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multi-objective grey wolf optimizer based on decomposition

EXPERT SYSTEMS WITH APPLICATIONS 2019年 120卷 357-371页

作者： Zapotecas-Martinez, Saul Garcia-Najera, Abel Lopez-Jaimes, Antonio Univ Autonoma Metropolitana Dept Matemat Aplicadas & Sistemas Unidad Cuajimalpa Ave Vasco de Quiroga 4871 Ciudad De Mexico 05348 Mexico

An optimization technique aims to find the best solution to an optimization problem. If the problem considers only one objective function, the best solution will provide the optimal value for such objective. However, if the problem considers two or more objectives, the selection of solutions will not be that straightforward since such objective functions are usually in conflict. For this kind of optimization problems, the use of analytical or exact methods becomes impractical. Thus, heuristic or metaheuristic approaches have to be applied for finding the optimal solutions or, at least, approximate solutions to the optimum. As a consequence, a wide variety of metaheuristics inspired by nature has been proposed for solving optimization problems. Among them, the grey wolf optimizer is a metaheuristic of recent creation that in the last few years has attracted the attention of many researchers. Furthermore, a multi-objective extension of this technique was recently introduced proving its high performance comparable to other multi-objective optimization methods. In this paper, a multi-objective grey wolf optimizer based on the decomposition is introduced. Our proposed algorithm approximates Pareto solutions cooperatively by defining a neighborhood relation among the scalarizing subproblems in which the multi-objective problem is decomposed. The performance of our proposed method is compared against those achieved by six state-of-the-art bio-inspired techniques showing its high performance in both, well-known benchmark problems and two real-life engineering problems. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： multi-objective evolutionary algorithms multi-objective grey wolf optimizer Decomposition-based MOEAs

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multi-objective grey wolf optimizer for the Identification Process of a real MIMO System 15

Multi-objective Grey Wolf Optimizer for the Identification P...

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15th IEEE International Conference on Industry Applications, INDUSCON 2023

作者： Ferrari, Allan Christian Krainski Leandro, Gideon Villar dos Santos Coelho, Leandro Eletrical Engineering Department Federal University of Paraná UFPR Curitiba Brazil Brazil

ISBN: (纸本)9798350314182

This article implements the multi-objective grey wolf optimizer (MOGWO) in the identification process of a multivariable system. For a sequence of 100 runs, MOGWO approach had compared and analyzed with the grey wolf optimizer (GWO), the single-objective version. In the computational simulation, the determination coefficient values and mean square error had used to analyze the quality of the models obtained. In the results, MOGWO showed superior to GWO to the differences between the order of magnitude of the system outputs. The implementation shows that MOGWO and other multi-objective metaheuristics can be an alternative method to solve problems of identification processes of multivariable systems. ©2023 IEEE.

关键词： identification system metaheuristic multi-objective grey wolf optimizer multivariable systems

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multi-objective grey wolf optimizer for Optimal Design of Switching Matrix for Shaded PV array Dynamic Reconfiguration

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IEEE ACCESS 2020年 8卷 159931-159946页

作者： Yousri, Dalia Thanikanti, Sudhakar Babu Balasubramanian, Karthik Osama, Ahmed Fathy, Ahmed Fayoum Univ Fac Engn Elect Engn Dept Al Fayyum 63514 Egypt Univ Tenaga Nas Dept Elect Power Engn Inst Power Engn Jalan IKRAM UNITEN Kajang 43000 Malaysia Keppel Offshore & Marine Ltd Offshore Technol Dev Private Ltd Singapore 629353 Singapore Jouf Univ Fac Engn Elect Engn Dept Sakaka 72388 Saudi Arabia Zagazig Univ Elect Power & Machine Dept Fac Engn Zagazig 44516 Egypt

One of the worst negative phenomena faced by photovoltaic (PV) array is the operation under the shadow phenomenon, which significantly affects the generated power. multiple local maximum power point (MPP) and unique global MPP are generated from the shaded array. Therefore, regular dispersion of the shadow falling on the PV array surface is a vital issue to extract the GMP via reconfiguration of the shaded modules in the array. This article proposes a recent approach based on multi-objective grey wolf optimizer (MOGWO) to reconfigure the shaded PV array optimally. The main objective of the proposed MOGWO is providing the optimal structure for the switching matrix to minimize the row current difference and maximize the output power. The benefits of the proposed strategy is performing a dynamic reconfiguration process which closes to the reality. The proposed method is validated across 9 x 9 PV array with six shade patterns. MOGWO schemes results are compared with TCT and modified SuDoKu based on several statistical metrics. The comparison reveals the superiority of MOGWO in tackling the multi-peak issue in the P-V characteristics with harvesting the highest power levels.

关键词： multi-objective grey wolf optimizer grey wolf optimizer total-cross-tied PV reconfiguration partial shading

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Energy-Saving Distributed Flexible Job Shop Scheduling Optimization with Dual Resource Constraints Based on Integrated Q-Learning multi-objective grey wolf optimizer

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Computer Modeling in Engineering & Sciences 2024年第8期140卷 1459-1483页

作者： Hongliang Zhang Yi Chen Yuteng Zhang Gongjie Xu School of Management Science and Engineering Anhui University of TechnologyMa’anshan243032China Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes Anhui University of TechnologyMa’anshan243032China Department of Industrial Engineering School of Mechanical EngineeringNorthwestern Polytechnical UniversityXi’an710072China

The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing *** DFJSP research only considers machine constraints and ignores worker *** one critical factor of production,effective utilization of worker resources can increase ***,energy consumption is a growing concern due to the increasingly serious environmental ***,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this *** solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling *** further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are *** strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto *** effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 *** results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.

关键词： Distributed flexible job shop scheduling problem dual resource constraints energy-saving scheduling multi-objective grey wolf optimizer Q-learning

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Recent advances in multi-objective grey wolf optimizer, its versions and applications

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NEURAL COMPUTING & APPLICATIONS 2022年第22期34卷 19723-19749页

作者： Makhadmeh, Sharif Naser Alomari, Osama Ahmad Mirjalili, Seyedali Al-Betar, Mohammed Azmi Elnagar, Ashraf Ajman Univ Coll Engn & Informat Technol Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates Univ Sharjah MLALP Res Grp Sharjah U Arab Emirates Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea Al Balqa Appl Univ Al Huson Univ Coll Dept Informat Technol POB 50 Irbid Jordan Univ Sharjah Dept Comp Sci Sharjah U Arab Emirates

In this work, a comprehensive review of the multi-objective grey wolf optimizer (MOGWO) is provided. In multi-objective optimization (MO), more than one objective function must be considered at the same time. To deal with such problems, a priori or a posteriori MOGWO variants have been proposed in the literature. In the a priori model, the multi-objective functions are aggregated into a single objective function by a number of weights. In the posterior model, the multi-objective formulation is maintained and MOGWO is employed to estimate the Pareto optimal solutions representing the best trade-offs between the objectives. Due to the successful performance of MOGWO, it has been widely utilized for MO. This review covers the research growth of MOGWO in terms of a number of researches, topics, top researchers, etc. Furthermore, several versions of MOGWO have been introduced and reviewed with applications in diverse fields. This work also provides a critical analysis to show the shortcomings and limitations of using the basic version of MOGWO followed by several future directions. This review paper will be a base paper for any researcher interested to implement MOGWO in its work.

关键词： multi-objective grey wolf optimizer multi-objective optimization Metaheuristics

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Developed multi-objective grey wolf optimizer with fuzzy logic decision-making tool for direction overcurrent relays coordination

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SOFT COMPUTING 2020年第17期24卷 13305-13317页

作者： Korashy, Ahmed Kamel, Salah Nasrat, Loai Jurado, Francisco Aswan Univ Fac Engn Elect Engn Dept Aswan 81542 Egypt Univ Jaen Dept Elect Engn EPS Linares Jaen 23700 Spain Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Se Chongqing Peoples R China

This paper proposes a new methodology for solving the coordination problem of DOCRs based on multi-objective grey wolf optimizer and fuzzy logic decision-making. In addition to the conventional objective function, a new objective function which aims to minimize the discrimination time between primary and backup relays is proposed. Moreover, the conventional objective function related to minimizing the total operating time of primary and backup relays is considered. The feasibility and performance of the proposed methodology for solving the coordination problem of DOCRs are investigated using two different systems (8-bus system and IEEE-30 bus system). The proposed methodology is compared with other reported methods. The results prove the viability and effectiveness of the proposed methodology to solve the DOCR coordination problem without any miscoordination between primary and backup relays.

关键词： Direction overcurrent relays Optimal coordination multi-objective grey wolf optimizer Fuzzy logic decision-making

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A novel link-based multi-objective grey wolf optimizer for Appliances Energy Scheduling Problem

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2022年第6期25卷 4355-4382页

作者： Makhadmeh, Sharif Naser Abasi, Ammar Kamal Al-Betar, Mohammed Azmi Awadallah, Mohammed A. Abu Doush, Iyad Alyasseri, Zaid Abdi Alkareem Alomari, Osama Ahmad Ajman Univ Coll Engn & Informat Technol Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates Mohamed Bin Zayed Univ Artificial Intelligence MB Machine Learning Dept Abu Dhabi U Arab Emirates Univ Kufa Fac Engn ECE Dept POB 21 Najaf Iraq Al Balqa Appl Univ Al Huson Univ Coll Dept Informat Technol POB 50 Irbid Jordan Al Aqsa Univ Dept Comp Sci POB 4051 Gaza Palestine Univ Sharjah MLALP Res Grp Sharjah U Arab Emirates Amer Univ Kuwait Comp Dept Salmiya Kuwait Yarmouk Univ Comp Sci Dept Irbid Jordan Ajman Univ Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates Univ Warith Al Anbiyaa Coll Engn Karbala Iraq

In this paper, a modified version of the multi-objective grey wolf optimizer (MGWO), known as linked-based GWO (LMGWO), is proposed for the Appliances Energy Scheduling Problem (AESP). The proposed LMGWO is utilized by combining the MGWO searching mechanism with a novel strategy, called neighbourhood selection strategy, to improve local exploitation capabilities. AESP is a problem that can be tackled by searching for the best appliances schedule according to a set of constraints and a dynamic pricing scheme(s) utilized for optimizing energy consumed at a particular period. Three objectives are considered to handle AESP: improving user comfort while reducing electricity bills and maintaining power systems' performance. Therefore, AESP is modelled as a multi-objective optimization problem to handle all objectives simultaneously. In the evaluation results, the LMGWO is tested using a new dataset containing 30 power consumption scenarios with up to 36 appliances. For comparative purposes, the same linked-based neighbourhood selection strategy is utilized with other three optimization algorithms, including particle swarm optimization, salp swarm optimization, and wind-driven algorithm. The performance of the modified versions is compared with each other and that of the original versions to show their improvements. Furthermore, the proposed LMGWO is compared with eight state-of-the-art methods using their recommended datasets to show the viability of the proposed LMGWO. Interestingly, the proposed LMGWO is able to outperform the compared methods in almost all produced results.

关键词： multi-objective grey wolf optimizer Neighbourhood Selection Strategy Linked Based grey wolf optimizer Appliances Energy Scheduling Problem

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Estimation Localization in Wireless Sensor Network Based on multi-objective grey wolf optimizer

Estimation Localization in Wireless Sensor Network Based on ...

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International Conference on Advances in Information and Communication Technology (ICTA)

作者： Trong-The Nguyen Ho Thi Huong Thom Thi-Kien Dao Hai Phong Private Univ Dept Informat Technol Haiphong Vietnam Vietnam Maritime Univ Fac Informat Technol Haiphong Vietnam

ISBN: (纸本)9783319490731;9783319490724

Determining the position of nodes of a network plays an important role in many wireless sensor networks (WSN) applications e.g. in tracking, detecting, monitoring, etc. In this paper, the multi-objective grey wolf optimizer (MGWO) for the estimating approaches of the located nodes in a network is proposed to solve the multi-objective optimization localization issues in WSNs. There two objective functions related to the estimation localization are the distance of nodes and the geometric topology that consider to formula multiobjective optimization localization. The simulation results show considerable improvements in terms of localization accuracy and convergence rate in comparison with those obtained from the other methods.

关键词： multi-objective grey wolf optimizer Swarm intelligent Node localization Wireless sensor networks

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Competitive binary multi-objective grey wolf optimizer for fast compact antenna topology optimization

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Frontiers of Information Technology & Electronic Engineering 2022年第9期23卷 1390-1406页

作者： Jian DONG Xia YUAN Meng WANG School of Computer Science and Engineering Central South UniversityChangsha410083China

We propose a competitive binary multi-objective grey wolf optimizer(CBMOGWO)to reduce the heavy computational burden of conventional multi-objective antenna topology optimization *** method introduces a population competition mechanism to reduce the burden of electromagnetic(EM)simulation and achieve appropriate fitness ***,we introduce a function of cosine oscillation to improve the linear convergence factor of the original binary multi-objective grey wolf optimizer(BMOGWO)to achieve a good balance between exploration and ***,the optimization performance of CBMOGWO is verified on 12 standard multi-objective test problems(MOTPs)and four multi-objective knapsack problems(MOKPs)by comparison with the original BMOGWO and the traditional binary multi-objective particle swarm optimization(BMOPSO).Finally,the effectiveness of our method in reducing the computational cost is validated by an example of a compact high-isolation dual-band multiple-input multiple-output(MIMO)antenna with high-dimensional mixed design variables and multiple *** experimental results show that CBMOGWO reduces nearly half of the computational cost compared with traditional methods,which indicates that our method is highly efficient for complex antenna topology optimization *** provides new ideas for exploring new and unexpected antenna structures based on multi-objective evolutionary algorithms(MOEAs)in a flexible and efficient manner.

关键词： Antenna topology optimization multi-objective grey wolf optimizer High-dimensional mixed variables Fast design

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A discrete multi-objective grey wolf optimizer for the home health care routing and scheduling problem with priorities and uncertainty

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COMPUTERS & INDUSTRIAL ENGINEERING 2022年 169卷

作者： Li, Yan Ye, Chunming Wang, Heping Wang, Fuyu Xu, Xiao Univ Shanghai Sci & Technol Sch Business Shanghai 200093 Peoples R China Anhui Univ Technol Sch Management Sci & Engn Maanshan 243000 Peoples R China

In China, with the acceleration of the population aging, the demand for home health care is growing rapidly. However, the available caregivers are insufficient to serve all the customers who make an appointment in advance. Besides, there are actually some uncertain parameters such as travel and service times when implementing the given schedule. Hence, this paper presents a home health care routing and scheduling problem (HHCRSP) by considering customers' priorities and times uncertainty. A fuzzy multi-objective optimization model is established to maximize the total priority of all visited customers and minimize the total service cost. Jimenez's method is utilized to transform the fuzzy parameters in the model. A discrete multi-objective grey wolf optimizer (DMOGWO) is proposed to solve the model. In the DMOGWO, the heuristic rule of priority maximum earliest service is designed to generate the initial solution. An individual position updating mechanism is developed to make the algorithm suitable for discrete search space. Subsequently, three neighborhood structures are designed to enhance the local search ability of the algorithm. Finally, the DMOGWO is compared with CPLEX, MOGWO, MOPSO, NSGA-II and NSGA-III. Experimental results indicate that the proposed DMOGWO is competitive in convergence, diversity and distribution of the solutions on many instances. This shows that the proposed DMOGWO can effectively solve the home care routing and scheduling problem.

关键词： Home health care optimization Fuzzy service time and travel time multi-objective grey wolf optimizer Customers? priorities

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