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Journal articles on the topic 'Multi-Objective Estimation'

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Author: Grafiati

Published: 10 March 2023

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1

R, Rakesh. "Nonlinear Analysis for Parameter Estimation by Multi Objective Single Variable Inverse Analysis." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 529–38. http://dx.doi.org/10.5373/jardcs/v12sp7/20202136.

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Mitra, Amitava, and Jayprakash G. Patankar. "A multi-objective model for warranty estimation." European Journal of Operational Research 45, no. 2-3 (April 1990): 347–55. http://dx.doi.org/10.1016/0377-2217(90)90198-k.

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3

Alberdi, Iciar, Roberto Vallejo, Juan G. Álvarez-González, Sonia Condés, Eduardo González-Ferreiro, Silvia Guerrero, Laura Hernández, et al. "The multi-objective Spanish National Forest Inventory." Forest Systems 26, no. 2 (August 3, 2017): e04S. http://dx.doi.org/10.5424/fs/2017262-10577.

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Aim of study: To present the evolution of the current multi-objective Spanish National Forest Inventory (SNFI) through the assessment of different key indicators on challenging areas of the forestry sector.Area of study: Using information from the Second, Third and Fourth SNFI, this work provides case studies in Navarra, La Rioja, Galicia and Balearic Island regions and at national Spanish scale.Material and methods: These case studies present an estimation of reference values for dead wood by forest types, diameter-age modeling for Populus alba and Populus nigra in riparian forest, the invasiveness of alien species and the invasibility of forest types, herbivore preferences and effects on trees and shrub species, the methodology for estimating cork production , and the combination of SNFI4 information and Airborne Laser Scanning datasets with the aim of updating forest-fire behavior assessment information with a high degree of accuracy.Main results: The results show the suitability and feasibility of the proposed methodologies to estimate the indicators using SNFI data with the exception of the estimation of cork production. In this case, additional field variables were suggested in order to obtain robust estimates.Research highlights: By broadening the variables recorded, the SNFI has become an even more important source of forest information for the development of support tools for decision-making and assessment in diverse strategic fields such as those analyzed in this study.

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Silva, Cláudia M., and Evaristo C. Biscaia. "Multi-Objective parameter estimation problems: an improved strategy." Inverse Problems in Science and Engineering 12, no. 3 (June 2004): 297–316. http://dx.doi.org/10.1080/10682760310001598715.

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Barroso, Márcio F. S., Ricardo H. C. Takahashi, and Luis A. Aguirre. "Multi-objective parameter estimation via minimal correlation criterion." Journal of Process Control 17, no. 4 (April 2007): 321–32. http://dx.doi.org/10.1016/j.jprocont.2006.10.005.

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van Brummelen, E. H., S. Zhuk, and G. J. van Zwieten. "Worst-case multi-objective error estimation and adaptivity." Computer Methods in Applied Mechanics and Engineering 313 (January 2017): 723–43. http://dx.doi.org/10.1016/j.cma.2016.10.007.

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Papaioannidis, Christos, and Ioannis Pitas. "3D Object Pose Estimation Using Multi-Objective Quaternion Learning." IEEE Transactions on Circuits and Systems for Video Technology 30, no. 8 (August 2020): 2683–93. http://dx.doi.org/10.1109/tcsvt.2019.2929600.

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Emami Niri, Mohammad, and David E. Lumley. "Estimation of subsurface geomodels by multi-objective stochastic optimization." Journal of Applied Geophysics 129 (June 2016): 187–99. http://dx.doi.org/10.1016/j.jappgeo.2016.03.031.

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9

Martins, Marcella S. R., Myriam R. B. S. Delgado, Ricardo Lüders, Roberto Santana, Richard A. Gonçalves, and Carolina P. de Almeida. "Hybrid multi-objective Bayesian estimation of distribution algorithm: a comparative analysis for the multi-objective knapsack problem." Journal of Heuristics 24, no. 1 (September 2, 2017): 25–47. http://dx.doi.org/10.1007/s10732-017-9356-7.

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Dörgő, Gyula, and János Abonyi. "Group Contribution Method-based Multi-objective Evolutionary Molecular Design." Hungarian Journal of Industry and Chemistry 44, no. 1 (October 1, 2016): 39–50. http://dx.doi.org/10.1515/hjic-2016-0005.

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Abstract The search for compounds exhibiting desired physical and chemical properties is an essential, yet complex problem in the chemical, petrochemical, and pharmaceutical industries. During the formulation of this optimization-based design problem two tasks must be taken into consideration: the automated generation of feasible molecular structures and the estimation of macroscopic properties based on the resultant structures. For this structural characteristic-based property prediction task numerous methods are available. However, the inverse problem, the design of a chemical compound exhibiting a set of desired properties from a given set of fragments is not so well studied. Since in general design problems molecular structures exhibiting several and sometimes conflicting properties should be optimized, we proposed a methodology based on the modification of the multi-objective Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The originally huge chemical search space is conveniently described by the Joback estimation method. The efficiency of the algorithm was enhanced by soft and hard structural constraints, which expedite the search for feasible molecules. These constraints are related to the number of available groups (fragments), the octet rule and the validity of the branches in the molecule. These constraints are also used to introduce a special genetic operator that improves the individuals of the populations to ensure the estimation of the properties is based on only reliable structures. The applicability of the proposed method is tested on several benchmark problems.

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Velasco-Carrau, J., S. García-Nieto, J. V. Salcedo, and R. H. Bishop. "Multi-Objective Optimization for Wind Estimation and Aircraft Model Identification." Journal of Guidance, Control, and Dynamics 39, no. 2 (February 2016): 372–89. http://dx.doi.org/10.2514/1.g001294.

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黄, 忠强. "Trend Prediction Model Based Multi-Objective Estimation of Distribution Algorithm." Artificial Intelligence and Robotics Research 05, no. 01 (2016): 1–12. http://dx.doi.org/10.12677/airr.2016.51001.

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13

Xiaoping, Zhong, Ding Jifeng, Li Weiji, and Zhang Yong. "Robust Airfoil Optimization with Multi-objective Estimation of Distribution Algorithm." Chinese Journal of Aeronautics 21, no. 4 (August 2008): 289–95. http://dx.doi.org/10.1016/s1000-9361(08)60038-2.

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14

Macwan, Richard, Yannick Benezeth, and Alamin Mansouri. "Heart rate estimation using remote photoplethysmography with multi-objective optimization." Biomedical Signal Processing and Control 49 (March 2019): 24–33. http://dx.doi.org/10.1016/j.bspc.2018.10.012.

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Lu, Cheng, Da Teng, Jun-Yu Chen, Cheng-Wei Fei, and Behrooz Keshtegar. "Adaptive vectorial surrogate modeling framework for multi-objective reliability estimation." Reliability Engineering & System Safety 234 (June 2023): 109148. http://dx.doi.org/10.1016/j.ress.2023.109148.

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OKELLO, Moses Oyaro. "Time Governed Multi-Objective Optimization." Eurasia Proceedings of Science Technology Engineering and Mathematics 16 (December 31, 2021): 167–81. http://dx.doi.org/10.55549/epstem.1068585.

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Multi-objective optimization (MOO) is an optimization involving minimization or maximization of several objective functions more than the conventional one objective optimization, which is useful in many fields. Many of the current methodologies addresses challenges and solutions that attempt to solve simultaneously several Objectives with multiple constraints subjoined to each. Often MOO are generally subjected to linear inequality, equality and or bounded constraint that prevent all objectives from being optimized at once. This paper reviews some recent articles in area of MOO and presents deep analysis of Random and Uniform Entry-Exit time of objectives. It further breaks down process into sub-process and then provide some new concepts for solving problems in MOO, which comes due to periodical objectives that do not stay for the entire duration of process lifetime, unlike permanent objectives which are optimized once for the entire process duration. A methodology based on partial optimization that optimizes each objective iteratively and weight convergence method that optimizes sub-group of objectives are given. Furthermore, another method is introduced which involve objective classification, ranking, estimation and prediction where objectives are classified based on their properties, and ranked using a given criteria and in addition estimated for an optimal weight point (pareto optimal point) if it certifies a coveted optimal weight point. Then finally predicted to find how far it deviates from the estimated optimal weight point. A Sample Mathematical Tri-Objectives and Real-world Optimization was analyzed using partial method, ranking and classification method, the result showed that an objective can be added or removed without affecting previous or existing optimal solutions. Therefore, suitable for handling time governed MOO. Although this paper presents concepts work only, it’s practical application are beyond the scope of this paper, however base on analysis and examples presented, the concept is worthy of igniting further research and application.

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Guolin Yu. "Multi-objective estimation of Estimation of Distribution Algorithm based on the Simulated binary Crossover." Journal of Convergence Information Technology 7, no. 3 (February 29, 2012): 110–16. http://dx.doi.org/10.4156/jcit.vol7.issue3.13.

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18

Jiang, Min, Liming Qiu, Zhongqiang Huang, and Gary G. Yen. "Dynamic Multi-objective Estimation of Distribution Algorithm based on Domain Adaptation and Nonparametric Estimation." Information Sciences 435 (April 2018): 203–23. http://dx.doi.org/10.1016/j.ins.2017.12.058.

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Sudha, T. "Multi-Objective Optimization Based Multi-Objective Controller Tuning Method with Robust Stabilization of Fractional Calculus CSTR." WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL 16 (July 8, 2021): 375–82. http://dx.doi.org/10.37394/23203.2021.16.32.

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In Continuous Stirred Tank Reactor (CSTR) have Fractional order PID with the nominal order PID controller has been used to Multi-Criteria Decision Making (MCDM) and EMO (Evolutionary Multi-objective Optimization) by adjustment of control parameters like Hybrid methods in Multi objective optimization. But, this Fractional order PID with the nominal PID controller has maximum performance estimation. Proposed research work focused the Flower Pollination Algorithm based on Multi objective optimization with Genetic evaluation and Fractional order PID with the nominal PID controller is provides CSTR results. When a flower is displayed to maximum variations in this practical state, the Genetic evaluation has been used to identify the variations. The FPID (Flower Pollination Integral Derivative) is used for tuning the parameters of a Fractional order PID with the nominal PID controller for each region to improve the multi-criteria decision making. FPID also denoted as Flower Optimization Integral Derivative (FOID). The Genetic evaluation scheduler has been combined with multiple local linear Fractional order PID with the nominal PID controller to check the stability of loop for entire regions with various levels of temperatures. MATLAB results demonstrate that the feasibility of using the proposed Fractional order PID with the nominal PID controller compared than the existing PID controller, and it shows the FOID attained better results.

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20

Price, Andrew R., Richard J. Myerscough, Ivan I. Voutchkov, Robert Marsh, and Simon J. Cox. "Multi-objective optimization of GENIE Earth system models." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 367, no. 1898 (July 13, 2009): 2623–33. http://dx.doi.org/10.1098/rsta.2009.0039.

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The tuning of parameters in climate models is essential to provide reliable long-term forecasts of Earth system behaviour. We apply a multi-objective optimization algorithm to the problem of parameter estimation in climate models. This optimization process involves the iterative evaluation of response surface models (RSMs), followed by the execution of multiple Earth system simulations. These computations require an infrastructure that provides high-performance computing for building and searching the RSMs and high-throughput computing for the concurrent evaluation of a large number of models. Grid computing technology is therefore essential to make this algorithm practical for members of the GENIE project.

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21

Niri, Mohammad Emami, and David Lumley. "A multi-objective stochastic optimization approach for estimation of subsurface geomodels." ASEG Extended Abstracts 2013, no. 1 (December 2013): 1–4. http://dx.doi.org/10.1071/aseg2013ab019.

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22

Kaji, Hirotaka, and Hajime Kita. "Acceleration of Experiment-Based Evolutionary Multi-Objective Optimization Using Fitness Estimation." IEEJ Transactions on Electronics, Information and Systems 128, no. 3 (2008): 388–98. http://dx.doi.org/10.1541/ieejeiss.128.388.

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23

Gao, Ying, Lingxi Peng, Fufang Li, Miao Liu, and Waixi Liu. "Archimedean copula-based estimation of distribution algorithm for multi-objective optimisation." International Journal of Trust Management in Computing and Communications 1, no. 3/4 (2013): 200. http://dx.doi.org/10.1504/ijtmcc.2013.056430.

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24

Mackay, D. Scott, Sudeep Samanta, Ramakrishna R. Nemani, and Lawrence E. Band. "Multi-objective parameter estimation for simulating canopy transpiration in forested watersheds." Journal of Hydrology 277, no. 3-4 (June 2003): 230–47. http://dx.doi.org/10.1016/s0022-1694(03)00130-6.

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25

Kasiviswanathan, K. S., Jianxun He, and Joo-Hwa Tay. "Flood frequency analysis using multi-objective optimization based interval estimation approach." Journal of Hydrology 545 (February 2017): 251–62. http://dx.doi.org/10.1016/j.jhydrol.2016.12.025.

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26

Palaniraj, Palanivel, and Ganesan Sakthivel. "Multi-objective genetic algorithm on hexagonal search for fast motion estimation." IET Image Processing 13, no. 11 (September 19, 2019): 1892–902. http://dx.doi.org/10.1049/iet-ipr.2018.5918.

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27

Maksimović-Moićević, Sanja, Željko Lukač, and Miodrag Temerinac. "Objective estimation of subjective image quality assessment using multi-parameter prediction." IET Image Processing 13, no. 13 (November 14, 2019): 2428–35. http://dx.doi.org/10.1049/iet-ipr.2018.6143.

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28

Lin, Yanyan, Han Liu, and Qiaoyong Jiang. "A Double Learning Models-Based Multi-Objective Estimation of Distribution Algorithm." IEEE Access 7 (2019): 144580–90. http://dx.doi.org/10.1109/access.2019.2945818.

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29

Zhang, Jian-Qiu, Feng Xu, and Xian-Wen Fang. "Decomposition of Multi-Objective Evolutionary Algorithm based on Estimation of Distribution." Applied Mathematics & Information Sciences 8, no. 1 (January 1, 2014): 249–54. http://dx.doi.org/10.12785/amis/080130.

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30

Patankar, Jayprakash G., and Amitava Mitra. "A multi-objective model for warranty cost estimation using multiple products." Computers & Operations Research 16, no. 4 (January 1989): 341–51. http://dx.doi.org/10.1016/0305-0548(89)90006-3.

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31

Sakthivel, V. P., R. Bhuvaneswari, and S. Subramanian. "Multi-objective parameter estimation of induction motor using particle swarm optimization." Engineering Applications of Artificial Intelligence 23, no. 3 (April 2010): 302–12. http://dx.doi.org/10.1016/j.engappai.2009.06.004.

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32

Tang, Junfeng, Handing Wang, and Lin Xiong. "Surrogate-assisted multi-objective optimization via knee-oriented Pareto front estimation." Swarm and Evolutionary Computation 77 (March 2023): 101252. http://dx.doi.org/10.1016/j.swevo.2023.101252.

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Luo, Na, and Feng Qian. "Parameter estimation of industrial PET reactor using multi-objective kernel density estimation of distribution algorithm." Asia-Pacific Journal of Chemical Engineering 7, no. 5 (November 18, 2011): 783–94. http://dx.doi.org/10.1002/apj.646.

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Petwal, Hemant, and Rinkle Rani. "An Improved Artificial Electric Field Algorithm for Multi-Objective Optimization." Processes 8, no. 5 (May 14, 2020): 584. http://dx.doi.org/10.3390/pr8050584.

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Real-world problems such as scientific, engineering, mechanical, etc., are multi-objective optimization problems. In order to achieve an optimum solution to such problems, multi-objective optimization algorithms are used. A solution to a multi-objective problem is to explore a set of candidate solutions, each of which satisfies the required objective without any other solution dominating it. In this paper, a population-based metaheuristic algorithm called an artificial electric field algorithm (AEFA) is proposed to deal with multi-objective optimization problems. The proposed algorithm utilizes the concepts of strength Pareto for fitness assignment and the fine-grained elitism selection mechanism to maintain population diversity. Furthermore, the proposed algorithm utilizes the shift-based density estimation approach integrated with strength Pareto for density estimation, and it implements bounded exponential crossover (BEX) and polynomial mutation operator (PMO) to avoid solutions trapping in local optima and enhance convergence. The proposed algorithm is validated using several standard benchmark functions. The proposed algorithm’s performance is compared with existing multi-objective algorithms. The experimental results obtained in this study reveal that the proposed algorithm is highly competitive and maintains the desired balance between exploration and exploitation to speed up convergence towards the Pareto optimal front.

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Krauße, T., J. Cullmann, P. Saile, and G. H. Schmitz. "Robust multi-objective calibration strategies – chances for improving flood forecasting." Hydrology and Earth System Sciences Discussions 8, no. 2 (April 15, 2011): 3693–741. http://dx.doi.org/10.5194/hessd-8-3693-2011.

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Abstract. Process-oriented rainfall-runoff models are designed to approximate the complex hydrologic processes within a specific catchment and in particular to simulate the discharge at the catchment outlet. Most of these models exhibit a high degree of complexity and require the determination of various parameters by calibration. Recently automatic calibration methods became popular in order to identify parameter vectors with high corresponding model performance. The model performance is often assessed by a purpose-oriented objective function. Practical experience suggests that in many situations one single objective function cannot adequately describe the model's ability to represent any aspect of the catchment's behaviour. This is regardless whether the objective is aggregated of several criteria that measure different (possibly opposite) aspects of the system behaviour. One strategy to circumvent this problem is to define multiple objective functions and to apply a multi-objective optimisation algorithm to identify the set of Pareto optimal or non-dominated solutions. One possible approach to estimate the Pareto set effectively and efficiently is the particle swarm optimisation (PSO). It has already been successfully applied in various other fields and has been reported to show effective and efficient performance. Krauße and Cullmann (2011b) presented a method entitled ROPEPSO which merges the strengths of PSO and data depth measures in order to identify robust parameter vectors for hydrological models. In this paper we present a multi-objective parameter estimation algorithm, entitled the Multi-Objective Robust Particle Swarm Parameter Estimation (MO-ROPE). The algorithm is a further development of the previously mentioned single-objective ROPEPSO approach. It applies a newly developed multi-objective particle swarm optimisation algorithm in order to identify non-dominated robust model parameter vectors. Subsequently it samples robust parameter vectors by the application of data depth metrics. In a preliminary assessment MO-PSO-GA is compared with other multi-objective optimisation algorithms. In the frame of a real world case study MO-ROPE is applied identifying robust parameter vectors of a distributed hydrological model with focus on flood events in a small, pre-alpine, and fast responding catchment in Switzerland. The method is compared with existing robust parameter estimation methods.

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Togo, Hidetoshi, Kohei Asanuma, Tatsushi Nishi, and Ziang Liu. "Machine Learning and Inverse Optimization for Estimation of Weighting Factors in Multi-Objective Production Scheduling Problems." Applied Sciences 12, no. 19 (September 21, 2022): 9472. http://dx.doi.org/10.3390/app12199472.

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In recent years, scheduling optimization has been utilized in production systems. To construct a suitable mathematical model of a production scheduling problem, modeling techniques that can automatically select an appropriate objective function from historical data are necessary. This paper presents two methods to estimate weighting factors of the objective function in the scheduling problem from historical data, given the information of operation time and setup costs. We propose a machine learning-based method, and an inverse optimization-based method using the input/output data of the scheduling problems when the weighting factors of the objective function are unknown. These two methods are applied to a multi-objective parallel machine scheduling problem and a real-world chemical batch plant scheduling problem. The results of the estimation accuracy evaluation show that the proposed methods for estimating the weighting factors of the objective function are effective.

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Tran, Gia Quoc Bao, Thanh-Phong Pham, and Olivier Sename. "Multi-objective Grid-based Lipschitz NLPV PI Observer for Damper Fault Estimation." IFAC-PapersOnLine 55, no. 6 (2022): 163–68. http://dx.doi.org/10.1016/j.ifacol.2022.07.123.

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V suresh, Chintalapudi, and S. Sivanagaraju. "Multi-Objective Optimized Loadability Estimation by considering Power System Objectives using NSPSO." i-manager's Journal on Power Systems Engineering 2, no. 4 (January 15, 2015): 7–20. http://dx.doi.org/10.26634/jps.2.4.3111.

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Shim, Vui Ann, Kay Chen Tan, Jun Yong Chia, and Abdullah Al Mamun. "Multi-Objective Optimization with Estimation of Distribution Algorithm in a Noisy Environment." Evolutionary Computation 21, no. 1 (March 2013): 149–77. http://dx.doi.org/10.1162/evco_a_00066.

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Many real-world optimization problems are subjected to uncertainties that may be characterized by the presence of noise in the objective functions. The estimation of distribution algorithm (EDA), which models the global distribution of the population for searching tasks, is one of the evolutionary computation techniques that deals with noisy information. This paper studies the potential of EDAs; particularly an EDA based on restricted Boltzmann machines that handles multi-objective optimization problems in a noisy environment. Noise is introduced to the objective functions in the form of a Gaussian distribution. In order to reduce the detrimental effect of noise, a likelihood correction feature is proposed to tune the marginal probability distribution of each decision variable. The EDA is subsequently hybridized with a particle swarm optimization algorithm in a discrete domain to improve its search ability. The effectiveness of the proposed algorithm is examined via eight benchmark instances with different characteristics and shapes of the Pareto optimal front. The scalability, hybridization, and computational time are rigorously studied. Comparative studies show that the proposed approach outperforms other state of the art algorithms.

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Faraji Amiri, M., and J. Behnamian. "Multi-objective green flowshop scheduling problem under uncertainty: Estimation of distribution algorithm." Journal of Cleaner Production 251 (April 2020): 119734. http://dx.doi.org/10.1016/j.jclepro.2019.119734.

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Martí, Luis, Jesús García, Antonio Berlanga, Carlos A. Coello Coello, and José M. Molina. "MB-GNG: Addressing drawbacks in multi-objective optimization estimation of distribution algorithms." Operations Research Letters 39, no. 2 (March 2011): 150–54. http://dx.doi.org/10.1016/j.orl.2011.01.002.

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Zhao, Jingcheng, Yang Liu, and Yufeng Gui. "Multi-Objective Optimization of University Bus Based on Passenger Probability Density Estimation." Applied Mathematics 08, no. 05 (2017): 621–29. http://dx.doi.org/10.4236/am.2017.85048.

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Shi, Xiaoran, and Nurcin Celik. "A particle filtering-based estimation of distribution algorithm for multi-objective optimisation." International Journal of Simulation and Process Modelling 11, no. 3/4 (2016): 176. http://dx.doi.org/10.1504/ijspm.2016.078524.

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44

Sharifi, Soroosh, Mark Sterling, and Donald W. Knight. "Can the application of a multi-objective evolutionary algorithm improve conveyance estimation?" Water and Environment Journal 25, no. 2 (May 5, 2011): 230–40. http://dx.doi.org/10.1111/j.1747-6593.2010.00223.x.

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45

Azzeh, Mohammad, Ali Bou Nassif, Shadi Banitaan, and Fadi Almasalha. "Pareto efficient multi-objective optimization for local tuning of analogy-based estimation." Neural Computing and Applications 27, no. 8 (September 1, 2015): 2241–65. http://dx.doi.org/10.1007/s00521-015-2004-y.

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46

Giagkiozis, Ioannis, and Peter J. Fleming. "Pareto Front Estimation for Decision Making." Evolutionary Computation 22, no. 4 (December 2014): 651–78. http://dx.doi.org/10.1162/evco_a_00128.

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The set of available multi-objective optimisation algorithms continues to grow. This fact can be partially attributed to their widespread use and applicability. However, this increase also suggests several issues remain to be addressed satisfactorily. One such issue is the diversity and the number of solutions available to the decision maker (DM). Even for algorithms very well suited for a particular problem, it is difficult—mainly due to the computational cost—to use a population large enough to ensure the likelihood of obtaining a solution close to the DM's preferences. In this paper we present a novel methodology that produces additional Pareto optimal solutions from a Pareto optimal set obtained at the end run of any multi-objective optimisation algorithm for two-objective and three-objective problem instances.

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Smirnov, A. V. "Properties of objective functions and search algorithms in multi-objective optimization problems." Russian Technological Journal 10, no. 4 (July 30, 2022): 75–85. http://dx.doi.org/10.32362/2500-316x-2022-10-4-75-85.

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Objectives. A frequently used method for obtaining Pareto-optimal solutions is to minimize a selected quality index under restrictions of the other quality indices, whose values are thus preset. For a scalar objective function, the global minimum is sought that contains the restricted indices as penalty terms. However, the landscape of such a function has steep-ascent areas, which significantly complicate the search for the global minimum. This work compared the results of various heuristic algorithms in solving problems of this type. In addition, the possibility of solving such problems using the sequential quadratic programming (SQP) method, in which the restrictions are not imposed as the penalty terms, but included into the Lagrange function, was investigated.Methods. The experiments were conducted using two analytically defined objective functions and two objective functions that are encountered in problems of multi-objective optimization of characteristics of analog filters. The corresponding algorithms were realized in the MATLAB environment.Results. The only heuristic algorithm shown to obtain the optimal solutions for all the functions is the particle swarm optimization algorithm. The sequential quadratic programming (SQP) algorithm was applicable to one of the analytically defined objective functions and one of the filter optimization objective functions, as well as appearing to be significantly superior to heuristic algorithms in speed and accuracy of solutions search. However, for the other two functions, this method was found to be incapable of finding correct solutions.Conclusions. A topical problem is the estimation of the applicability of the considered methods to obtaining Pareto-optimal solutions based on preliminary analysis of properties of functions that determine the quality indices.

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Franks, S. W., K. J. Beven, and J. H. C. Gash. "Multi-objective conditioning of a simple SVAT model." Hydrology and Earth System Sciences 3, no. 4 (December 31, 1999): 477–88. http://dx.doi.org/10.5194/hess-3-477-1999.

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Abstract. It has previously been argued that current Soil Vegetation Atmosphere Transfer (SVAT) models are over-parameterised given the calibration data typically available. Using the Generalised Likelihood Uncertainty Estimation (GLUE) methodology, multiple feasible model parameter sets are here conditioned on latent heat fluxes and then additionally on the sensible and ground heat fluxes at a single site in Amazonia. The model conditioning schemes were then evaluated with a further data set collected at the same site according to their ability to reproduce the latent, sensible and ground heat fluxes. The results indicate that conditioning the model on only the latent heat flux component of the energy balance does not constrain satisfactorily the predictions of the other components of the energy balance. When conditioning on all heat flux objectives, significant additional constraint of the feasible parameter space is achieved with a consequent reduction in the predictive uncertainty. There are still, however, many parameter sets that adequately reproduce the calibration/validation data, leading to significant predictive uncertainty. Surface temperature measurements, whilst also subject to uncertainty, may be employed usefully in a multi-objective calibration of SWAT models.

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Wang, Penghong, Fei Xue, Hangjuan Li, Zhihua Cui, and Jinjun Chen. "A Multi-Objective DV-Hop Localization Algorithm Based on NSGA-II in Internet of Things." Mathematics 7, no. 2 (February 15, 2019): 184. http://dx.doi.org/10.3390/math7020184.

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Locating node technology, as the most fundamental component of wireless sensor networks (WSNs) and internet of things (IoT), is a pivotal problem. Distance vector-hop technique (DV-Hop) is frequently used for location node estimation in WSN, but it has a poor estimation precision. In this paper, a multi-objective DV-Hop localization algorithm based on NSGA-II is designed, called NSGA-II-DV-Hop. In NSGA-II-DV-Hop, a new multi-objective model is constructed, and an enhanced constraint strategy is adopted based on all beacon nodes to enhance the DV-Hop positioning estimation precision, and test four new complex network topologies. Simulation results demonstrate that the precision performance of NSGA-II-DV-Hop significantly outperforms than other algorithms, such as CS-DV-Hop, OCS-LC-DV-Hop, and MODE-DV-Hop algorithms.

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Chien, Wei, Chien-Ching Chiu, Yu-Ting Cheng, Wei-Lin Fang, and Eng Hock Lim. "Multi-Objective Function for SWIPT System by SADDE." Applied Sciences 10, no. 9 (April 30, 2020): 3124. http://dx.doi.org/10.3390/app10093124.

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Simultaneous wireless information and power transfer (SWIPT) optimization with multiple objective function optimization is presented in the millimeter band in this paper. Three different objective functions that are used for harvest power (HP), capacity, and bit error rate (BER) were studied. There are three different nodes in real environment for wireless power transfer (WPT) and SWIPT. The channel estimation calculated by shooting and bouncing ray/image techniques includes multi-path, fading effect, and path-loss in the real environment. We applied beamforming techniques at the transmitter to focus the transmitter energy in order to reduce the multi-path effect and adjust the length of the feed line on each array element in order to find the extremum of the objective functions by the self-adaptive dynamic differential evolution (SADDE) method. Numerical results showed that SWIPT node cannot achieve good performance by single objective function, but wireless power transfer (WPT) can. Nevertheless, both WPT and SWIPT nodes can meet the criteria by the multiple objective function. The harvesting power ratio as well as the BER and capacity can be improved by the multiple objective function to an acceptable level by only reducing a little harvesting energy compared to the best harvesting energy for the single objective function. Finally, the multiple optimization function cannot merely provide good information quality for SWIPT node but achieve good total harvesting power for WPT and SWIPT node as well.

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