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Journal articles on the topic 'Indicator-based optimization'

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Published: 7 June 2025
Last updated: 26 June 2025

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1

Brockhoff, Dimo, Tobias Wagner, and Heike Trautmann. "2 Indicator-Based Multiobjective Search." Evolutionary Computation 23, no. 3 (2015): 369–95. http://dx.doi.org/10.1162/evco_a_00135.

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In multiobjective optimization, set-based performance indicators are commonly used to assess the quality of a Pareto front approximation. Based on the scalarization obtained by these indicators, a performance comparison of multiobjective optimization algorithms becomes possible. The [Formula: see text] and the hypervolume (HV) indicator represent two recommended approaches which have shown a correlated behavior in recent empirical studies. Whereas the HV indicator has been comprehensively analyzed in the last years, almost no studies on the [Formula: see text] indicator exist. In this extended version of our previous conference paper, we thus perform a comprehensive investigation of the properties of the [Formula: see text] indicator in a theoretical and empirical way. The influence of the number and distribution of the weight vectors on the optimal distribution of [Formula: see text] solutions is analyzed. Based on a comparative analysis, specific characteristics and differences of the [Formula: see text] and HV indicator are presented. Furthermore, the [Formula: see text] indicator is integrated into an indicator-based steady-state evolutionary multiobjective optimization algorithm (EMOA). It is shown that the so-called [Formula: see text]-EMOA can accurately approximate the optimal distribution of [Formula: see text] solutions regarding [Formula: see text].
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2

Li, Fei, Jianchang Liu, Peiqiu Huang, and Huaitao Shi. "An R2 Indicator and Decomposition Based Steady-State Evolutionary Algorithm for Many-Objective Optimization." Mathematical Problems in Engineering 2018 (2018): 1–18. http://dx.doi.org/10.1155/2018/1435463.

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An R2 indicator based selection method is a major ingredient in the formulation of indicator based evolutionary multiobjective optimization algorithms. The existing classical indicator based selection methodologies have demonstrated an excellent performance to solve low-dimensional optimization problems. However, the R2 indicator based evolutionary multiobjective optimization algorithms encounter enormous challenges in high-dimensional objective space. Our main purpose is to explore how to extend the R2 indicator to handle many-objective optimization problems. After analyzing the R2 indicator, the objective space partition strategy, and the decomposition method, we propose a steady-state evolutionary algorithm based on the R2 indicator and the decomposition method, named, R2-MOEA/D, to obtain well-converged and well-distributed Pareto front. The main contribution of this paper contains two aspects. (1) The convergence and diversity for the R2 indicator based selection are analyzed. Two improper selection situations will be properly solved via applying the decomposition method. (2) According to the position of a new individual in the steady-state evolutionary algorithm, two different objective space partition strategies and the corresponding selection methods are proposed. Extensive experiments are conducted on a variety of benchmark test problems, and the experimental results demonstrate that the proposed algorithm has competitive performance in comparison with several tailored algorithms for many-objective optimization.
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3

Hui, Jiyuan, Zhiyuan Dai, and Jinjin Chen. "Mining Area Production Safety Optimization Based on Multi-objective Particle Swarm Optimization Model." Academic Journal of Science and Technology 13, no. 2 (2024): 308–12. https://doi.org/10.54097/93cmbe94.

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Safe production in metal mines is an important task to ensure the safety of workers and the integrity of production equipment. From the perspective of optimization, this paper establishes an early warning model and a multi-objective particle swarm optimization algorithm model by analyzing the relevant production data affecting the indicator system of mine safety production, and then solves the model through the multi-objective particle swarm optimization algorithm to give an optimization scheme for maximizing the safety production of the relevant mines. For the safety production problems in metal mines, four indicator systems are proposed, namely, production ecological environment safety, production personnel safety standards, production equipment safety and production information security, and then the relevant production data of the four indicator systems are analyzed and the early warning model is established. Based on the relevant production data of the four indicator systems, the mathematical relationship between the production data affecting each indicator system is constructed. Then, a multi-objective particle swarm optimization algorithm is established to construct the relationship between the maximum safe production of the mine and each indicator system, and the index of the maximum safe production production data of the mine is obtained. The feasibility of the mine safety production optimization scheme is given.
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4

Brockhoff, Dimo, Johannes Bader, Lothar Thiele, and Eckart Zitzler. "Directed Multiobjective Optimization Based on the Weighted Hypervolume Indicator." Journal of Multi-Criteria Decision Analysis 20, no. 5-6 (2013): 291–317. http://dx.doi.org/10.1002/mcda.1502.

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5

Sun, Yanan, Gary G. Yen, and Zhang Yi. "IGD Indicator-Based Evolutionary Algorithm for Many-Objective Optimization Problems." IEEE Transactions on Evolutionary Computation 23, no. 2 (2019): 173–87. http://dx.doi.org/10.1109/tevc.2018.2791283.

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6

Mansour, Imen Ben, and Ines Alaya. "Indicator Based Ant Colony Optimization for Multi-objective Knapsack Problem." Procedia Computer Science 60 (2015): 448–57. http://dx.doi.org/10.1016/j.procs.2015.08.165.

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7

Zapotecas-Martínez, Saúl, Abel García-Nájera, and Adriana Menchaca-Méndez. "Improved Lebesgue Indicator-Based Evolutionary Algorithm: Reducing Hypervolume Computations." Mathematics 10, no. 1 (2021): 19. http://dx.doi.org/10.3390/math10010019.

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One of the major limitations of evolutionary algorithms based on the Lebesgue measure for multi-objective optimization is the computational cost required to approximate the Pareto front of a problem. Nonetheless, the Pareto compliance property of the Lebesgue measure makes it one of the most investigated indicators in the design of indicator-based evolutionary algorithms (IBEAs). The main deficiency of IBEAs that use the Lebesgue measure is their computational cost which increases with the number of objectives of the problem. On this matter, the investigation presented in this paper introduces an evolutionary algorithm based on the Lebesgue measure to deal with box-constrained continuous multi-objective optimization problems. The proposed algorithm implicitly uses the regularity property of continuous multi-objective optimization problems that has suggested effectiveness when solving continuous problems with rough Pareto sets. On the other hand, the survival selection mechanism considers the local property of the Lebesgue measure, thus reducing the computational time in our algorithmic approach. The emerging indicator-based evolutionary algorithm is examined and compared versus three state-of-the-art multi-objective evolutionary algorithms based on the Lebesgue measure. In addition, we validate its performance on a set of artificial test problems with various characteristics, including multimodality, separability, and various Pareto front forms, incorporating concavity, convexity, and discontinuity. For a more exhaustive study, the proposed algorithm is evaluated in three real-world applications having four, five, and seven objective functions whose properties are unknown. We show the high competitiveness of our proposed approach, which, in many cases, improved the state-of-the-art indicator-based evolutionary algorithms on the multi-objective problems adopted in our investigation.
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8

Zheng, Maosheng, Haipeng Teng, and Yi Wang. "Application of new robust design by means of probability-based multi-objective optimization to machining process parameters." Vojnotehnicki glasnik 71, no. 1 (2023): 84–99. http://dx.doi.org/10.5937/vojtehg71-39747.

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Introduction/purpose: New robust design by means of probability-based multi-objective optimization takes the arithmetic mean value of the performance indicator and its deviation as twin independent responses of the performance indicator. The aim of this article is to check the applicability of new robust design in optimizing machining process parameters. To conduct the examination in detail, the robust design for optimal cutting parameters to minimize energy consumption during the turning of AISI 1018 steel at a constant material removal rate is applied as well as the concurrent optimization of the machining process parameters and the tolerance allocation of a spheroidal graphite cast iron piston. Methods: In the spirit of the probability-based method for multi-objective optimization, the arithmetic mean value of the performance indicator and its deviation are taken as two independent responses of the performance indicator to implement robust design. Each of the above twin responses contributes one part of the partial preferable probabilities to the performance indicator of the alternatives in the treatment. The arithmetic mean value of the performance indicator should be assessed as a representative of the performance indicator according to the function or the preference of the performance indicator, and the deviation is the other index of the performance indicator, which has the characteristic of the 99 smaller-the-better in general. Furthermore, the square root of the product of the above two parts of the partial preferable probability forms the actual preferable probability of the performance indicator. Moreover, the product of partial preferable probabilities gives the total preferable probability of each alternative, which is the overall and unique index of each alternative in the robust optimum. Results: The paper gives the rational optimum cutting parameters for minimizing energy consumption during the turning of AISI 1018 steel at a constant material removal rate and the concurrent optimization of the machining process parameters and the tolerance allocation of a spheroidal graphite cast iron piston. Conclusion: The application study indicates its rationality and convenience of new robust optimization in the optimization of machining process parameters.
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9

Trautmann, Heike, Tobias Wagner, Dirk Biermann, and Claus Weihs. "Indicator-based Selection in Evolutionary Multiobjective Optimization Algorithms Based On the Desirability Index." Journal of Multi-Criteria Decision Analysis 20, no. 5-6 (2013): 319–37. http://dx.doi.org/10.1002/mcda.1503.

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10

Wang, Hai Feng, Hong E. Ren, Kun Zhang, and Hong Xu Wang. "Mining Methods Based on Vague Optimization Evaluation." Advanced Materials Research 659 (January 2013): 128–33. http://dx.doi.org/10.4028/www.scientific.net/amr.659.128.

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Method based on vague optimization evaluation is vague pattern recognition. There are six detailed steps of application. The first, Set up Techno-economic indicator system. Secondly set up preparative optimization scheme sets. Thirdly set up optimal scheme in theory. It is made up of each Techno-economic indicator optimal data. Fourthly transform techno-economic input data into vague data. The fifth, Calculating similarly measures. Similarity measures will be evaluated between preparative optimization scheme vague sets and optimal scheme in theory. The last is vague optimization evaluation. The weight of each preparative optimization scheme is given. The data of weighted similarity measures by the weight factors are obtained. And applying them we obtain the good and bad sort of vague optimization scheme. The new similarity measures formula between vague sets is given. The formula is indispensable in the method of vague optimization evaluation. Application examples show that the Vague optimization evaluation method to the conclusion is reliable.
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11

Hong, Wenjing, Ke Tang, Aimin Zhou, Hisao Ishibuchi, and Xin Yao. "A Scalable Indicator-Based Evolutionary Algorithm for Large-Scale Multiobjective Optimization." IEEE Transactions on Evolutionary Computation 23, no. 3 (2019): 525–37. http://dx.doi.org/10.1109/tevc.2018.2881153.

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12

Qian, JiDong, GuoHui Zhou, Wei He, YanLing Cui, and HanLin Deng. "Optimization of teacher evaluation indicator system based on fuzzy-DEMATEL-BP." Heliyon 10, no. 13 (2024): e34034. http://dx.doi.org/10.1016/j.heliyon.2024.e34034.

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13

Zapotecas-Martínez, Saúl, Antonio López-Jaimes, and Abel García-Nájera. "LIBEA: A Lebesgue Indicator-Based Evolutionary Algorithm for multi-objective optimization." Swarm and Evolutionary Computation 44 (February 2019): 404–19. http://dx.doi.org/10.1016/j.swevo.2018.05.004.

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14

Sun, Xiaoyan, Yang Chen, Yiping Liu, and Dunwei Gong. "Indicator-based set evolution particle swarm optimization for many-objective problems." Soft Computing 20, no. 6 (2015): 2219–32. http://dx.doi.org/10.1007/s00500-015-1637-1.

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15

Wei, Li-Xin, Xin Li, Rui Fan, Hao Sun, and Zi-Yu Hu. "A Hybrid Multiobjective Particle Swarm Optimization Algorithm Based on R2 Indicator." IEEE Access 6 (2018): 14710–21. http://dx.doi.org/10.1109/access.2018.2812701.

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16

Cai, Xinye, Haoran Sun, and Zhun Fan. "A diversity indicator based on reference vectors for many-objective optimization." Information Sciences 430-431 (March 2018): 467–86. http://dx.doi.org/10.1016/j.ins.2017.11.051.

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17

Li, Fei, Yujie Yang, Zhengkun Shang, Siyuan Li, and Haibin Ouyang. "Kriging-assisted indicator-based evolutionary algorithm for expensive multi-objective optimization." Applied Soft Computing 147 (November 2023): 110736. http://dx.doi.org/10.1016/j.asoc.2023.110736.

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18

Xia, Yizhang, Jianzun Huang, Xijun Li, Yuan Liu, Jinhua Zheng, and Juan Zou. "A Many-Objective Evolutionary Algorithm Based on Indicator and Decomposition." Mathematics 11, no. 2 (2023): 413. http://dx.doi.org/10.3390/math11020413.

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In the field of many-objective evolutionary optimization algorithms (MaOEAs), how to maintain the balance between convergence and diversity has been a significant research problem. With the increase of the number of objectives, the number of mutually nondominated solutions increases rapidly, and multi-objective evolutionary optimization algorithms, based on Pareto-dominated relations, become invalid because of the loss of selection pressure in environmental selection. In order to solve this problem, indicator-based many-objective evolutionary algorithms have been proposed; however, they are not good enough at maintaining diversity. Decomposition-based methods have achieved promising performance in keeping diversity. In this paper, we propose a MaOEA based on indicator and decomposition (IDEA) to keep the convergence and diversity simultaneously. Moreover, decomposition-based algorithms do not work well on irregular PFs. To tackle this problem, this paper develops a reference-points adjustment method based on the learning population. Experimental studies of several well-known benchmark problems show that IDEA is very effective compared to ten state-of-the-art many-objective algorithms.
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19

Zeng, Yu, and Xing Xu. "The Construction and Optimization of an AI Education Evaluation Indicator Based on Intelligent Algorithms." International Journal of Cognitive Informatics and Natural Intelligence 16, no. 1 (2022): 1–22. http://dx.doi.org/10.4018/ijcini.315275.

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The basic tool in the analytic hierarchy process (AHP) is the complete judgment matrix. To address the weakness of the AHP in determining weight in the comprehensive evaluation system, the particle swarm optimization (PSO)-AHP model proposed in this paper is based on the PSO in the meta-heuristic algorithm. The model was used to solve the indicator weights in the evaluation system of AI education in primary and secondary schools in Fujian Province and was compared with the genetic algorithm and war strategy optimization algorithm. From the comparison results, the PSO-AHP optimization is more effective among the three algorithms, and the indicator consistency can be improved by about 30%. They are both effective in solving the problem that once the judgment matrix is given in the AHP, the weights and indicator consistency cannot be improved. Finally, the results were tested by Friedman statistics to prove the viability of the proposed algorithm.
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20

Yan, Xi, Qi Li, and Ran Yan. "Lithium battery SOH prediction based on multi-indicator optimal weight fusion." Journal of Physics: Conference Series 2835, no. 1 (2024): 012029. http://dx.doi.org/10.1088/1742-6596/2835/1/012029.

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Abstract To improve the prediction accuracy of battery health state, this study proposes a prediction method combining health indicator polynomial (HIP) and Improved Grasshopper Optimization Algorithm (IGOA). Based on Support Vector Regression (SVR), three representative health indicators (HI) were selected, and a polynomial model integrating multiple HI was constructed by using weighted fusion. Considering that the value of the weight coefficient of the feature, the penalty coefficient of SVR and the kernel parameter have a great influence on the prediction accuracy, the improved grasshopper optimization algorithm is used to jointly optimize the weight coefficient and related hyperparameters in the model. The simulation results on the University of Maryland battery data set show that the proposed prediction method has higher prediction accuracy, and the prediction error is basically maintained within 0.03.
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21

Huang, Jiazhen, Cong Yu, guangpeng Xiao, and jian Xiang. "Research on Smart City Construction Management Evaluation System Based on Ternary Subject Theory." IOP Conference Series: Earth and Environmental Science 1011, no. 1 (2022): 012036. http://dx.doi.org/10.1088/1755-1315/1011/1/012036.

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Abstract In this paper, the existing problems and optimization principles of the current smart city construction management evaluation indicator system in China are analyzed. The evaluation indicator system is selected for two rounds based on the membership and utility and constructed systematically. The distribution of weight of smart city construction management evaluation indicators in China is obtained based on empirical investigation of the government officials, experts, scholars and the public participating in the evaluation from different typical regions in eastern, central, and western China by means of surveys and questionnaire investigations. Finally, the existing problems of the evaluation indicator system are analyzed and the optimization path is proposed.
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22

Wang, Chuang, Guanghui Zhou, Fengtian Chang, and Yu Zhang. "The module configuration optimization of vertical computer numerical control honing machine based on ideal outranking cardinal point." Science Progress 103, no. 3 (2020): 003685042093572. http://dx.doi.org/10.1177/0036850420935729.

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To find the optimal one from all feasible module configuration schemes of high-end computer numerical control machine tools according to performance, cost, and delivery, this article studies the method of module configuration optimization. An ideal outranking cardinal point based on the module configuration optimization approach of vertical computer numerical control honing machine is proposed in this article. First, it establishes the multi-objective decision model of the module configuration optimization according to performance indicator function, cost indicator function, and delivery indicator function. Second, the feasible configuration schemes, which are obtained based on customer requirements, are transformed into the data matrix of decision attributes. Third, ideal outranking cardinal point is used to calculate the multi-objective decision model of the module configuration optimization. According to the comprehensive distance between the feasible configuration schemes and the ideal outranking cardinal point, and the inverse ideal outranking cardinal point, the optimal configuration scheme is selected. The feasibility and universality of the proposed approach is verified by a module configuration optimization case of vertical computer numerical control honing machine.
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23

Han, Ding, and Jianrong Zheng. "A Kriging Model-Based Expensive Multiobjective Optimization Algorithm Using R2 Indicator of Expectation Improvement." Mathematical Problems in Engineering 2020 (June 27, 2020): 1–16. http://dx.doi.org/10.1155/2020/9474580.

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Most of the multiobjective optimization problems in engineering involve the evaluation of expensive objectives and constraint functions, for which an approximate model-based multiobjective optimization algorithm is usually employed, but requires a large amount of function evaluation. Aiming at effectively reducing the computation cost, a novel infilling point criterion EIR2 is proposed, whose basic idea is mapping a point in objective space into a set in expectation improvement space and utilizing the R2 indicator of the set to quantify the fitness of the point being selected as an infilling point. This criterion has an analytic form regardless of the number of objectives and demands lower calculation resources. Combining the Kriging model, optimal Latin hypercube sampling, and particle swarm optimization, an algorithm, EIR2-MOEA, is developed for solving expensive multiobjective optimization problems and applied to three sets of standard test functions of varying difficulty and comparing with two other competitive infill point criteria. Results show that EIR2 has higher resource utilization efficiency, and the resulting nondominated solution set possesses good convergence and diversity. By coupling with the average probability of feasibility, the EIR2 criterion is capable of dealing with expensive constrained multiobjective optimization problems and its efficiency is successfully validated in the optimal design of energy storage flywheel.
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24

Yan, Yongzhao, Zhenqian Sun, Yueqi Hou, et al. "UAV Swarm Mission Planning and Load Sensitivity Analysis Based on Clustering and Optimization Algorithms." Applied Sciences 13, no. 22 (2023): 12438. http://dx.doi.org/10.3390/app132212438.

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Unmanned aerial vehicle (UAV) swarms offer unique advantages for area search and environmental monitoring applications. For practical deployments, determining the optimal number of UAVs required for a given task and defining key performance metrics for the platforms and payloads are crucial challenges. This study aims to address mission planning and performance optimization for cooperative UAV swarm search scenarios. A new clustering algorithm is proposed, integrating enhanced clustering techniques with ant colony optimization, particle swarm optimization, and crow search optimization. This jointly optimizes and validates the UAV numbers and coordinated trajectories. Sensitivity analysis and indicator optimization further examine specific scenarios to quantify platform and sensor factors influencing search efficiency. Lastly, sensitivity analysis and performance indicator optimization are conducted in specific scenarios. The modular algorithmic components and modeling techniques established in this work lay a foundation for continued research into real−world mission−based swarm optimization.
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25

Li, Fei, Ran Cheng, Jianchang Liu, and Yaochu Jin. "A two-stage R2 indicator based evolutionary algorithm for many-objective optimization." Applied Soft Computing 67 (June 2018): 245–60. http://dx.doi.org/10.1016/j.asoc.2018.02.048.

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26

Gratton, S., N. Soualmi, and L. N. Vicente. "An indicator for the switch from derivative-free to derivative-based optimization." Operations Research Letters 45, no. 4 (2017): 353–61. http://dx.doi.org/10.1016/j.orl.2017.05.003.

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27

Dominic, S., Y. A. W. Shardt, and S. X. Ding. "Economic Performance Indicator Based Optimization for the Air Separation Unit Compressor Trains." IFAC-PapersOnLine 48, no. 21 (2015): 858–63. http://dx.doi.org/10.1016/j.ifacol.2015.09.634.

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28

Gao, Qi, Jinzhi Feng, and Songlin Zheng. "Optimization design of the key parameters of McPherson suspension systems using generalized multi-dimension adaptive learning particle swarm optimization." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 233, no. 13 (2019): 3403–23. http://dx.doi.org/10.1177/0954407018824766.

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The performance parameters of suspension systems must be properly matched to ensure the handling and stability performance of a vehicle. Based on real vehicle measured data, a parameterized vehicle dynamic model is built, and the validity of the parameterized vehicle dynamic model is verified by comparing simulation results with real vehicle test results. Seven representative steady-state and transient single evaluation indicators of handling and stability of the vehicle are selected. The key parameters of McPherson suspension system, which significantly affects steady-state and transient handling and stability performance, are selected through a sensitivity analysis. Their contribution rates for each single evaluation indicator are calculated based on 81 simulation tests using the parameterized vehicle dynamic model. A comprehensive evaluation indicator system for the whole vehicle is established. This system contains the seven steady-state and transient single handling and stability evaluation indicators that are obtained using a quadratic response surface fitting for the selected key parameters. The comprehensive evaluation indicator system is used to show whether a vehicle has good steady-state and desirable transient responses. Moreover, a generalized multi-dimension adaptive learning particle swarm optimization is proposed to search for the global optimum of the comprehensive evaluation indicator system across the search space with rapid convergence. Optimization results show that a comprehensive handling and stability performance are improved, and simulation results of the parameterized vehicle dynamic model that is modified in accordance with the optimization results verify the improvement of the steady-state steering driving behavior and transient yaw response of the vehicle. In conclusion, the comprehensive evaluation indicator system is feasible, and the generalized multi-dimension adaptive learning particle swarm optimization is effective for the optimization design of the key parameters of the McPherson suspension system.
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Bader, Johannes, and Eckart Zitzler. "HypE: An Algorithm for Fast Hypervolume-Based Many-Objective Optimization." Evolutionary Computation 19, no. 1 (2011): 45–76. http://dx.doi.org/10.1162/evco_a_00009.

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In the field of evolutionary multi-criterion optimization, the hypervolume indicator is the only single set quality measure that is known to be strictly monotonic with regard to Pareto dominance: whenever a Pareto set approximation entirely dominates another one, then the indicator value of the dominant set will also be better. This property is of high interest and relevance for problems involving a large number of objective functions. However, the high computational effort required for hypervolume calculation has so far prevented the full exploitation of this indicator's potential; current hypervolume-based search algorithms are limited to problems with only a few objectives. This paper addresses this issue and proposes a fast search algorithm that uses Monte Carlo simulation to approximate the exact hypervolume values. The main idea is not that the actual indicator values are important, but rather that the rankings of solutions induced by the hypervolume indicator. In detail, we present HypE, a hypervolume estimation algorithm for multi-objective optimization, by which the accuracy of the estimates and the available computing resources can be traded off; thereby, not only do many-objective problems become feasible with hypervolume-based search, but also the runtime can be flexibly adapted. Moreover, we show how the same principle can be used to statistically compare the outcomes of different multi-objective optimizers with respect to the hypervolume—so far, statistical testing has been restricted to scenarios with few objectives. The experimental results indicate that HypE is highly effective for many-objective problems in comparison to existing multi-objective evolutionary algorithms. HypE is available for download at http://www.tik.ee.ethz.ch/sop/download/supplementary/hype/ .
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30

Qu, Jiahao, Hui Wang, and Ying Zhang. "LSTM multivariate time series-based prediction of grassland multi-indicator data." Journal of Physics: Conference Series 2450, no. 1 (2023): 012040. http://dx.doi.org/10.1088/1742-6596/2450/1/012040.

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Abstract Xilinguole grassland in Inner Mongolia is one of the four major grasslands in China, which not only provides a large amount of livestock industry for the country but also prevents soil erosion and plays a key role in the regulation of ecological climate. To ensure the sustainable development of grasslands, this paper provides prediction data for the grassland grazing system optimization model by using LSTM multivariate time series prediction method to predict soil moisture, chemical properties, and desertification degree of grasslands under different grazing strategies to ensure the sustainable development of grasslands.
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31

Song, Jiajia, Jinbo Zhang, and Xinnan Fan. "A precise ultra high frequency partial discharge location method for switchgear based on received signal strength ranging." International Journal of Distributed Sensor Networks 16, no. 5 (2020): 155014772090363. http://dx.doi.org/10.1177/1550147720903634.

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Partial discharges are the main insulation defects encountered in gas-insulated switchgears. When it occurs inside the gas-insulated switchgear cavity, it degrades insulation, and, sooner or later, causes a breakdown. Therefore, it is important to discover insulation defects as early as possible, locate the discharge, and perform both defect identification and maintenance. Current ultra high frequency-based partial discharge location methods mainly use time delay. To obtain accurate delay times, however, a very high sampling rate is needed, which requires expensive hardware and greatly limits its application. Therefore, in this article, a localization method based on received signal strength indicator ranging is proposed, and location estimation is carried out. An easily implementable particle swarm optimization algorithm with high positioning accuracy is selected to compensate for the low positioning accuracy of current received signal strength indicator ranging methods. To further improve positioning accuracy, the convergence conditions of the particle swarm optimization are investigated, and, considering their constraints, an improved particle swarm optimization algorithm is proposed. By combining the characteristics of ultra high frequency wireless sensor array positioning, the particle size is optimized. The simulation results show that the location accuracy using the ultra high frequency switchgear partial discharge location method based on received signal strength indicator ranging with the improved particle swarm optimization algorithm performs significantly better.
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32

Zhang, Xufang, Zhenguang Wu, and Wei He. "An effective approach for robust design optimization of wind turbine airfoils with random aerodynamic variables." Advances in Mechanical Engineering 11, no. 9 (2019): 168781401987926. http://dx.doi.org/10.1177/1687814019879263.

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The robust design optimization of an airfoil needs to continuously realize the probability-based aerodynamic simulation for various combinations of geometry and wind climate parameters. The simulation time is lengthy when a full aerodynamic model is embedded for the numerical iteration. To this end, a second-order polynomial-based response surface model is first presented to relate the airfoil performance indicator with geometry and random aerodynamic variables. This allows to quickly evaluate the response moments and optimization constraints. Then, the robust design optimization is formulated to simultaneously maximize the mean aerodynamic performance and minimize the variance of design results due to the variation of geometry and aerodynamic parameters. The robust design optimization based on the NACA63418 and the DU93-W-210 airfoils with random Mach and Reynolds numbers is presented to demonstrate potential applications of this proposed model. Results have shown that the mean-value aerodynamic indicator is generally improved, whereas the variance is minimized to archive the robust design objective. The proposed approach is simple and accurate, suggesting an attractive tool for robust design optimization of airfoils with random aerodynamic variables.
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Jia, Songhao, and Cai Yang. "Received Signal Strength Indicator Node Localization Algorithm Based on Constraint Particle Swarm Optimization." TELKOMNIKA (Telecommunication Computing Electronics and Control) 13, no. 1 (2015): 221. http://dx.doi.org/10.12928/telkomnika.v13i1.1263.

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34

Chen, Ye, Xiaoping Yuan, and Xiaohui Cang. "A new gradient stochastic ranking-based multi-indicator algorithm for many-objective optimization." Soft Computing 23, no. 21 (2018): 10911–29. http://dx.doi.org/10.1007/s00500-018-3642-7.

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35

Liu, Yuanchao, Jianchang Liu, Tianjun Li, and Qian Li. "An R2 indicator and weight vector-based evolutionary algorithm for multi-objective optimization." Soft Computing 24, no. 7 (2019): 5079–100. http://dx.doi.org/10.1007/s00500-019-04258-y.

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方, 静静. "Resource Allocation Indicator Design of Multi-Population-Based Constrained Multi-Objective Optimization Algorithm." Operations Research and Fuzziology 13, no. 02 (2023): 1027–34. http://dx.doi.org/10.12677/orf.2023.132106.

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Jiang, Liang, Ya Dong Zhu, Victor Jin, and Li Jun Yu. "Comprehensive Evaluation Method of ORC System Performance Based on the Multi-Objective Optimization." Advanced Materials Research 997 (August 2014): 721–27. http://dx.doi.org/10.4028/www.scientific.net/amr.997.721.

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The choice of evaluating indicator has a most important influence on the performance analysis and optimization of Organic Rankine-cycle (ORC) system. In this paper, the net output power of unit mass of exhaust gas (), the cycle exergy efficiency (), the total waste heat emissions () and the product of exchanger's area and total heat transfer coefficient () are selected as the four key indicators which can reflect the characteristics of ORC system. And then, a comprehensive evaluating indicator of ORC system performance () is obtained according to the theory of multi-objective optimization. Further, a comprehensive evaluation method of ORC system performance based on the multi-objective optimization is put forward in this paper. After that, this method is used to optimize the evaporation temperature which is considered as a key parameter of ORC system performance. According to researches, the results gained by using a single-objective optimization method with only one performance parameter as the indicator, cannot often take a global view of the ORC system performance and sometimes may lead to a deviation. Researches also show that increases firstly and then decreases with the increment of the evaporation temperature, and there exists a value of evaporation temperature to maximize . Through analysis and researches, it can be found that the comprehensive evaluation method of ORC system performance based on the multi-objective optimization provided in this paper gives a synthetic consideration to the requirements from high-efficiency, economy, thermodynamic perfection and environment protection. Moreover, by using this method, the ORC power generation system may be more likely to achieve the best comprehensive performance.
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Ullah, W., M. A. N. Mu'tasim, and M. F. F. Rashid. "Optimization of Cost-Based Hybrid Flowshop Scheduling Using Teaching-Learning-Based Optimization Algorithm." International Journal of Automotive and Mechanical Engineering 21, no. 3 (2024): 11616–28. http://dx.doi.org/10.15282/ijame.21.3.2024.13.0896.

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A cost-based hybrid flowshop scheduling (CHFS) combines flow shop and job shop elements, with cost considerations as a key indicator. CHFS is a complex combinatorial optimization challenge encountered in real-world manufacturing and production environments. This paper investigates the optimization of a CHFS problem using the Teaching Learning-Based Optimization (TLBO) algorithm. Effective CHFS is crucial for achieving production balance, reducing costs, and improving customer satisfaction. The authors formulate the CHFS scheduling problem and propose applying the TLBO algorithm to minimize total costs, including labor, energy, maintenance, and delay expenses. The performance of the TLBO technique is evaluated through computational experiments on various CHFS problem instances. The results demonstrate the effectiveness of the TLBO algorithm, which achieved the best results in 42% of the test cases, surpassing other algorithms like the Grey Wolf Optimizer and Particle Swarm Optimization. Additionally, the TLBO algorithm had the highest average performance ranking across the comparative algorithms. The study highlights the potential of the TLBO algorithm as an efficient optimization tool for complex manufacturing scheduling problems.
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Kedong, Yin, Shiwei Zhou, and Tongtong Xu. "Research on optimization of index system design and its inspection method." Marine Economics and Management 2, no. 1 (2019): 1–28. http://dx.doi.org/10.1108/maem-10-2019-0010.

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Purpose To construct a scientific and reasonable indicator system, it is necessary to design a set of standardized indicator primary selection and optimization inspection process. The purpose of this paper is to provide theoretical guidance and reference standards for the indicator system design process, laying a solid foundation for the application of the indicator system, by systematically exploring the expert evaluation method to optimize the index system to enhance its credibility and reliability, to improve its resolution and accuracy and reduce its objectivity and randomness. Design/methodology/approach The paper is based on system theory and statistics, and it designs the main line of “relevant theoretical analysis – identification of indicators – expert assignment and quality inspection” to achieve the design and optimization of the indicator system. First, the theoretical basis analysis, relevant factor analysis and physical process description are used to clarify the comprehensive evaluation problem and the correlation mechanism. Second, the system structure analysis, hierarchical decomposition and indicator set identification are used to complete the initial establishment of the indicator system. Third, based on expert assignment method, such as Delphi assignments, statistical analysis, t-test and non-parametric test are used to complete the expert assignment quality diagnosis of a single index, the reliability and validity test is used to perform single-index assignment correction and consistency test is used for KENDALL coordination coefficient and F-test multi-indicator expert assignment quality diagnosis. Findings Compared with the traditional index system construction method, the optimization process used in the study standardizes the process of index establishment, reduces subjectivity and randomness, and enhances objectivity and scientificity. Originality/value The innovation point and value of the paper are embodied in three aspects. First, the system design process of the combined indicator system, the multi-dimensional index screening and system optimization are carried out to ensure that the index system is scientific, reasonable and comprehensive. Second, the experts’ background is comprehensively evaluated. The objectivity and reliability of experts’ assignment are analyzed and improved on the basis of traditional methods. Third, aim at the quality of expert assignment, conduct t-test, non-parametric test of single index, and multi-optimal test of coordination and importance of multiple indicators, enhance experts the practicality of assignment and ensures the quality of expert assignment.
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Konopatskiy, E. V., M. V. Lagunova, S. I. Rotkov, O. V. Veretennikova, and A. A. Bezditnyi. "APPLICATION OF PROJECTION ALGORITHMS FOR GEOMETRIC MODELING AND OPTIMIZATION OF SOCIO-ECONOMIC PROCESSES." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 221 (November 2022): 14–23. http://dx.doi.org/10.14489/vkit.2022.11.pp.014-023.

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The article proposes an approach to systematization, modeling and optimization of multidimensional statistical data based on the use of projection algorithms for computer modeling. The proposed approach is presented on the example of computer modeling and optimization of socio-economic data, but it can also be effectively used to systematize and analyze other experimental statistical data. It consists the fact that the original multidimensional data are presented in the form of projections on the Radishchev’s complex drawing in the form of curved lines system. Then, on the indicator curve, the optimal value of the socio-economic indicator is selected (as a rule, this is one of the extrema of the function) and the value of the time at which it was reached is fixed. Here, the indicator curve is understood as the curve corresponding to the response function, and the factor curve is the curves corresponding to the factors influencing the response function. Further, a scientific hypothesis is put forward that the joint interaction of factors recorded at a given moment in time ensures the optimal value of the socio-economic indicator. Thus, we obtain the optimal values of the factors influencing the response function, which in this case is the socio-economic indicator. The interaction between the indicator curve and the factor curves is carried out through the line of interprojection connection. The proposed scientific hypothesis is fully justified, provided that all possible factors affecting the behavior of the socio-economic indicator are taken into account. The implementation of the proposed approach was carried out using the Radishchev’s complex drawing, which displays both the values of the factors and the socio-economic indicator. At the same time, on the Radishchev’s complex drawing, the most favorable conditions for the socio-economic indicator are selected by methods of mathematical analysis. Further, with the help of the line of inter-projection communication, by means of standardization, the desired weight coefficients are determined, corresponding to the most favorable conditions for the socio-economic indicator. This approach is completely independent of the subjective opinion of experts and based solely on the initial statistical information.
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Giovanelli, Joseph, Alexander Tornede, Tanja Tornede, and Marius Lindauer. "Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 11 (2024): 12172–80. http://dx.doi.org/10.1609/aaai.v38i11.29106.

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Hyperparameter optimization (HPO) is important to leverage the full potential of machine learning (ML). In practice, users are often interested in multi-objective (MO) problems, i.e., optimizing potentially conflicting objectives, like accuracy and energy consumption. To tackle this, the vast majority of MO-ML algorithms return a Pareto front of non-dominated machine learning models to the user. Optimizing the hyperparameters of such algorithms is non-trivial as evaluating a hyperparameter configuration entails evaluating the quality of the resulting Pareto front. In literature, there are known indicators that assess the quality of a Pareto front (e.g., hypervolume, R2) by quantifying different properties (e.g., volume, proximity to a reference point). However, choosing the indicator that leads to the desired Pareto front might be a hard task for a user. In this paper, we propose a human-centered interactive HPO approach tailored towards multi-objective ML leveraging preference learning to extract desiderata from users that guide the optimization. Instead of relying on the user guessing the most suitable indicator for their needs, our approach automatically learns an appropriate indicator. Concretely, we leverage pairwise comparisons of distinct Pareto fronts to learn such an appropriate quality indicator. Then, we optimize the hyperparameters of the underlying MO-ML algorithm towards this learned indicator using a state-of-the-art HPO approach. In an experimental study targeting the environmental impact of ML, we demonstrate that our approach leads to substantially better Pareto fronts compared to optimizing based on a wrong indicator pre-selected by the user, and performs comparable in the case of an advanced user knowing which indicator to pick.
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Chen, Zhuo, and Kang Tian. "Optimization of Evaluation Indicators for Driver’s Traffic Literacy: An Improved Principal Component Analysis Method." SAGE Open 12, no. 2 (2022): 215824402211052. http://dx.doi.org/10.1177/21582440221105262.

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The traditional traffic concept seems to be unable to adapt to the traffic problems brought by cities’ rapid development. People must cultivate new modern traffic literacy to deal with traffic problems. Based on traffic literacy, this paper constructs a traffic literacy evaluation indicator system including 13 evaluation indicators such as traffic rules and mechanical knowledge by summarizing relevant literature. We propose an Improved Principal Component Analysis (I-PCA) method, introduce the concept of information contribution sensitivity, and optimize and empower the traffic literacy indicator system. The primary research is to construct a traffic literacy evaluation indicator system including 13 evaluation indicators such as traffic rules and mechanical knowledge. The top 10 indicators that satisfy the cumulative information contribution rate value greater than 90% are retained, and the three indicators with low contribution rate are excluded. The optimization method can retain the indicator with a relatively large information contribution rate so that the indicator’s weight can genuinely reflect the information content of the corresponding indicator. The optimization method can retain the indicator with a relatively large information contribution rate so that the indicator’s weight can genuinely reflect the information content of the corresponding indicator.
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Dui, Hongyan, Kailong Zhang, and Wanyun Xia. "Importance-based Resilience Assessment and Optimization of Unmanned Ship Swarm System." International Journal of Mathematical, Engineering and Management Sciences 9, no. 3 (2024): 616–31. http://dx.doi.org/10.33889/ijmems.2024.9.3.031.

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Based on the unmanned ship swarm system, a resilience model for unmanned ship swarms is proposed by comprehensively considering the preventive indicators, robustness indicators, recoverability indicators, and reconfigurability indicators of the swarm system. Firstly, preventive and robust indicators are proposed based on the characteristics of the unmanned ship swarm system, and the improvement of system performance efficiency by redundant unmanned ships is established as a recoverability indicator. Then, reconfigurable indicators are proposed based on importance, and the resilience indicator of the unmanned ship swarm is determined. Finally, a numerical example is used to model and simulate the performance change and capricious process of the unmanned ship swarm. Most of the research on the resilience assessment model of unmanned ship swarms considered too single indicators. The model of the unmanned ship swarm under attack is constructed, and the superiority of the resilience optimization strategy proposed in this paper is verified.
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Lin, Nan, Hanlin Liu, Genjun Li, et al. "Extraction of mineralized indicator minerals using ensemble learning model optimized by SSA based on hyperspectral image." Open Geosciences 14, no. 1 (2022): 1444–65. http://dx.doi.org/10.1515/geo-2022-0436.

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Abstract Mineralized indicator minerals are an important geological and mineral exploration indicator. Rapid extraction of mineralized indicator minerals from hyperspectral remote sensing images using ensemble learning model has important geological significance for mineral resources exploration. In this study, two mineralized indicator minerals, limonite and chlorite, exposed at the surface of Qinghai Gouli area were used as the research objects. Sparrow search algorithm (SSA) was combined with random forest (RF) and gradient boosting decision tree (GBDT) ensemble learning models, respectively, to construct hyperspectral mineralized indicative mineral information extraction models in the study area. Youden index (YD) and ore deposit coincidence (ODC) were applied to evaluate the performance of different models in the mineral information extraction. The results indicate that the optimization of SSA parameter algorithm is obvious, and the accuracy of both the integrated learning models after parameter search has been improved substantially, among which the SSA-GBDT model has the best performance, and the YD and the ODC can reach 0.661 and 0.727, respectively. Compared with traditional machine learning model, integrated learning model has higher reliability and stronger generalization performance in hyperspectral mineral information extraction and application, with YD greater than 0.6. In addition, the distribution of mineralized indicative minerals extracted by the ensemble learning model after parameter optimization is basically consistent with the distribution pattern of the fracture tectonic spreading characteristics and known deposits (points) in the area, which is in line with the geological characteristics of mineralization in the study area. Therefore, the classification and extraction model of minerals based on hyperspectral remote sensing technology, combined with the SSA optimization algorithm and ensemble learning model, is an efficient mineral exploration method.
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Abdullah, Fahad Bin, Rizwan Iqbal, Sadique Ahmad, Mohammed A. El-Affendi, and Pardeep Kumar. "Optimization of Multidimensional Energy Security: An Index Based Assessment." Energies 15, no. 11 (2022): 3929. http://dx.doi.org/10.3390/en15113929.

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This study introduces Pakistan’s multidimensional energy security index (PMESI) and indices across dimensions from 1991 to 2020 through indicator optimization. Based on criteria, expert participation, and reliability testing, 27 indicators were identified and weighted based on dimension reduction utilizing the Varimax Rotation technique. As a result of robust evaluation framework, there has been a considerable change in Pakistan’s energy security when compared to other studies such as the energy security indicator of Pakistan (ESIP) and the energy security index of Pakistan (ESIOP). According to the findings, energy security decreased by 25% between 1991 and 2012, followed by a modest increase through 2020. During the study period, the “Affordability” dimension improved; however, the other four dimensions, namely “Availability,” “Technology,” “Governance,” and “Environment,” regressed. Few goals under the petroleum policy (1991), petroleum policy (2012), and power policy (2013) were partially met, while conservation programs, such as the renewable policy (2006) and national climate change policy (2012), fell short. Indicators such as price, reserves, governance, corruption, and consumption contributed to PMESI across five dimensions. Thus, PMESI and indices guiding policymakers to focus on improving governance and exploiting local energy resources in order to provide affordable and sufficient energy in the long run.
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Groshev, S. V., and A. P. Karpenko. "Multiple-criteria Decision-making Support Based on the Multi-Indicator Evaluation of the Pareto-Approximation Quality." Mechanical Engineering and Computer Science, no. 11 (December 22, 2017): 64–74. http://dx.doi.org/10.24108/1117.0001339.

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The paper considers a problem, which is defined as multi-indicator finding the "best" algorithm to solve a multiple-criteria optimization (MCO) problem. Of a large number of known algorithms for solving the MCO, we deal with the algorithms based on the preliminary construction of its Pareto front (set) approximation and called P-algorithms.Because of a large number of P-algorithms, a problem of choosing the "best" algorithm for the given MCO problem (and / or this class of these problems) arises, i.e. a meta-optimization problem. We pose the problem of structural meta-optimization of P-algorithms, which suggests a simultaneous P-approximation construction and optimization of this approximation according to one or several P-indicators.The paper presents basic MCO-problem formulation and describes used P-approximation quality indicators. Considers several methods to choose the "best" P-algorithm such as a method based on using one or another method to visualize multi-indicator estimates of P-approximation quality, a method based on the scalar convolution of P-approximation quality indicators, chosen by a decision-maker (DM), and an author's automated method that supposes a preliminary approximation of the function of preference. Provides mathematical description, considers advantages and disadvantages, as well as shows the ways to overcome these shortcomings.The main research result involves a development of the original PREF-I method to solve the MCO problem based on identification of so-called DM’s function of preference. This method may be thought as evolution of the PREF method aimed at solving the initial MCO problem.
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Catullo, Ermanno, Mauro Gallegati, and Antonio Palestrini. "Towards a credit network based early warning indicator for crises." Journal of Economic Dynamics and Control 50 (January 2015): 78–97. http://dx.doi.org/10.1016/j.jedc.2014.08.011.

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Chen, Jiayang, and Xuebin Xie. "Optimization of Rockburst Grade Prediction Model Based on Multidimensional Feature Selection: Integrated Learning and Index System Correlation Analysis." Applied Sciences 15, no. 12 (2025): 6466. https://doi.org/10.3390/app15126466.

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Rockburst is a major disaster in deep underground engineering, and its prediction is crucial for engineering safety. This study proposes an optimization method based on multidimensional feature selection and integrated learning that systematically evaluates the impact of different indicator dimensions by constructing an indicator–indicator system and an indicator–rockburst hierarchy using a combination of seven-, six-, five-, four-, and three-dimensional indicators in conjunction with six machine-learning models, such as XGBoost, LightGBM, and CatBoost. The results show that tree models (e.g., CatBoost, LightGBM, etc.) are naturally resistant to multicollinearity, and PCA preprocessing destroys their nonlinear feature relationships, leading to performance degradation. CatBoost has the best performance and strong overfitting resistance; LightGBM is the second most efficient and suitable for real-time applications. The indicator–indicator system has better overall performance but less stability, and the indicator–rockburst system has slightly lower performance but a more stable downward trend. The six-dimensional system in both types of systems can balance the performance and complexity and is the optimal choice for engineering applications. This study provides theoretical support and practical reference for the selection of rockburst prediction and an evaluation index system.
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Liang, Yifan. "Production Scheduling Optimization of An Aviation Bearing Manufacturing Enterprise Based on Teaching-Learning-based Optimization." Academic Journal of Science and Technology 6, no. 2 (2023): 108–11. http://dx.doi.org/10.54097/ajst.v6i2.9707.

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This article is based on an aviation bearing manufacturing and wind power bearing manufacturing enterprise production scheduling for its key process optimization, in Qingdao, Shandong. The production scheduling is aimed at the shortest delivery time, and the production scheduling assessment indicator is the on-time delivery rate of orders. This article, based on the production process and production mode characteristics of the enterprise, improves the convergence ability of the Teaching-learning-based optimization (TLBO) in the early stage and the detail search ability in the later stage by optimizing the static teaching factor function and algorithm retrieval method of the classic algorithm. By combining Levy flight to further enhance the algorithm's global search ability and reduce the probability of falling into local optima. The theoretical reliability of the algorithm was verified by comparing the calculation result of the MK calculation example with other algorithms. The reliability of the algorithm in actual production was verified through the long-term order execution data and order delivery rate of the enterprise.
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Sun, Hong, Fangquan Yang, Peiwen Zhang, and Qingqing Hu. "Behavioral Indicator-Based Initial Flight Training Competency Assessment Model." Applied Sciences 13, no. 10 (2023): 6346. http://dx.doi.org/10.3390/app13106346.

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Ensuring training safety is paramount to flight schools. In response to the inadequacy of traditional flight training assessment for comprehensive quantitative evaluation of cadet competency, an initial flight training competency assessment standard based on behavioral indicators was developed and optimized using the VENN model. Firstly, the Assessor Score Measurement Form (ASMF) was constructed according to the requirements of the Training Evaluation Worksheet specification, such as typical subjects, observations, and completion criteria. Secondly, based on the basic principles of the experience of the flight expert and the Competency-Based Training and Assessment (CBTA), a matrix of correlations between the observations and each competency-based behavioral indicator was created to construct a competency assessment matrix. In addition, a two-dimensional model for representing competency items characterized by behavioral indicators was established and an optimization model for competency assessment criteria was constructed. Finally, through combining actual flight training data, the proposed method was validated in the flight screening check phase. The results show that the optimized flight training competency assessment scheme can be well quantified and matched to real instructor ratings with an accuracy of 84%. The assessment worksheet, the assessment matrix, and the VENN competency rating model can be adapted to the different teaching requirements of each flight phase, achieving a perfect match between the behavioral indicators and the competency items, which is highly versatile. The proposed model can more accurately reflect the core competencies of flight trainees, enable quantitative assessment of behavioral indicators and competency items, and provide support for subsequent training of trainees.
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