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A multi-objective grey wolf optimizer (GWO)-based multi-layer perceptrons (MLPs) trainer for optimal PMUs placement

COMPEL-THE INTERNATIONAL JOURNAL FOR COMPUTATION AND MATHEMATICS IN ELECTRICAL AND ELECTRONIC ENGINEERING 2022年第1期41卷 187-208页

作者： Laouid, Abdelkader Azzeddine Mohrem, Abdelkrim Djalab, Aicha Ziane Achour Univ Djelfa Fac Sci & Technol Appl Automat & Ind Diagnost Lab LAADDI Djelfa Algeria Univ MHamed Bougara Res Lab Electrificat Ind Enterprises Boumerdes Algeria Ziane Achour Univ Djelfa Fac Sci & Technol Dept Elect Engn Djelfa Algeria

Purpose This paper aims to find the minimum possible number of phasor measurement units (PMUs) to achieve maximum and complete observability of the power system and improve the redundancy of measurements, in normal cases (with and without zero injection bus [ZIB]), and then in conditions of a single PMU failure and outage of a single line. Design/methodology/approach An efficient approach operates adequately and provides the optimal solutions for the PMUs placement problem. The finest function of optimal PMUs placement (OPP) should be mathematically devised as a problem, and via that, the aim of the OPP problem is to identify the buses of the power system to place the PMU devices to ensure full observability of the system. In this paper, the grey wolf optimizer (GWO) is used for training multi-layer perceptrons (MLPs), which is known as Grey Wolf Optimizer (GWO) based Neural Network ("GW-NN") to place the PMUs in power grids optimally. Findings Following extensive simulation tests with MATLAB/Simulink, the results obtained for the placement of PMUs provide system measurements with less or at most the same number of PMUs, but with a greater degree of observability than other approaches. practical implications The efficiency of the suggested method is tested on the IEEE 14-bus, 24-bus, New England 39-bus and Algerian 114-bus systems. Originality/value This paper proposes a new method for placing PMUs in the power grids as a multi-objective to reduce the cost and improve the observability of these grids in normal and faulty cases.

关键词： Particle swarm optimization Power transmission systems multi-objective optimization Phasor measurement units (PMUs) Zero injection bus (ZIB) Grey wolf optimizer (GWO) for training multi-layer perceptron (MLP) Redundancy of measurement

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multi-objective optimization of the 3D container stowage planning problem in a barge convoy system

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COMPUTERS & OPERATIONS RESEARCH 2022年 144卷 1页

作者： El Yaagoubi, Amina Charhbili, Mohamed Boukachour, Jaouad Alaoui, Ahmed El Hilali Normandie Univ UNIHAVRE F-76600 Le Havre France Euro Mediterranean Fez Fes Morocco

Mainly motivated by the massification of flows in inland shipping, where several barges can be assembled in convoys to transport a large number of containers to and from the hinterland, we introduce an original extension of the Container Stowage Planning Problem (CSPP) (which we denote the 3D - barge Convoy Container Stowage Planning Problem (3D -CCSPP)). The requirements for stowing all containers in a barge convoy entail additional constraints related to both the containers and the convoy. In this paper, the 3D -CCSPP is dealt with, on the basis of its relation to the 3D Bin-Packing Problem (3D-BPP), in which the filled objects represent the containers, and the bins indicate the convoy barges. We consider the multi-objective aspect of the problem with three realistic functions to be optimized: shifting movements, the convoy stability and the number of real-used barges. This is a new and effective aspect considering the state of the art of CSPP in inland shipping. We develop a multi-objective mathematical formulation of the 3D-CCSPP and propose a novel adaptation of the multi-objective evolutionary algorithm NSGA-II (Non-Dominated Sorting Genetic Algorithm-II) based on a set of heuristics introduced by the BPP resolution methods. Specific encoding method, generation of the initial population and genetic operators are designed based on the mathematical description of the problem. Extensive experiments are then carried out on a varied set of instances. The computational results demonstrate the effectiveness of the proposed approaches, they are evaluated using CPLEX solver and their performance is verified by a set of measurements which are adapted to the problem in real-world applications in relation to each objective function. Additional studies are carried out in sensitivity analysis to provide decision makers with a better knowledge of the problem. They can then choose, from the set of compromise solutions of the Pareto front, the most promising one for practical

关键词： Shifting movements Barge convoy system Containers loading/unloading Stability Bin-packing multi-objective optimization NSGA-II

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A modified energy management strategy for PV/diesel hybrid system to reduce diesel consumption based on artificial protozoa optimizer

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SCIENTIFIC REPORTS 2025年第1期15卷 1-24页

作者： Mohamed, Rania G. Hassan, Amal A. Abdel Aleem, Shady H. E. Inst Aviat Engn & Technol Dept Elect Engn Giza 12658 Egypt Elect Res Inst Dept Photovolta Cells Cairo 11843 Egypt

The photovoltaic (PV)/diesel hybrid system (PV/D-HS) combines solar PV panels with a diesel generator (DG) to meet energy demands, especially in industrial operations. This study introduces an improved energy management strategy designed to optimize the performance of PV/D-HS by reducing diesel consumption, increasing solar energy utilization, and minimizing environmental impact. The strategy dynamically manages power distribution between the PV panels and the DG, adapting to changing solar conditions and energy demands. Doing so reduces the system's reliance on diesel, improves operational efficiency, and supports the integration of cleaner energy sources. Simulation results show significant improvements over traditional approaches: carbon emissions decreased from 62 kg/day with a standalone diesel generator to 38 kg/day, representing a 38% reduction. The solar energy fraction (SEF) increases from 12 to 35%, a 23% improvement in solar energy utilization. These results demonstrate the potential of the proposed strategy to enhance sustainability by lowering greenhouse gas emissions, reducing dependence on fossil fuels, and advancing global efforts to combat climate change. In addition to environmental benefits, the approach reduces operational costs and improves the system's reliability, making it a practical solution for industrial energy needs.

关键词： Artificial protozoa optimizer Modified energy management strategy Hybrid system Photovoltaic Diesel generators multi-objective optimization

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Analysis of a many-objective optimization approach for identifying microservices from legacy systems

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EMPIRICAL SOFTWARE ENGINEERING 2022年第2期27卷 1-31页

作者： Assuncao, Wesley K. G. Colanzi, Thelma Elita Carvalho, Luiz Garcia, Alessandro Pereira, Juliana Alves de Lima, Maria Julia Lucena, Carlos Pontifical Catholic Univ Rio de Janeiro PUC Rio Rio De Janeiro Brazil State Univ Maringa UEM Maringa Parana Brazil Pontifical Catholic Univ Rio de Janeiro Tecgraf Inst Rio De Janeiro Brazil

The expensive maintenance of legacy systems leads companies to migrate such systems to modern architectures. Microservice architectural style has become a trend to modernize monolithic legacy systems. A microservice architecture consists of small, autonomous, and highly-independent services communicating by using lightweight network protocols. To support the designing of microservice architectures, recent studies have proposed either single or multi-objective approaches. In order to improve the effectiveness of existing approaches, we introduced toMicroservices that is a many-objective search-based approach to aid the identification of boundaries among services. In previous studies, we have focused on a qualitative evaluation of the applicability and adoption of the proposed approach from a practical point of view, thus the optimization process itself has not been investigated in depth. In this paper, we extend our previous work by performing a more in-depth analysis of our many-objective approach for microservice identification. We compare our approach against a baseline approach based on a random search using a set of performance indicators widely used in the literature of many-objective optimization. Our results are validated through a real-world case study. The study findings reveal that (i) the criteria optimized by our approach are interdependent and conflicting;and (ii) all candidate solutions lead to better performance indicators in comparison to random search. Overall, the proposed many-objective approach for microservice identification yields promising results, which shed light on insights for further improvements.

关键词： Microservice architecture Many-criteria optimization Industrial case study Performance indicators

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A Mathematical Model and Two Fuzzy approaches Based on Credibility and Expected Interval for Project Cost-Quality-Risk Trade-Off Problem in Time-Constrained Conditions

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ALGORITHMS 2022年第7期15卷 226-226页

作者： Haghighi, Mohammad Hossein Mousavi, Seyed Meysam Shahed Univ Dept Ind Engn Tehran *** Iran

To successfully finalize projects and attain their determined purposes, it is indispensable to control all success criteria of a project. The time-cost trade-off (TCT) is known as a prevalent and efficient approach applied when the planned finish date of a project is not admitted by stakeholders, and consequently, the project duration must be decreased. This paper proposes a new mathematical model under fuzzy uncertainty to deal with the project cost-risk-quality trade-off problem (CRQT) under time constraints. Because of the unique nature of projects and their uncertain circumstances, applying crisp values for some project parameters does not seem appropriate. Hence, this paper employs fuzzy sets to resolve these weaknesses. In this study, two approaches are presented to handle proposed fuzzy multi-objective mathematical model. First, fuzzy credibility theory and then goal attainment method are used. Secondly, the model is solved by a fuzzy method based on expected interval and value and augmented e-constraint method. A project from the literature review is adopted and solved by the presented methodology. The results demonstrate the accuracy and efficiency of the two proposed approaches for the introduced practical problem.

关键词： time-cost trade-off (TCT) fuzzy credibility theory multi-objective optimization augmented e-constraint method goal attainment method

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Adaptive Optimal multi-Surface Back-Stepping Sliding Mode Control Design for the Takagi-Sugeno Fuzzy Model of Uncertain Nonlinear System With External Disturbance

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IEEE ACCESS 2022年 10卷 14680-14690页

作者： Soltanian, Farzad Shasadeghi, Mokhtar Mobayen, Saleh Fekih, Afef Shiraz Univ Technol Elect Engn Fac Shiraz 715555313 Iran Univ Zanjan Dept Elect Engn Zanjan *** Iran Natl Yunlin Univ Sci & Technol Future Technol Res Ctr Touliu 64002 Yunlin Taiwan Univ Louisiana Lafayette Dept Elect & Comp Engn Lafayette LA 70504 USA

This paper proposes a novel scheme of the multi-objective robust control design for a class of uncertain nonlinear systems in strict-feedback-form based on Takagi-Sugeno fuzzy model (TSFM). The nonlinear system contains both the matched and the unmatched uncertainties and also subjected to the external disturbances. The TSFM provides the generalization of the linear systems concepts to the nonlinear systems field in a convex framework. First, a new sliding surface is defined using a convex combination of the surfaces which are defined for each fuzzy rule consequence local linear subsystems. Then, their gains are designed optimally via a generalized eigenvalue problem (GEVP). Also, the upper-bounds of the matched and unmatched uncertainties are estimated using the adaptive update laws. The multi-objective control aims not only to satisfy the H-2-optimization performance, but also, alpha-stability region is formulated to improve the transient response performance. The H-2-optimization characterization and alpha-stability design conditions are derived in terms of new linear matrix inequalities (LMIs) conditions. Finally, the effectiveness of the proposed approach is demonstrated by considering a comparative practical example.

关键词： Back-stepping sliding mode control approaches H-2-performance LMI regional pole placement Takagi-Sugeno fuzzy model unknown bounded uncertainty

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A self-organizing map approach for constrained multi-objective optimization problems

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COMPLEX & INTELLIGENT SYSTEMS 2022年第6期8卷 5355-5375页

作者： He, Chao Li, Ming Zhang, Congxuan Chen, Hao Zhong, Peilong Li, Zhengxiu Li, Junhua Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 210016 Peoples R China Nanchang Hangkong Univ Sch Informat Engn Nanchang 330063 Jiangxi Peoples R China Nanchang Hangkong Univ Key Lab Nondestruct Testing Minist Educ Nanchang 330063 Jiangxi Peoples R China

There exist many multi-objective optimization problems (MOPs) containing several inequality and equality constraints in practical applications, which are known as CMOPs. CMOPs pose great challenges for existing multi-objective evolutionary algorithms (M0EAs) since the difficulty in balancing the objective minimization and constraint satisfaction. Without loss of generality, the distribution of the Pareto set for a continuous m-objective CMOP can be regarded as a piecewise continuous manifold of dimension (m - 1). According to this property, a self-organizing map (SOM) approach for constrained multiobjective optimization problems is proposed in this article. In the proposed approach, we adopt the strategy of two population evolution, in which one population is evolved by considering all the constraints and the other population is used to assist in exploring the areas. In the evolutionary stage, each population is assigned a self-organizing map for discovering the population distribution structure in the decision space. After the topological mapping, we utilize the extracted neighborhood relationship information to generate promising offspring solutions. Afterwards, the neuron weight vectors of SOM are updated by the objective vectors of the surviving offsprings. Through the proposed approach, we can make the population efficiently converge to the feasible region with suitable levels of diversity. In the experiments, we compare the proposed method with several state-of-the-art approaches by using 48 benchmark problems. The evaluation results indicate that the overwhelmingly superior performance of the proposed method over the other peer algorithms on most of the tested problems. The source code is available at https://***/hccccc92918/CMOSMA.

关键词： Constrained multi-objective optimization Self-organizing map Evolutionary algorithms

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multi-objective Branch and Bound based on Decomposition

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and Systems 2022年第3期142卷 373-381页

作者： Kaho, Tomoki Watanabe, Shinya Sakakibara, Kazutoshi Muroran Institute of Technology 27-1 Mizumoto-cho Hokkaido Muroran050-8585 Japan Toyama Prefectural University 5180 Kurokawa Toyama Imizu939-0398 Japan

Traditional multi-objective branch-and-bound approaches to multi-objective mixed-integer linear programming (MOMILP) problems are very expensive to search due to the huge number of Pareto-optimal solutions. In this research, we propose a practical method of dividing a multi-objective problem into multiple single-objective problems by weight vectors and applying the branch-and-bound method (BB) to each subproblem. The proposed method is named multi-objective branch-and-bound based on decomposition (MOBB/D) because it is a combination of the concept of multi-objective evolutionary algorithm based on decomposition (MOEA/D) and BB. The most important feature of MOBB/D is to obtain many Pareto solutions efficiently by sharing information between nearby subproblems in MOMILP problems. In this paper, we describe an approach to share tables in the simplex method as an example of information. Moreover, MOBB/D can control computation cost for solving MOMILP problems by adjusting the number of obtained Pareto solutions. To verify the effectiveness of the proposed method, we compared the search performance of MOBB/D with and without the use of neighborhood information. © 2022 The Institute of Electrical Engineers of Japan.

关键词： multiobjective optimization

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Life cycle assessment-based optimization approaches for sustainable disposal of municipal solid waste

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SUSTAINABLE CITIES AND SOCIETY 2022年第0期79卷 103665-103665页

作者： Xu, Jiuping Li, Ruolan Shi, Yi Deng, Yawen Sichuan Univ Uncertainty Decis Making Lab Chengdu 610064 Peoples R China

Increasing municipal solid waste (MSW) has caused severe environmental pollution and social problems, which require effective treatment and resource utilization. Therefore, combined with advanced sustainable gasification technology, a classified MSW collection, transportation, and treatment process was established in this study. To optimize MSW management, a mathematical multi-objective model was established to optimize the revenue from the value-added energy electricity products and control greenhouse gas (GHG) emission. Life cycle assessment (LCA) was introduced into the objective evaluation to quantitatively evaluate the environmental greenhouse effect caused by gasification technology. Then, to further explore regional variations in gasification implementation, four practical case studies were presented to demonstrate the applicability and efficiency of the proposed model in selecting the disposal processes, determining volumes, generating income, and controlling GHG emissions. The sensitivity analysis of the gasification ratio (GR) in different cases shows that an increase in GR will improve economic benefits and reduce GHG emissions. In addition, the optimal GR varies in countries;for example, it was 0.6-0.7 in China, 0.9 in the USA, 0.6-0.8 in the UK and Japan. Therefore, a practical and effective management method for MSW gasification based on LCA is proposed, which can assist in the development of targeted, valid emission reduction measures for MSW gasification to reduce the environmental impact and provide practical guidance for the MSW gasification implementation.

关键词： Municipal solid waste Sustainable disposal Life cycle assessment GHG emission optimization Gasification

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Solving the Workshop Layout optimization Problem Based on a Constrained multimodal multiobjective Evolutionary Algorithm 6

Solving the Workshop Layout Optimization Problem Based on a ...

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6th International Conference on Data-Driven optimization of Complex Systems (DOCS)

作者： Yue, Caitong Wu, Wenhao Liang, Jing Yu, Kunjie Lin, Hongyu Zhang, Tuo Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou Peoples R China

ISBN: (纸本)9798350377859;9798350377842

Layout optimization (LO) is gaining increasing attention as competition among enterprises intensifies and the demand for cost reduction and efficiency improvement continues to grow. LO poses its peculiar challenges with Many-dimensions, Many-constraints and Many-optima. Although many approaches have been proposed for various LO applications, they focus only on the diversity and convergence of the objective space, neglecting the diversity of solutions, which is detrimental to solving potential issues. Therefore, this paper proposes a constrained multimodal multiobjective optimization (CMMO) framework based on dual-population multi-task collaborative evolution. This framework assists in solving the workshop layout problem by creating an auxiliary task population and transferring knowledge to support the optimization process. Additionally, the dynamic narrowing of constraint boundaries and gradual expansion of the contrast neighborhood for the auxiliary tasks ensure a high degree of correlation with the main task, continuously providing supplementary evolutionary directions. Ultimately, compared to other constrained multimodal multiobjective algorithms and two classic genetic algorithms, the proposed algorithm proves to be more competitive in practical layout optimization problems.

关键词： Workshop layout optimization evolutionary multitasking auxiliary task

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