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KMOEA: A Knowledge-Based multiobjective algorithm for Distributed Hybrid Flow Shop in a Prefabricated System

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2022年第8期18卷 5318-5329页

作者： Li, Jun-Qing Chen, Xiao-Long Duan, Pei-Yong Mou, Jian-Hui Shandong Normal Univ Sch Informat & Engn Jinan 250014 Peoples R China Liaocheng Univ Sch Comp Sci Liaocheng 252059 Shandong Peoples R China Yantai Univ Sch Math & Informat Sci Yantai 264005 Peoples R China Yantai Univ Sch Mech Elect & Automot Engn Yantai 264005 Peoples R China

In this article, a distributed hybrid flow shop scheduling problem with variable speed constraints is considered. To solve it, a knowledge-based adaptive reference points multiobjective algorithm (KMOEA) is developed. In the proposed algorithm, each solution is represented with a 3-D vector, where the factory assignment, machine assignment, operation scheduling, and speed setting are encoded. Then, four problem-specific lemmas are proposed, which are used as the knowledge to guide the main components of the algorithm, including the initialization, global, and local search procedures. Next, an efficient initialization approach is presented, which is embedded with several problem-related initialization rules. Furthermore, a novel Pareto-based crossover heuristic is designed to learn from more promising solutions. To enhance the local search abilities, a speed adjustment local search method is investigated. Finally, a set of instances generated based on the realistic prefabricated production system is tested to verify the efficiency and effectiveness of the proposed algorithm.

关键词： Production facilities Production Casting Job shop scheduling Energy consumption Heuristic algorithms Power demand Distributed hybrid flow shop energy consumption multiobjective algorithm prefabricated system variable speed

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Robust and Reliable Design Alternatives to Water Distribution Networks: Introducing a Penalty-Free Hybrid Metaheuristic multiobjective algorithm with a Posterior Performance Investigation Model

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2024年第12期150卷 04024055-04024055页

作者： Poojitha, S. N. Jothiprakash, V. Natl Inst Technol Calicut Dept Civil Engn Kozhikode 673601 Kerala India Indian Inst Technol Dept Civil Engn Mumbai 400076 Maharashtra India

The present study introduces a novel penalty-free hybrid metaheuristic, differential evolution-krill herd algorithm (DE-KHA) a multiobjective algorithm (MOA) for the reliability-based optimal design of water distribution networks (WDNs). The selection mechanism of the DE-KHA MOA is equipped with nondominated sorting and density estimation schemes to obtain a Pareto front with diverse trade-off solutions. The introduced penalty-free scheme allows the algorithm to search for hydraulically functional nondominant solutions. With this implemented framework of the DE-KHA MOA, the WDN design problem is formulated with two conflicting objectives, (1) minimizing pipe investment cost;and (2) maximizing network's reserve through flow entropy (SF), a surrogate reliability measure. The application and computational efficiency of the proposed MOA are demonstrated by employing two well-established benchmark case studies. Parallelly a trial-based approach for conducting sensitivity analysis for MOAs is demonstrated. The computational results manifest the efficacy of the penalty-free DE-KHA MOA in resulting in the Pareto front comprising the hydraulic feasible nondominant solutions of disparate trade-off relationships. As well, the results highlight the applicability of the proposed sensitivity analysis approach in improving the convergence behavior of the MOA. Following the reliability-based design, to assess the flexibility of the solved nondominant solutions under critical scenarios above design standards, the study performed a posterior performance investigation using pressure-driven analysis. The results demonstrate the effectiveness of the proposed approach with supported subjective knowledge in selecting robust design alternatives that are mechanically and hydraulically reliable. Moreover, the proposed approach eases the effort and perplexing state of handling increased nondominant design options with larger-size WDNs. The present study introduces a computationally efficient

关键词： Hydraulic uncertainties multiobjective algorithm Penalty-free mechanism Posterior performance investigation Sensitivity analysis Reliability-based design Robust design alternatives

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multiobjective Placement of Edge Servers in MEC Environment Using a Hybrid algorithm Based on NSGA-II and MOPSO

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IEEE INTERNET OF THINGS JOURNAL 2024年第18期11卷 29819-29837页

作者： Bahrami, Bahareh Khayyambashi, Mohammad Reza Mirjalili, Seyedali Univ Isfahan Fac Comp Engn Dept Comp Architecture Esfahan *** Iran Univ Isfahan Fac Comp Engn Dept Software Engn Esfahan *** Iran Torrens Univ Australia Brisbane Ctr Artificial Intelligence Res & Optimisat Fortitudevalley Qld 4006 Australia Obuda Univ Univ Res & Innovat Ctr H-1034 Budapest Hungary VSB TU Ostrava Fac Elect Engn & Comp Sci Ostrava 70080 Czech Republic

In a mobile edge computing (MEC) environment, latency and energy consumption can be reduced by offloading tasks from mobile devices to edge servers (ESs) instead of remote cloud servers. The placement of ESs closest to end users can improve Quality of Experience and Quality of Service. Additionally, the deployment of additional servers to cover each user will ensure that user requirements are met even if the designated ES is unable to provide service. Therefore, the use of additional ESs can improve network robustness. However, edge service providers tend to cover all areas of a city with a minimum number of servers to save costs. Since the coverage zones of ESs can overlap, fewer additional ESs need to be deployed to support overlapping areas, resulting in cost savings. This article examines the problem of ES placement and proposes a new model to simultaneously optimize network latency, coverage with overlap control, and operational expenditures (OPEXs) of the MEC. In addition, a binary version of the hybrid NSGA II-MOPSO algorithm called BHNM is proposed to obtain the approximated Pareto front. Results based on the real-world data set from Shanghai Telecom show that the BHNM algorithm outperforms the binary MOPSO with turbulence (BMOPSO-T) and NSGA-II algorithms in terms of Pareto front diversity.

关键词： Edge server (ES) mobile edge computing (MEC) MOPSO multiobjective algorithm NSGA-II placement

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A Steady-State algorithm for Solving Expensive multiobjective Optimization Problems With Nonparallelizable Evaluations

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2023年第5期27卷 1544-1558页

作者： Rahi, Kamrul Hasan Singh, Hemant Kumar Ray, Tapabrata Univ New South Wales Sch Engn & Informat Technol Canberra ACT 2600 Australia

Expensive multiobjective optimization problems (EMOPs) refer to those wherein evaluation of each candidate solution incurs a significant cost. To solve such problems within a limited number of solution evaluations, surrogate-assisted evolutionary algorithms (SAEAs) are often used. However, existing SAEAs typically operate in a generational framework wherein multiple solutions are identified for evaluation in each generation. There exist relatively few proposals in steady-state framework, wherein only a single solution is evaluated in each iteration. The development of such algorithms is crucial to efficiently solve EMOPs for which the evaluation of candidate designs cannot be parallelized. Furthermore, regardless of the framework used, the performance of current SAEAs tends to degrade when the Pareto front (PF) of the problem has irregularities, such as extremely concave/convex segments, even for 2/3-objective problems. To contextualize the motivation of this study, the performance of a few state-of-the-art SAEAs is first demonstrated on some such selected problems. Then, to address the above research gaps, we propose a surrogate-assisted steady-state EA (SASSEA), which incorporates a number of novel elements, including: 1) effective use of model uncertainty information to aid the search, including the use of the probabilistic dominance and Mahalanobis distance;2) two-step infill identification using nondominance (ND) and distance-based selection;and 3) a shadow ND mechanism to avoid repeated selection and evaluation of dominated solutions. The efficacy of the proposed approach is demonstrated through extensive benchmarking on a range of test problems. It shows competitive performance relative to many state-of-the-art SAEAs, including both steady-state and generational approaches.

关键词： Infill strategy multiobjective algorithm steady-state algorithm surrogate-assisted optimization.

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Expensive multiobjective Optimization Based on Information Transfer Surrogate

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2023年第3期53卷 1684-1696页

作者： Luo, Jianping Dong, YongFei Zhu, Zexuan Cao, Wenming Li, Xia Shenzhen Univ Coll Elect & Informat Engn Guangdong Key Lab Intelligent Informat Proc Shenzhen 518060 Peoples R China Shenzhen Univ Coll Comp & Software Engn Shenzhen 518060 Peoples R China Shenzhen Univ Coll Elect & Informat Engn Shenzhen 518060 Peoples R China

Objective value estimation based on computationally efficient surrogate models is widely used to reduce the computational cost in solving expensive multiobjective optimization problems (MOPs). However, due to the scarcity of training data and the lack of data sharing between training tasks in a surrogate-based system, the estimation effectiveness of the surrogate models might not be satisfactory. In this study, we present a novel surrogate methodology based on information transfer to deal with this problem. Particularly, in the proposed framework, the objectives of an MOP that may have little apparent similarity or correlation are linearly mapped to a number of related tasks. Afterward, the related tasks are used to train a multitask Gaussian process (MTGP). MTGP expands the training data leading to more confident learning of the parameters of the model. The predicted values of the objective functions can be obtained by a reverse mapping from the learned MTGP model. In this way, the computational burden of the expensive objective functions of an MOP can be substantially reduced while maintaining good estimation accuracy. MTGP facilitates mutual information transfer across tasks, avoids learning from scratch for new tasks, and captures the underlying structural information between tasks. The proposed surrogate approach is merged into MOEA/D to address MOPs. Experimental tests under various scenarios indicate that the resultant algorithm outperforms other state-of-the-art surrogate-based multiobjective optimization algorithms.

关键词： Optimization Task analysis Linear programming Computational modeling Correlation Estimation Data models Expensive optimization Gaussian process (GP) information transfer multiobjective algorithm optimization methods surrogate

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A multiobjective Design Architecture for Versatile Stretchable Touchscreen Displays

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IEEE SENSORS JOURNAL 2023年第18期23卷 21956-21965页

作者： Wu, Ziyi Gu, Yifan Chen, Junwei Qiu, Zhiguang Cheng, Yunfan Lu, Hao Zhu, Yuyang Wang, Yida Lin, Yu-Sheng Gui, Xuchun Deng, Shaozhi Qin, Zong Yang, Bo-Ru Sun Yat Sen Univ State Key Lab Optoelect Mat & Technol Guangdong Prov Key Lab Display Mat & Technol Sch Elect & Informat Technol Guangzhou 510006 Peoples R China

Stretchable touch sensors, which can be conformally attached to arbitrary surfaces, endow any uneven surfaces with intelligence for interacting with humans by sensing the input signal, which is critical for future Internet of Things environments. Theoretical derivation and numerical simulations for structural design have been reported to help understand and analyze stretchable sensors. However, the device prototype simultaneously fulfilling the stretchability and touch sensing was not yet investigated. Here, we demonstrated the prototype, a 5 x 5 copper-based stretchable touch sensor with serpentine shapes. Furthermore, we proposed a hybrid design architecture, combining geometrical modeling, multiphysics field analysis, and multiobjective evolutionary algorithm (MOEA) to construct a stretchable touch sensor with optimal electrical and mechanical performance. This design demonstrated the touch sensitivity improvement by a factor of 72.6% without compromising the stretchability. The proposed touch sensor still functions well under 30% of strain when stretched. This research demonstrates the potential of MOEA as a powerful design tool for versatile stretchable sensors.

关键词： Microtopography design multiobjective algorithm stretchable electronics touch sensitivity touch sensors

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Grid-based multi-objective cheetah optimization for engineering applications

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第4期28卷 1-27页

作者： Sharma, Shubhkirti Kumar, Vijay Natl Inst Technol Dept Comp Sci & Engn Hamirpur 177001 HP India Dr BR Ambedkar Natl Inst Technol Dept Informat Technol Jalandhar 144011 Punjab India

This paper introduces a novel Grid-based Multi-Objective Cheetah Optimizer (MCO) algorithm for engineering applications. The MCO algorithm is derived from the hunting strategy of cheetahs and builds upon its single-objective predecessor, the Cheetah Optimizer (CO). The MCO employs a combination of non-dominated sorting, a grid mechanism, and an archive to maintain and distribute solutions effectively. Firstly, non-dominance selection is applied to identify the best set of solutions. Secondly, an archive is maintained to preserve these optimal solutions. Thirdly, a grid-based method ensures a better distribution and selection of the solution set. The viability of the proposed MCO algorithm is verified through simulation studies on twenty two benchmark test functions and five engineering problems, evaluated against five performance metrics. Comparative analysis with five well-established multi-objective algorithms demonstrates that the MCO algorithm surpasses these in terms of achieving closer approximations to the Pareto front. The results confirm that the MCO algorithm can provide a diverse and effective set of optimal solutions, making it a superior choice for complex engineering problems requiring multi-objective optimization.

关键词： Multi-objective cheetah optimizer multiobjective algorithm Multi-objective optimization Meta-heuristics Optimization

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A new MOPSO algorithm for solving mathematical test functions and control engineering problems

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2023年第15期45卷 2968-2980页

作者： Mahmoodabadi, M. J. Rasekh, M. Sirjan Univ Technol Dept Mech Engn Sirjan Iran Shahid Rajaee Teacher Training Univ Sch Mech Engn Tehran Iran Sirjan Univ Technol Dept Mech Engn Sirjan 78185439 Iran

In this article, a new multiobjective particle swarm optimization (MOPSO) algorithm is introduced to improve the performance of a sliding mode based robust fuzzy proportional-integral-derivative (PID) controller. In this regard, the non-dominated solution having minimum number of neighbors is considered as the global best position, while the sigma values of the members are employed to determine the personal best position. A modified multiple-crossover operator is combined with the operators of the particle swarm optimization to significantly increase the convergence speed of the algorithm. To limit the size of the archive, a dynamical elimination scheme defined in the Euclidean space is introduced. Besides, iteration-based linear relations are implemented to adaptively compute the inertia weight and learning coefficients. To evaluate the effectiveness of the introduced MOPSO algorithm, the requirements are conducted by means of three benchmark functions with regard to generational distance, spacing, and maximum spread metrics. This analysis demonstrates that the proposed algorithm operates better through comparison with well-known elitist multiobjective evolutionary algorithms. Moreover, the MOPSO algorithm is applied for optimal design of a hybrid robust fuzzy PID controller for a pneumatic system with two bellows. Conflicting objective functions are considered as the normalized values of overshoot and settling time of the displacement between the bellows that should be simultaneously minimized. The feasibility and efficiency of the strategy are assessed in comparison with the conventional controllers.

关键词： multiobjective algorithm particle swarm optimization PID controller fuzzy system robust control pneumatic system

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Multi-Objective Optimization Framework for Optimal Power Flow Problem of Hybrid Power Systems Considering Security Constraints

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IEEE ACCESS 2022年 10卷 103509-103528页

作者： Pandya, Sundaram B. Ravichandran, Sowmya Manoharan, Premkumar Jangir, Pradeep Alhelou, Hassan Haes Shri KJ Polytech Dept Elect Engn Bharuch 392001 Gujarat India Natl Inst Technol Dept Elect & Elect Engn Tiruchirappalli 620015 Tamil Nadu India Dayananda Sagar Coll Engn Dept Elect & Elect Engn Bengaluru 560078 Karnataka India Rajasthan Rajya Vidyut Prasaran Nigam Ltd Sikar 332025 Rajasthan India Tishreen Univ Dept Elect Power Engn Latakia 2230 Syria

The hybrid model of the power system infrastructure is an essential part of the sophisticated technology of the electrical network. Generally, for the Optimal Power Flow (OPF) problem, the power system with only thermal generators is considered. In traditional OPF problems, the fuel cost required to produce electrical energy is considered, and emissions are frequently neglected. Renewable Energy Sources (RESs) have received increasing attention due to various potential characteristics such as clean, diversity, and renewability. As a result, RESs are being integrated into the existing electrical grid at an increasing rate. The study in this paper proposes a techno-economic investigation into the single- and multi-objective OPF, coordinating with RESs, such as wind, PhotoVoltaic (PV), and small hydropower units with hybrid PV. Moreover, the probability density functions of Weibull, Lognormal, and Gumble have been used to predict the required power. A recently reported equilibrium optimizer and its multi-objective version are considered for handling OPF problems. The superior performance of the equilibrium optimizer is further verified with the results of both single- and multi-objective through comparative analysis with state-of-the-art counterparts, and the indications are that the suggested algorithm can find better optimal solutions in a smaller number of generations (iterations) with faster convergence and well distributed optimal Pareto front for multi-objective problems. The results are verified by employing an IEEE-30 bus hybrid power network, and performance comparisons are made among well-established algorithms. Simulation findings show that the suggested algorithm can achieve a reasonable compromise solution for different objectives.

关键词： Load flow Renewable energy sources Optimization Wind power generation Hybrid power systems Optimal control Security Equilibrium optimizer multiobjective algorithm optimal power flow renewable energy sources security constraints

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HyMOTree: Automatic Hyperparameters Tuning for Non-Technical Loss Detection Based on Multi-Objective and Tree-Based algorithms

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ENERGIES 2023年第13期16卷 4971页

作者： Coelho, Francisco Jonatas Siqueira Feitosa, Allan Rivalles Souza Alcantara, Andre Luis Michels Li, Kaifeng Lima, Ronaldo Ferreira Silva, Victor Rios da Silva-Filho, Abel Guilhermino Univ Fed Pernambuco Informat Ctr CIn BR-50670901 Recife PE Brazil Eldorado Res Inst BR-13083898 Campinas SP Brazil Paulista Power & Light Co BR-13070740 Campinas SP Brazil

The most common methods to detect non-technical losses involve Deep Learning-based classifiers and samples of consumption remotely collected several times a day through Smart Meters (SMs) and Advanced Metering Infrastructure (AMI). This approach requires a huge amount of data, and training is computationally expensive. However, most energy meters in emerging countries such as Brazil are technologically limited. These devices can measure only the accumulated energy consumption monthly. This work focuses on detecting energy theft in scenarios without AMI and SM. We propose a strategy called HyMOTree intended for the hyperparameter tuning of tree-based algorithms using different multiobjective optimization strategies. Our main contributions are associating different multiobjective optimization strategies to improve the classifier performance and analyzing the model's performance given different probability cutoff operations. HyMOTree combines NSGA-II and GDE-3 with Decision Tree, Random Forest, and XGboost. A dataset provided by a Brazilian power distribution company CPFL ENERGIA (TM) was used, and the SMOTE technique was applied to balance the data. The results show that HyMOTree performed better than the random search method, and then, the combination between Random Forest and NSGA-II achieved 0.95 and 0.93 for Precision and F1-Score, respectively. Field studies showed that inspections guided by HyMOTree achieved an accuracy of 76%.

关键词： energy theft non-technical losses hyperparameter tuning machine learning multiobjective algorithm

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