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metaheuristic optimization for Sustainable Unrelated Parallel Machine Scheduling: A Concise Overview With a Proof-of-Concept Study

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IEEE ACCESS 2024年 12卷 3386-3416页

作者： Ezugwu, Absalom El-Shamir North West Univ Unit Data Sci & Comp ZA-2520 Potchefstroom South Africa

Sustainable development has emerged as a global priority, and industries are increasingly striving to align their operations with sustainable practices. Parallel machine scheduling (PMS) is a critical aspect of production planning that directly impacts resource utilization and operational efficiency. In this paper, we investigate the application of metaheuristic optimization algorithms to address the unrelated parallel machine scheduling problem (UPMSP) through the lens of sustainable development goals (SDGs). The primary objective of this study is to explore how metaheuristic optimization algorithms can contribute to achieving sustainable development goals in the context of UPMSP. We examine a range of metaheuristic algorithms, including genetic algorithms, particle swarm optimization, ant colony optimization, and more, and assess their effectiveness in optimizing the scheduling problem at hand. The evaluation of the algorithms focuses on their ability to improve the optimization of job-to-machine assignments, enabling industries to efficiently minimize the overall makespan of scheduling tasks. This, in turn, leads to waste reduction and enhanced energy efficiency. To conduct a comprehensive analysis, we consider UPMSP instances that incorporate sustainability-related constraints and objectives. We assess the algorithms' performance in terms of solution quality, convergence speed, robustness, and scalability, while also examining their implications for sustainable resource allocation and environmental stewardship. The findings of this study provide insights into the efficacy of metaheuristic optimization algorithms for addressing UPMSP with a focus on sustainable development goals. By leveraging these algorithms, industries can optimize scheduling decisions to minimize waste and enhance energy efficiency. The practical implications of this research are valuable for decision-makers, production planners, and researchers seeking to achieve sustainable development goal

关键词： optimization metaheuristics Job shop scheduling Sustainable development Parallel machines Industries Resource management Sustainable development goals metaheuristic optimization algorithms unrelated parallel machine scheduling resource utilization energy consumption environmental impact

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Optimal placement of FACTS devices and power-flow solutions for a power network system integrated with stochastic renewable energy resources using new metaheuristic optimization techniques

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2021年第13期45卷 18786-18809页

作者： Nusair, Khaled Alasali, Feras Hayajneh, Ali Holderbaum, William Natl Elect Power Co Protect & Metering Dept Amman Jordan Hashemite Univ Dept Elect Engn Zarqa 13113 Jordan Aston Univ Aston Inst Mat Res Birmingham W Midlands England

Flexible AC transmission systems (FACTS) and optimal power-flow (OPF) solutions play an important role in solving power operation problems. The volatile nature of the power generation profiles from renewable energy sources, solar and wind systems, and determining the optimal locations and sizes of FACTS devices increase the complexity of the OPF problems in modern power network models, such as transmission power loss, power generation operation cost and voltage deviation, as a highly nonlinear-nonconvex optimization problem. Therefore, this article introduces and employs four new independent, reliable and efficient optimization algorithms inspired by nature and biological nature, namely: Slime Mould Algorithm (SMA), Artificial Ecosystem-based optimization (AEO), Marine Predators Algorithm (MPA) and Jellyfish Search (JS), for solving both multi- and single-OPF objective problems for a power network incorporating FACTS and stochastic renewable energy sources. The proposed new metaheuristic optimization techniques are compared to the common and available alternatives in the literature, Particle Swarm optimization (PSO), Moth Flame optimization (MFO) and Grey Wolf Optimizer (GWO), using IEEE 30-bus test system. To consider and address the challenges of the OPF in modern power network models, the proposed optimization techniques tested under different operation cases such as an increasing in the load, with and without FCTAS and renewable energy sources, different renewable energy sources locations on the network. The result showed that the MPA, SMA, JS and AEO algorithms are more effective solvers for the OPF problems cases compared to the PSO, GWO and MFO algorithms. For example, the AEO obtained 0.0844 p.u. in case of minimizing the voltage deviation compared to 0.1155 p.u. for PSO, which means that the AEO algorithm improved the voltage deviation term by 27% compared to the PSO algorithm.

关键词： flexible AC transmission systems (FACTS) metaheuristic optimization algorithms optimal power-flow (OPF) renewable energy sources

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metaheuristic optimization Algorithm-Based Enhancement of Photovoltaic Energy System Performance

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2023年第11期48卷 14789-14810页

作者： Nasr, Rowan Abou-Zalam, Belal Nabil, Essam Menoufia Univ Fac Elect Engn Dept Ind Elect & Control Engn Menoufia Egypt

Photovoltaic panels (PVs) are solar panels that turn sunlight into electricity. Tracking the maximum power point (MPP) of PVs is especially important for economic issues. The most popular maximum power point tracking techniques are perturb and observation, hill climbing, constant voltage, parasitic capacitance, and incremental conductance (INC). However, these techniques give oscillated results about the MPP that causes low accuracy, especially in partial shading conditions. This paper is discussing the enhancement of photovoltaic energy system performance using several metaheuristic optimization algorithms. Using MATLAB SIMULINK, a comparative analysis of several algorithms for tracking MPP of PV systems under partially shadowed conditions was conducted. The metaheuristic optimization algorithms that are used in this paper are particle swarm optimization (PSO), cuckoo search algorithm (CSA), grey wolf optimization (GWO), and whale optimization algorithm (WOA). The results show that using WOA and GWO achieved the best efficiency in tracking MPP, whereas, using PSO and CSA achieved lower efficiency in tracking MPP. The MPP of the PV system was not tracked by INC under the partial shaded conditions.

关键词： Photovoltaic system MPPT Incremental conductance INC metaheuristic optimization algorithms Particle swarm optimization PSO Cuckoo search algorithm CSA Grey wolf optimization GWO Whale optimization algorithm WOA

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metaheuristic optimization approaches to predict shear-wave velocity from conventional well logs in sandstone and carbonate case studies

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JOURNAL OF GEOPHYSICS AND ENGINEERING 2018年第3期15卷 1071-1083页

作者： Niri, Mohammad Emami Kolajoobi, Rasool Amiri Arbat, Mohammad Khodaiy Raz, Mahdi Shahbazi Univ Tehran Inst Petr Engn Coll Engn Tehran Iran

Seismic wave velocities, along with petrophysical data, provide valuable information during the exploration and development stages of oil and gas fields. The compressional-wave velocity (VP) is acquired using conventional acoustic logging tools in many drilled wells. But the shear-wave velocity (VS) is recorded using advanced logging tools only in a limited number of wells, mainly because of the high operational costs. In addition, laboratory measurements of seismic velocities on core samples are expensive and time consuming. So, alternative methods are often used to estimate VS. Heretofore, several empirical correlations that predict VS by using well logging measurements and petrophysical data such as VP, porosity and density are proposed. However, these empirical relations can only be used in limited cases. The use of intelligent systems and optimization algorithms are inexpensive, fast and efficient approaches for predicting VS. In this study, in addition to the widely used Greenberg-Castagna empirical method, we implement three relatively recently developed metaheuristic algorithms to construct linear and nonlinear models for predicting VS: teaching-learning based optimization, imperialist competitive and artificial bee colony algorithms. We demonstrate the applicability and performance of these algorithms to predict Vs using conventional well logs in two field data examples, a sandstone formation from an offshore oil field and a carbonate formation from an onshore oil field. We compared the estimated VS using each of the employed metaheuristic approaches with observed VS and also with those predicted by Greenberg-Castagna relations. The results indicate that, for both sandstone and carbonate case studies, all three implemented metaheuristic algorithms are more efficient and reliable than the empirical correlation to predict VS. The results also demonstrate that in both sandstone and carbonate case studies, the performance of an artificial bee colony algorithm i

关键词： compressional-wave velocity shear-wave velocity petrophysical properties empirical relations metaheuristic optimization algorithms

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Performance enhancement of a tuned mass damper for vibration suppression of an offshore jacket platform utilizing a novel metaheuristic optimization technique

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OCEAN ENGINEERING 2022年 263卷

作者： Shehata, Mohamed Abdelnaeem, Mohamed Mokhiamar, Ossama Alexandria Univ Fac Engn Mech Engn Dept Alexandria 21544 Egypt

Offshore platforms are key structures in energy provision that are subjected to continuous and harsh environmental loadings. A tuned mass damper (TMD) has been proposed as a robust and effective vibration control method for marine structures. However, selecting optimum TMD parameters under certain constraints may become a multimodal optimization problem. Several studies focused on finding the TMD optimum parameters using different metaheuristic optimization algorithms. Nonetheless, it becomes a necessity to find an answer to the question: which algorithm can find the most effective TMD parameters to minimize the structure response? This paper presents the use of a novel metaheuristic optimizer, the Improved Grey Wolf Optimizer (IGWO), for optimum tuning of a TMD aimed at reducing the wave-induced vibration level of a jacket type platform. Moreover, a comprehensive performance analysis of four different metaheuristic algorithms for optimizing a TMD has been investigated. They are IGWO, popular algorithms such as Genetic Algorithm (GA), Particle Swarm optimization (PSO), and Whale optimization Algorithm (WOA). In order to achieve this purpose, a finite element (FE) model representing a prototype of a jacket-type platform under the influence of irregular waves is numerically solved in COMSOL Multiphysics. Irregular waves are presented using the JONSWAP spectrum and hydrodynamic forces are modeled using Morison's equation. The comparative algorithms are evaluated for the independent minimization of three different objective functions. The target parameters of TMD to be tuned are the mass ratio (mu), the frequency ratio (f(opt)), and the damping ratio (zeta(opt)). Finally, the optimized TMD will be applied to the FE model and the resultant response will be investigated. The results demonstrate the superior ability of the IGWO optimizer to optimally tune a TMD attached to marine structures compared with the other algorithms. In addition, IGWO proved its unique ability to

关键词： Offshore jacket platform Wave-induced vibration Dynamic time history analysis Tuned mass damper metaheuristic optimization algorithms Improved grey wolf optimizer

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Optimal parameter identification strategy applied to lithium-ion battery model for electric vehicles using drive cycle data

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ENERGY REPORTS 2024年 11卷 2049-2058页

作者： Ghadbane, Houssam Eddine Rezk, Hegazy Ferahtia, Seydali Barkat, Said Al-Dhaifallah, Mujahed Univ 8 Mai 1945 Electrotech & Automat Engn Dept Elect Engn Lab Guelma LGEG Guelma 24000 Algeria Prince Sattam Bin Abdulaziz Univ Coll Engn Wadi Alddawasir Dept Elect Engn Al Kharj Saudi Arabia Univ Nantes Ind Engn Dept IREENA Lab Nantes France Univ Msila Elect Engn Lab Elect Engn Dept Msila Algeria King Fahd Univ Petr & Minerals Control & Instrumentat Engn Dept Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals IRC Sustainable Energy Syst IRC SES Dhahran 31261 Saudi Arabia

The optimal parameter identification of lithium -ion (Li -ion) battery models is essential for accurately capturing battery behavior and performance in electric vehicle (EV) applications. Traditional methods for parameter identification often rely on manual tuning or trial-and-error approaches, which can be time-consuming and yield suboptimal results. In recent years, metaheuristic optimization algorithms have emerged as powerful tools for efficiently searching and identifying optimal parameter values. This paper proposes an optimal parameter identification strategy using a metaheuristic optimization algorithm applied to a Shepherd model for EV applications. The identification technique that was based on the Self -adaptive Bonobo Optimizer (SaBO) performed extremely well when it came to the process of identifying the battery's unidentified properties. Because of this, the overall voltage error of the suggested identification technique has been lowered to 4.2377 x 10-3, and the root mean square error (RMSE) between the model and the data has been calculated to be 8.64 x 10-3. In addition, compared to the other optimization methods, the optimization efficiency was able to attain 96.6%, which validated its efficiency.

关键词： Electric vehicles Li-ion battery metaheuristic optimization algorithms Parameters identification

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Community detection from biological and social networks: A comparative analysis of metaheuristic algorithms

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APPLIED SOFT COMPUTING 2017年 50卷 194-211页

作者： Atay, Yilmaz Koc, Ismail Babaoglu, Ismail Kodaz, Halife Selcuk Univ Fac Engn Dept Comp Engn Konya Turkey

In order to analyze complex networks to find significant communities, several methods have been proposed in the literature. Modularity optimization is an interesting and valuable approach for detection of network communities in complex networks. Due to characteristics of the problem dealt with in this study, the exact solution methods consume much more time. Therefore, we propose six metaheuristic optimization algorithms, which each contain a modularity optimization approach. These algorithms are the original Bat Algorithm (BA), Gravitational Search Algorithm (GSA), modified Big BangBig Crunch algorithm (BB-BC), improved Bat Algorithm based on the Differential Evolutionary algorithm (BADE), effective Hyperheuristic Differential Search Algorithm (HDSA) and Scatter Search algorithm based on the Genetic Algorithm (SSGA). Four of these algorithms (HDSA, BADE, SSGA, BB-BC) contain new methods, whereas the remaining two algorithms (BA and GSA) use original methods. To clearly demonstrate the performance of the proposed algorithms when solving the problems, experimental studies were conducted using nine real-world complex networks - five of which are social networks and the rest of which are biological networks. The algorithms were compared in terms of statistical significance. According to the obtained test results, the HDSA proposed in this study is more efficient and competitive than the other algorithms that were tested. (C) 2016 Elsevier B.V. All rights reserved.

关键词： metaheuristic optimization algorithms Community detection Biological networks Social networks Modularity

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GOOSE algorithm: a powerful optimization tool for real-world engineering challenges and beyond

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EVOLVING SYSTEMS 2024年第4期15卷 1249-1274页

作者： Hamad, Rebwar Khalid Rashid, Tarik A. Erbil Polytech Univ Erbil Tech Engn Coll Dept Informat Syst Engn Erbil Iraq Univ Kurdistan Hewler Comp Sci & Engn Dept Erbil Iraq

This study proposes the GOOSE algorithm as a novel metaheuristic algorithm based on the goose's behavior during rest and foraging. The goose stands on one leg and keeps his balance to guard and protect other individuals in the flock. The GOOSE algorithm is benchmarked on 19 well-known benchmark test functions, and the results are verified by a comparative study with genetic algorithm (GA), particle swarm optimization (PSO), dragonfly algorithm (DA), and fitness dependent optimizer (FDO). In addition, the proposed algorithm is tested on 10 modern benchmark functions, and the gained results are compared with three recent algorithms, such as the dragonfly algorithm, whale optimization algorithm (WOA), and salp swarm algorithm (SSA). Moreover, the GOOSE algorithm is tested on 5 classical benchmark functions, and the obtained results are evaluated with six algorithms, such as fitness dependent optimizer (FDO), FOX optimizer, butterfly optimization algorithm (BOA), whale optimization algorithm, dragonfly algorithm, and chimp optimization algorithm (ChOA). The achieved findings attest to the proposed algorithm's superior performance compared to the other algorithms that were utilized in the current study. The technique is then used to optimize Welded beam design and Economic Load Dispatch Problems, Pressure vessel Design Problems, and the Pathological IgG Fraction in the Nervous System, four renowned real-world challenges. The outcomes of the engineering case studies illustrate how well the suggested approach can optimize issues that arise in the real-world.

关键词： GOOSE algorithm metaheuristic optimization algorithms Evaluation study Benchmark test functions Real-World engineering challenges Pathological IgG fraction in the nervous system

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A Review of metaheuristic Techniques for Optimal Integration of Electrical Units in Distribution Networks

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IEEE ACCESS 2021年 9卷 5046-5068页

作者： Adetunji, Kayode E. Hofsajer, Ivan W. Abu-Mahfouz, Adnan M. Cheng, Ling Univ Witwatersrand Sch Elect & Informat Engn ZA-2000 Johannesburg South Africa Council Sci & Ind Res CSIR ZA-0184 Pretoria South Africa

The optimal integration of electrical units, such as distributed generation units, power electronic devices, and electric vehicles, is a significant development of smart grids. This development has effectively transformed the traditional grid system, promising numerous advantages for economic values and autonomous energy source control. In smart grids development, metaheuristic algorithms are one of the optimization algorithms that have been applied extensively to mitigate the accompanying problems such as voltage instability, power loss, and high installation cost. This paper presents a comprehensive review of metaheuristic techniques for the optimal integration of electrical units in distribution networks, considering different phases of the optimization process. These include the approaches for handling of crucial objective functions and the optimal integration methods for different electrical units. This review shows a need for more research on developing efficient metaheuristic algorithms and the effective handling of multiple objective functions.

关键词： Decision making distributed generation distribution networks electric vehicles metaheuristic optimization algorithms multi-objective optimization optimal location and sizing smart grids

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Performance-based seismic design of steel frames using constraint control method

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ADVANCES IN STRUCTURAL ENGINEERING 2019年第12期22卷 2648-2661页

作者： Mansouri, Seyed Fazlolah Maheri, Mahmoud R. Islamic Azad Univ Dept Engn Sci & Res Branch Tehran Iran Shiraz Univ Dept Civil Engn Zand Av Shiraz *** Iran

In this article, the optimum performance-based seismic design of steel frames is performed using the novel constraint control method. This method is based on a simple concept generally used by the engineers in structural design. In this method, the most conservative member sections are initially selected and by gradually reducing the size of the sections through controlling the problem constraints, the solution tends to an optimum design. The capacity curve of the structure is evaluated through static nonlinear analysis and used for the seismic assessment, and the structural weight is optimized by controlling relative displacement constraints at performance levels of operational, immediate occupancy, life safety and collapse prevention. The performance and efficiency of the proposed algorithm in solving for optimum performance-based seismic design are assessed through solving three benchmark problems. The results show that using constraint control method drastically reduces the number of structural analyses required to reach a solution, compared to the more commonly used metaheuristic optimization methods, while producing comparable optimum solutions. For this reason, the constraint control method is found to be particularly suitable as an optimizer for solving solution-extensive problems, such as performance-based optimum design of structures.

关键词： constraint control method global search metaheuristic optimization algorithms nonlinear pushover optimum design performance-based design steel frames

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