu, Zhongrong Sun Yat Sen Univ Sch Aeronaut & Astronaut Dept Appl Mech & Engn Guangzhou 510006 Guangdong Peoples R China Guangzhou Univ Sch Civil Engn Guangzhou 510006 Guangdong Peoples R China

The Big Bang-Big Crunch (BB-BC) algorithm is an effective global optimization technique of swarm intelligence with drawbacks of being easily trapped in local optimal results and of converging slowly. To overcome these shortages, an improved BB-BC algorithm (IBB-BC) is proposed in this paper with taking some measures, such as altering the reduced form of exploding radius and generating multiple mass centers. The accuracy and efficiency of IBB-BC is examined by different types of benchmark test functions. The IBB-BC is utilized for damage detection of a simply supported beam and the European Space Agency structure with an objective function established by structural frequency and modal data. Two damage scenarios are considered: damage only existed in stiffness and damage existed in both stiffness and mass. IBB-BC is also validated by an existing experimental study. Results demonstrated that IBB-BC is not trapped into local optimal results and is able to detect structural damages precisely even under measurement noise.

关键词： swarm intelligence BB-BC algorithm benchmark test function damage detection frequency domain

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