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An Improved multi objective Mayfly Algorithm for Solving Optimal Power Flow problem Considering Different Loading Conditions

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2022年第10期31卷 2250177-2250177页

作者： Ramesh, S. Bhaskar, K. Vijaya Karunanithi, K. Ettappan, M. Chandrasekar, P. Raja, S. P. Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept Elect & Elect Engn Chennai 600062 Tamil Nadu India Chennai Inst Technol Dept Elect & Elect Engn Chennai 600069 Tamil Nadu India Vellore Inst Technol Sch Comp Sci & Engn Vellore 632014 Tamil Nadu India

This paper presents an improved multi-objective mayfly algorithm (IMOMA) to resolve the optimal power flow (OPF) problem in a regulated power system network with different loading conditions. The OPF problem, considered a multi-objective optimization problem, comprises multiple objective functions related to economic, technical, operational and security aspects. The IMOMA algorithm has been developed by implementing the simulated binary crossover (SBX), polynomial mutation and dynamic crowding distance (DCD) operators in the original multi-objective mayfly algorithm (MOMA).The OPF problem is analyzed by considering multiple objective functions in the IEEE30-bus test system, the IEEE118-bus test system and the 62-bus Indian utility system. The hypervolume performance metric is used to compare the performance of the MOMA and IMOMA with respect to different operating scenarios. Further, loading conditions ranging between 150% and 50% of the base load are considered for the evaluation. The effectiveness of the IMOMA over the MOMA is observed from the results of the different loads. The best compromise solution is obtained from a set of pareto optimal solutions by implementing the TOPSIS method.

关键词： Hypervolume mayfly algorithm multi-objective optimization problem optimal power flow TOPSIS

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multi-objective Optimal Power Flow Solutions Using Improved multi-objective Mayfly Algorithm (IMOMA)

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2023年第12期32卷

作者： Bhaskar, K. Vijaya Ramesh, S. Karunanithi, K. Raja, S. P. Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept Elect & Elect Engn Chennai 600062 Tamil Nadu India Vellore Inst Technol Sch Comp Sci & Engn Vellore 632014 Tamil Nadu India

This paper realizes the implementation of Improved multi-objective Mayfly Algorithm (IMOMA) for getting optimal solutions related to optimal power flow problem with smooth and nonsmooth fuel cost coefficients. It is performed by considering Simulated Binary Crossover, polynomial mutation and dynamic crowding distance in the existing multi-objective Mayfly Algorithm. The optimal power flow problem is formulated as a multi-objective optimization problem that consists of different objective functions, viz. fuel cost with/without valve point loading effect, active power losses, voltage deviation and voltage stability. The performance of Improved multi-objective Mayfly Algorithm is interpreted in terms of the present multi-objective Mayfly Algorithm and Nondominated Sorting Genetic Algorithm-II. The algorithms are applied under different operating scenarios of the IEEE 30-bus test system, 62-bus Indian utility system and IEEE 118-bus test system with different combinations of objective functions. The obtained Pareto fronts achieved through the implementation of Improved multi-objective Mayfly Algorithm, multi-objective Mayfly Algorithm and Nondominated Sorting Genetic Algorithm-II are compared with the reference Pareto front attained by using weighted sum method based on the Covariance Matrix-adapted Evolution Strategy method. The performances of these algorithms are individually analyzed and validated by considering the performance metrics such as convergence, divergence, generational distance, inverted generational distance, minimum spacing, spread and spacing. The best compromising solution is achieved by implementing the Technique for Order of Preference by Similarity to Ideal Solution method. The overall result has shown the effectiveness of Improved multi-objective Mayfly Algorithm for solving multi-objective optimal power flow problem.

关键词： Covariance Matrix-adapted Evolution Strategy Improved multi-objective Mayfly Algorithm multi-objective optimization problem TOPSIS valve point loading

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Balanced multi-objective optimization algorithm using improvement based reference points approach

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SWARM AND EVOLUTIONARY COMPUTATION 2021年 60卷

作者： Abdel-Basset, Mohamed Mohamed, Reda Abouhawwash, Mohamed Zagazig Univ Fac Comp & Informat Zagazig 44519 Sharqiyah Egypt Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA Mansoura Univ Fac Sci Dept Math Mansoura 35516 Egypt

In this work, we explore a novel multi-objective optimization algorithm to identify a set of solutions that could be optimal for more than one task. The proposed approach is used to generate a set of solutions that balance the tradeoffbetween convergence and diversity in multi-objective optimization problems. Equilibrium Optimizer (EO) algorithm is a novel developed meta-heuristic algorithm inspired by the physics laws. In this paper, we propose a multi-objective Equilibrium Optimizer Algorithm (MEOA) for tackling multi-objective optimization problems. We suggest an enhancement for exploration and exploitation factors of the EO algorithm to randomize the values of these factors with decreasing the initial value of the exploration factor with the iteration and increasing the exploitation factor to accelerate the convergence toward the best solution. To achieve good convergence and well-distributed solutions, the proposed algorithm is integrated with the Improvement-Based Reference Points Method (IBRPM). The proposed approach is applied to the CEC 2020, CEC 2009, DTLZ, and ZDT test functions. Also, the inverted generational and spread spacing metrics are used to compare the proposed algorithm with the most recent evolutionary algorithms. It's obvious from the results that the proposed algorithm is better in both convergence and diversity.

关键词： multi-objective optimization problem CEC 2020 CEC 2009 Equilibrium optimizer Reference directions

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Real-Time Drilling Parameter optimization Model Based on the Constrained Bayesian Method

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ENERGIES 2022年第21期15卷 8030-8030页

作者： Song, Jinbo Wang, Jianlong Li, Bingqing Gan, Linlin Zhang, Feifei Wang, Xueying Wu, Qiong Yangtze Univ Sch Petr Engn Wuhan 430100 Peoples R China Key Lab Drilling & Prod Engn Oil & Gas Wuhan 430100 Peoples R China CNPC Bohai Drilling Engn Co Ltd Engn & Technol Res Inst Tianjin 300457 Peoples R China CNPC Xibu Drilling Engn Co Ltd Urumqi 830011 Peoples R China PetroChina Tarim Oilfield Branch Korla 841000 Peoples R China

To solve the problems of the low energy efficiency and slow penetration rate of drilling, we took the geological data of adjacent wells, real-time logging data, and downhole engineering parameters as inputs;the mechanical specific energy and unit footage cost as multi-objective optimization functions;and the machine pump equipment limit as the constraint condition. A constrained Bayesian optimization algorithm model was established for the optimization solution, and drilling parameters such as weight-of-bit, revolutions per minute, and flowrate were optimized in real time. Through a comparison with NSGA-II, random search, and other optimization algorithms, and the application results of example wells, we show that the established Bayesian optimization algorithm has a good optimization effect while maintaining timeliness. It is suitable for real-time optimization of drilling parameters, can aid a driller in identifying the drilling rate and potential tapping area, and provides a decision-making basis for avoiding the low-efficiency rock-breaking working area and improving rock-breaking efficiency.

关键词： drilling parameter real-time optimization multi-objective optimization problem constrained Bayesian algorithm

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An improved regularity-based vector evolutionary algorithm for multi-objective optimizations

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ADVANCES IN ENGINEERING SOFTWARE 2023年 176卷

作者： Li, Yiying Yang, Shiyou Zhejiang Univ Coll Elect Engn 38 Zheda Rd Hangzhou 310027 Peoples R China

Inverse models-based evolutionary algorithms can generate as many solutions as required in interest regions at a low budget cost. However, the objective value used for the inverse model in the original regularity-based al-gorithm using Gaussian process-based inverse models, (IM-MOEA), is linearly and separately sampled for each objective according to its maximum and minimum values so far searched. Consequently, some non-optimal even infeasible solutions may be sampled. Also, the number of the predefined reference vectors remains constant throughout the whole evolution. Consequently, it may result in a low computational efficiency for multi-objective optimization ones (MOOs) with nonuniform or disconnected Pareto fronts. In this respect, an improved regularity-based vector evolutionary algorithm for multi-objective optimizations is presented in the paper. The main component of the proposed algorithm is to obtain the training data for inverse models by interpolation in the objective space. The population is divided into subpopulations according to the number of objectives. The non-dominated solutions are utilized for interpolations in each subpopulation. The inverse models are finally used to generate offspring using data by interpolation. An adaptive interpolation range co-efficient is proposed for adaptive adjustments of the interpolation region to balance the exploitation and the exploration searches. An elite strategy of hiring elite individuals in the whole population to the current sub-population is employed to enhance the algorithm convergence. Experimental results on two test suites show the superiority of the proposed algorithm.

关键词： Evolutionary algorithm multi-objective optimization problem Interpolation Inverse models

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An online cooperative control strategy for enhancing microgrid participation into electricity market

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ELECTRIC POWER SYSTEMS RESEARCH 2024年 235卷

作者： Cagnano, A. De Tuglie, E. Introna, A. Montegiglio, P. Passarelli, A. Univ Mediterranea Reggio Calabria Dipartimento Ingn Informaz Infrastrutture & Energi I-89124 Reggio Di Calabria Italy Politecn Bari Dipartimento Ingn Elettr & Informaz I-70125 Bari Italy

This paper develops a cooperative control methodology for the online energy management of grid-connected microgrids. The main aim of this methodology is to actively manage the active power outputs of all dispatchable energy sources available within the microgrid as well as the power exchanged with the utility grid so as to match the total load demand at the minimum operating cost. This implies to solve a constrained multiobjective dynamic optimization problem aimed at minimizing the total microgrid operating costs and ensuring the real-time balance within the microgrid in compliance with its technical-operational constraints. Lyapunov's theorem using sensitivity theory is adopted to solve this optimization. To test the performances of the proposed control methodology, several computer simulations corresponding to different operating scenarios have been conducted on the PrInCE Lab experimental microgrid built at the Polytechnic University of Bari.

关键词： Microgrids Real-time energy management Online economic dispatch multi-objective optimization problem Lyapunov theorem

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A health indicator enabling both first predicting time detection and remaining useful life prediction: Application to rotating machinery

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MEASUREMENT 2024年 235卷

作者： Zhao, Yun-Sheng Li, Pengfei Kang, Yu Zhao, Yun-Bo Univ Sci & Technol China Dept Automat 96 Jinzhai Rd Hefei 230027 Anhui Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence 5089 Wangjiang W Rd Hefei 230026 Anhui Peoples R China Univ Sci & Technol China Inst Adv Technol 5089 Wangjiang W Rd Hefei 230000 Anhui Peoples R China

Remaining Useful Life (RUL) prediction is vital for system functionality. Non -end -to -end approaches is an important type of RUL prediction approaches for their important application in industrial scenarios. In non -endto -end approaches, Health Indicator (HI) construction is a critical aspect. However, existing HI construction approaches ignore First Predicting Time (FPT) detection, leading to increased domain knowledge demand and system health comprehension difficulty. To address this issue, this paper proposes a multi -objectiveoptimization -based HI construction approach enabling both FPT detection and RUL prediction. A novel metric called the monotonicity strength index is proposed to address the limitation of the conventional monotonicity. The constructed HI possesses the ability to indicate FPT by taking the detectability metric as an optimization objective. The optimization problem is solved by the combination of the multi -objective ant lion optimizer and the entropy weight method. The superiority of this HI is demonstrated through experiments on the widely used IMS bearing dataset and a gearbox dataset.

关键词： Health indicator First predicting time Remaining useful life multi-objective optimization problem

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multi-objective optimization design of rear seat for a passenger car based on GARS and NSGA-III

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2024年

作者： Zhou, Xuan Jiang, Hengliang Long, Jiangqi Wenzhou Univ Coll Mech & Elect Engn Chashan St Wenzhou 325035 Peoples R China

A collaborative multi-objective optimization design is conducted for the rear seat of a passenger car. This study introduces a combined optimization strategy that integrates both the multi-objective optimization problem and multi-criteria decision-making approaches. Firstly, a finite element model of the rear seat luggage compartment crash is established, and its accuracy is validated. Secondly, the thickness and material type of the primary stress components of the backrest framework for the rear seat are considered as design variables. The safety test point displacement, material cost, and weight are defined as the optimization objectives, while regulatory standards are taken as constraints to construct a multi-objective optimization problem. Once more, the Pareto frontier solution sets are achieved by constructing the genetic aggregation response surface surrogate model combined with the non-dominated sorting genetic algorithm-III optimization algorithm through experimental design. Finally, the Pareto frontier solution sets are ranked to determine the best compromise solution using the multi-criteria decision-making method, which involves the optimal combination weight and the technique for order preference by similarity to an ideal solution based on the Kullback-Leibler distance. The safety performance, lightweight, and cost-effectiveness of the optimized rear car seat are improved. Specifically, the displacement of the headrest skeleton and backrest skeleton is reduced by 5.96% and 4.47% respectively, the material cost is decreased by 7.1%, and the weight is reduced by 5.54%.

关键词： Rear seat multi-objective optimization problem multi-criteria decision-making optimized design

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An adaptive sequencing method of the learning objects or the e-learning environment

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ELECTRONICS AND COMMUNICATIONS IN JAPAN PART III-FUNDAMENTAL ELECTRONIC SCIENCE 2005年第3期88卷 54-71页

作者： Seki, K Matsui, T Okamoto, T Univ Electrocommun Grad Sch Informat Syst Chofu Tokyo 1828585 Japan

In this study, an e-learning system is developed to handle the c-learning environment based on the learning ecological model. In the learning ecological model, which represents the comprehensive e-learning environment, not only the contents of learning, but also the learning environment are managed and provided, based on the content, the goal, and the configuration of the learning. The major purpose of this study is to realize the function that can manage the diversified learning objects with various information granularities and representation formats, using the learning object metadata, so that each learner can utilize the learning object based on the learning scenario, which is matched to the individual learner. The learning scenario is constructed by sequencing the learning objects based on the learning necessity, the learning history information, and the curriculum information of the object of learning, according to the characteristics of the learning object. As the sequencing procedure, the sequencing of the learning objects is considered, by applying the optimization technique of the multi-objective optimization problem, so that multiple evaluation viewpoints are simultaneously satisfied. The genetic algorithm is used as the optimization procedure. The learning object metadata and the sequencing of the learning objects are discussed in detail in this paper. The evaluation of the developed e-learning system is also described. (C) 2004 Wiley Periodicals, Inc.

关键词： remote learning sequencing of learning objects LOM multi-objective optimization problem genetic algorithm

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MOMRFO: multi-objective Manta ray foraging optimizer for handling engineering design problems

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KNOWLEDGE-BASED SYSTEMS 2022年 237卷 107880-107880页

作者： Got, Adel Zouache, Djaafar Moussaoui, Abdelouahab Univ Mohamed Bachir Ibrahimi Dept Comp Sci Bordj Bou Arreridj Algeria Univ Setif 1 Dept Comp Sci Setif Algeria

Despite its recent appearance, manta ray foraging optimizer (MRFO) has shown a good ability to deal with single-objective real-world problems, which makes its application in solving problems with multiple objectives an interesting direction. Accordingly, the current paper investigates the MRFO optimizer to develop a new algorithm for handling multi-objective engineering design problems. To achieve this goal, the elitist concept is adopted to save the set of Pareto solutions through the integration of an external archive into the standard MRFO. This archive is considered also as a repository from which a search agent is chosen based on its density degree to control the convergence and diversity of manta rays population. Our algorithm's efficiency is first validated via extensive experiments on ten test functions, and the results were very satisfactory in terms of convergence and diversity, in almost all cases. Then, it is applied to four multi-objective engineering problems, and it showed a good promise in solving real-world problems with multiple objectives. (C) 2021 Elsevier B.V. All rights reserved.

关键词： multi-objective optimization problem Manta ray foraging optimizer Adaptive archive Convergence and diversity Engineering design problems

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