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Journal articles on the topic 'Multi-Objective Optimization'

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Author: Grafiati

Published: 4 June 2021

Last updated: 7 July 2024

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1

Xu, Liansong, and Dazhi Pan. "Multi-objective Optimization Based on Chaotic Particle Swarm Optimization." International Journal of Machine Learning and Computing 8, no. 3 (June 2018): 229–35. http://dx.doi.org/10.18178/ijmlc.2018.8.3.692.

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2

Mueller, Carsten. "Multi-Objective Optimization of Software Architectures Using Ant Colony Optimization." Lecture Notes on Software Engineering 2, no. 4 (2014): 371–74. http://dx.doi.org/10.7763/lnse.2014.v2.152.

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3

Velea, Marian N., and Simona Lache. "Decision Making Process on Multi-Objective Optimization Results." International Journal of Materials, Mechanics and Manufacturing 4, no. 3 (2015): 213–17. http://dx.doi.org/10.7763/ijmmm.2016.v4.259.

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4

Lee, Chen Jian Ken, and Hirohisa Noguchi. "515 Multi-objective topology optimization involving 3D surfaces." Proceedings of The Computational Mechanics Conference 2008.21 (2008): 233–34. http://dx.doi.org/10.1299/jsmecmd.2008.21.233.

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5

Coello Coello, Carlos A., Arturo Hernández Aguirre, and Eckart Zitzler. "Evolutionary multi-objective optimization." European Journal of Operational Research 181, no. 3 (September 2007): 1617–19. http://dx.doi.org/10.1016/j.ejor.2006.08.003.

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6

Sörensen, Kenneth, and Johan Springael. "Progressive Multi-Objective Optimization." International Journal of Information Technology & Decision Making 13, no. 05 (September 2014): 917–36. http://dx.doi.org/10.1142/s0219622014500308.

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This paper introduces progressive multi-objective optimization (PMOO), a novel technique to include the decision maker's preferences into the multi-objective optimization process. PMOO integrates a well-known method for multi-criteria decision making (PROMETHEE) into a simple multi-objective metaheuristic by maintaining and updating a small reference archive of nondominated solutions throughout the search. By applying this novel technique to a set of instances of the multi-objective knapsack problem, the superiority of PMOO over the commonly accepted sequential approach of generating a Pareto set approximation first and selecting a single solution afterwards is demonstrated.

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7

Luo, Jianping, Yun Yang, Qiqi Liu, Xia Li, Minrong Chen, and Kaizhou Gao. "A new hybrid memetic multi-objective optimization algorithm for multi-objective optimization." Information Sciences 448-449 (June 2018): 164–86. http://dx.doi.org/10.1016/j.ins.2018.03.012.

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8

Zhang, Kai, Minshi Chen, Xin Xu, and Gary G. Yen. "Multi-objective evolution strategy for multimodal multi-objective optimization." Applied Soft Computing 101 (March 2021): 107004. http://dx.doi.org/10.1016/j.asoc.2020.107004.

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Feng, Huijun, Wei Tang, Lingen Chen, Junchao Shi, and Zhixiang Wu. "Multi-Objective Constructal Optimization for Marine Condensers." Energies 14, no. 17 (September 5, 2021): 5545. http://dx.doi.org/10.3390/en14175545.

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A marine condenser with exhausted steam as the working fluid is researched in this paper. Constructal designs of the condenser are numerically conducted based on single and multi-objective optimizations, respectively. In the single objective optimization, there is an optimal dimensionless tube diameter leading to the minimum total pumping power required by the condenser. After constructal optimization, the total pumping power is decreased by 42.3%. In addition, with the increase in mass flow rate of the steam and heat transfer area and the decrease in total heat transfer rate, the minimum total pumping power required by the condenser decreases. In the multi-objective optimization, the Pareto optimal set of the entropy generation rate and total pumping power is gained. The optimal results gained by three decision methods in the Pareto optimal set and single objective optimizations are compared by the deviation index. The optimal construct gained by the TOPSIS decision method corresponding to the smallest deviation index is recommended in the optimal design of the condenser. These research ideas can also be used to design other heat transfer devices.

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10

Kaliszewski, Ignacy, Janusz Miroforidis, and Jarosław Stańczak. "THE AIRPORT GATE ASSIGNMENT PROBLEM – MULTI-OBJECTIVE OPTIMIZATION VERSUS EVOLUTIONARY MULTI-OBJECTIVE OPTIMIZATION." Computer Science 18, no. 1 (2017): 41. http://dx.doi.org/10.7494/csci.2017.18.1.41.

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11

Luan, Nguyen Duc, Nguyen Duc Minh, and Le Thi Phuong Thanh. "Multi-Objective Optimization of PMEDM Process Parameter by Topsis Method." International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (June 30, 2019): 112–15. http://dx.doi.org/10.31142/ijtsrd23169.

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12

Hussain, S. Jahir, and Ramalingam Sugumar. "Enhanced Whale Optimization Algorithm for Multi-Objective Node Disjoint Routing." Indian Journal Of Science And Technology 17, no. 21 (May 25, 2024): 2138–49. http://dx.doi.org/10.17485/ijst/v17i21.3092.

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Objectives: The research goal is to identify node-disjoint pathways maximizing path lifetime, delivery ratio, etc. while establishing several node-disjoint paths to optimize routing and enhance interference reduction and PDR efficiency. Methods: In the MANET, the Enhanced Whale Optimization Algorithm (EWOA) is utilized to discover the most secure routing path. The dataset used in this research comprises simulated scenarios representing various network conditions and configurations, allowing for a comprehensive evaluation of the proposed work. The software employed for simulation and analysis includes an NS-3 network simulation tool, allowing the model to evaluate the performance of the proposed routing protocol in diverse MANET scenarios. The parameters considered during the evaluation include throughput, end-to-end delay, packet delivery ratio (PDR), and routing overhead. The modifications involve enhancing the routing algorithm to prioritize node-disjoint pathways, thus improving interference reduction, energy efficiency, and overall network performance. Findings: The EWOA for Multi-Objective Node Disjoint Routing demonstrates superior performance compared to existing routing protocols such as SRABC and OLSR across various key metrics. Specifically, in terms of end-to-end delay, EWOA consistently outperforms SRABC and OLSR by reducing delay by 1.3% and 4.7%, respectively. Additionally, EWOA achieves a higher packet delivery ratio, surpassing SRABC and OLSR by 4.3% and 0.4%, respectively. Moreover, EWOA exhibits higher throughput, with a throughput increase of 11.3% compared to SRABC and 6.2% compared to OLSR. Furthermore, EWOA demonstrates lower routing overhead, reducing overhead by 9.6% compared to SRABC and 3.1% compared to OLSR. These findings highlight the efficacy of EWOA in optimizing multi-objective routing in MANETs, offering improved network performance and efficiency compared to existing protocols. Novelty: The Multi-Objective Node Disjoint Routing Protocol with Enhanced Whale Optimization is utilized in MANET to choose the best route, reducing latency resulting from link failure and distributing traffic loads across multiple paths. Keywords: Optimization, Whale Optimization Algorithm (WOA), Optimized Link State Routing (OLSR), Secure Routing Algorithm Blockchain Technology (SRABC)), MANET

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13

Zheng, J. H., Y. N. Kou, Z. X. Jing, and Q. H. Wu. "Towards Many-Objective Optimization: Objective Analysis, Multi-Objective Optimization and Decision-Making." IEEE Access 7 (2019): 93742–51. http://dx.doi.org/10.1109/access.2019.2926493.

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14

Dogan, Vedat, and Steven Prestwich. "Multi-Objective BiLevel Optimization by Bayesian Optimization." Algorithms 17, no. 4 (March 30, 2024): 146. http://dx.doi.org/10.3390/a17040146.

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In a multi-objective optimization problem, a decision maker has more than one objective to optimize. In a bilevel optimization problem, there are the following two decision-makers in a hierarchy: a leader who makes the first decision and a follower who reacts, each aiming to optimize their own objective. Many real-world decision-making processes have various objectives to optimize at the same time while considering how the decision-makers affect each other. When both features are combined, we have a multi-objective bilevel optimization problem, which arises in manufacturing, logistics, environmental economics, defence applications and many other areas. Many exact and approximation-based techniques have been proposed, but because of the intrinsic nonconvexity and conflicting multiple objectives, their computational cost is high. We propose a hybrid algorithm based on batch Bayesian optimization to approximate the upper-level Pareto-optimal solution set. We also extend our approach to handle uncertainty in the leader’s objectives via a hypervolume improvement-based acquisition function. Experiments show that our algorithm is more efficient than other current methods while successfully approximating Pareto-fronts.

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Guo, Weian, Ming Chen, Lei Wang, and Qidi Wu. "Hyper multi-objective evolutionary algorithm for multi-objective optimization problems." Soft Computing 21, no. 20 (May 24, 2016): 5883–91. http://dx.doi.org/10.1007/s00500-016-2163-5.

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16

Hasegawa, Tsuguto, Atsushi Ishigame, and Keiichiro Yasuda. "Multi-Objective Particle Swarm Optimization with Particle Density." IEEJ Transactions on Electronics, Information and Systems 129, no. 11 (2009): 2097–98. http://dx.doi.org/10.1541/ieejeiss.129.2097.

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17

Verma, Manish, and Ritu Shrivastava. "Multi Objective Optimization of Near-Dry EDM using MOORA-PCA based Taguchi Optimization Method." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 2534–39. http://dx.doi.org/10.31142/ijtsrd15647.

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18

Wang, Rui, Shiming Lai, Guohua Wu, Lining Xing, Ling Wang, and Hisao Ishibuchi. "Multi-clustering via evolutionary multi-objective optimization." Information Sciences 450 (June 2018): 128–40. http://dx.doi.org/10.1016/j.ins.2018.03.047.

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19

Min, Xinyuan, Jaap Sok, Feije de Zwart, and Alfons Oude Lansink. "Multi-stakeholder multi-objective greenhouse design optimization." Agricultural Systems 215 (March 2024): 103855. http://dx.doi.org/10.1016/j.agsy.2024.103855.

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20

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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21

Naseh, H., MNP Meibody, and F. Ommi. "Catalyst bed multi-objective optimization." Aeronautics and Aerospace Open Access Journal 3, no. 1 (January 4, 2019): 11–14. http://dx.doi.org/10.15406/aaoaj.2019.03.00076.

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22

Trummer, Immanuel, and Christoph Koch. "Multi-objective parametric query optimization." Communications of the ACM 60, no. 10 (September 25, 2017): 81–89. http://dx.doi.org/10.1145/3068612.

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23

Trummer, Immanuel, and Christoph Koch. "Multi-objective parametric query optimization." Proceedings of the VLDB Endowment 8, no. 3 (November 2014): 221–32. http://dx.doi.org/10.14778/2735508.2735512.

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24

Hämäläinen, Raimo P., and Juha Mäntysaari. "Dynamic multi-objective heating optimization." European Journal of Operational Research 142, no. 1 (October 2002): 1–15. http://dx.doi.org/10.1016/s0377-2217(01)00282-x.

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25

Niu, Ben, Hong Wang, Jingwen Wang, and Lijing Tan. "Multi-objective bacterial foraging optimization." Neurocomputing 116 (September 2013): 336–45. http://dx.doi.org/10.1016/j.neucom.2012.01.044.

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26

Galuzio, Paulo Paneque, Emerson Hochsteiner de Vasconcelos Segundo, Leandro dos Santos Coelho, and Viviana Cocco Mariani. "MOBOpt — multi-objective Bayesian optimization." SoftwareX 12 (July 2020): 100520. http://dx.doi.org/10.1016/j.softx.2020.100520.

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27

Trummer, Immanuel, and Christoph Koch. "Multi-Objective Parametric Query Optimization." ACM SIGMOD Record 45, no. 1 (June 2, 2016): 24–31. http://dx.doi.org/10.1145/2949741.2949748.

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28

Jie Zeng and Wei Nie. "Novel multi-objective optimization algorithm." Journal of Systems Engineering and Electronics 25, no. 4 (August 2014): 697–710. http://dx.doi.org/10.1109/jsee.2014.00080.

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29

Sergeyev, Yaroslav D. "Non-convex multi-objective optimization." Optimization Methods and Software 33, no. 2 (October 27, 2017): 416–17. http://dx.doi.org/10.1080/10556788.2017.1392081.

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30

Trummer, Immanuel, and Christoph Koch. "Multi-objective parametric query optimization." VLDB Journal 26, no. 1 (August 18, 2016): 107–24. http://dx.doi.org/10.1007/s00778-016-0439-0.

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31

Duan, Qibin, and Dirk P. Kroese. "Splitting for Multi-objective Optimization." Methodology and Computing in Applied Probability 20, no. 2 (June 8, 2017): 517–33. http://dx.doi.org/10.1007/s11009-017-9572-5.

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32

Elham, Ali, and Michel J. L. van Tooren. "Winglet multi-objective shape optimization." Aerospace Science and Technology 37 (August 2014): 93–109. http://dx.doi.org/10.1016/j.ast.2014.05.011.

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33

Roland, Julien, Yves De Smet, and José Rui Figueira. "Inverse multi-objective combinatorial optimization." Discrete Applied Mathematics 161, no. 16-17 (November 2013): 2764–71. http://dx.doi.org/10.1016/j.dam.2013.04.024.

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34

Al-Dujaili, Abdullah, and S. Suresh. "Multi-Objective Simultaneous Optimistic Optimization." Information Sciences 424 (January 2018): 159–74. http://dx.doi.org/10.1016/j.ins.2017.09.066.

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35

Zouache, Djaafar, Yahya Ould Arby, Farid Nouioua, and Fouad Ben Abdelaziz. "Multi-objective chicken swarm optimization: A novel algorithm for solving multi-objective optimization problems." Computers & Industrial Engineering 129 (March 2019): 377–91. http://dx.doi.org/10.1016/j.cie.2019.01.055.

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36

Adámek, Mikuláš, and Rastislav Toman. "RANGE EXTENDER ICE MULTI-PARAMETRIC MULTI-OBJECTIVE OPTIMIZATION." MECCA Journal of Middle European Construction and Design of Cars 18, no. 1 (November 10, 2021): 10. http://dx.doi.org/10.14311/mecdc.2021.01.02.

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Range Extended Electric Vehicles (REEV) are still one of the suitable concepts for modern sustainable low emission vehicles. REEV is equipped with a small and lightweight unit, comprised usually of an internal combustion engine with an electric generator, and has thus the technical potential to overcome the main limitations of a pure electric vehicle – range anxiety, overall driving range, heating, and air-conditioning demands – using smaller battery: saving money, and raw materials. Even though several REx ICE concepts were designed in past, most of the available studies lack more complex design and optimization approach, not exploiting the advantageous single point operation of these engines. Resulting engine designs are usually rather conservative, not optimized for the best efficiency. This paper presents a multi-parametric and multi-objective optimization approach, that is applied on a REx ICE. Our optimization toolchain combines a parametric GT-Suite ICE simulation model, modeFRONTIER optimization software with various optimization strategies, and a parametric CAD model, that first provides some simulation model inputs, and second also serves for the final designs’ feasibility check. The chosen ICE concept is a 90 degrees V-twin engine, four-stroke, spark-ignition, naturally aspirated, port injected, OHV engine. The optimization goal is to find the thermodynamic optima for three different design scenarios of our concept – three different engine displacements – addressing the compactness requirement of a REx ICE. The optimization results show great fuel efficiency potential by applying our optimization methodology, following the general trends in increasing ICE efficiency, and power for a naturally aspirated concept.

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37

Liu, Haichao, Xiangjie Jin, and Fagui Zhang. "Multi-objective robust design of vehicle structure based on multi-objective particle swarm optimization." Journal of Intelligent & Fuzzy Systems 39, no. 6 (December 4, 2020): 9063–71. http://dx.doi.org/10.3233/jifs-189305.

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With the continuous spread of COVID-19 epidemic, the strict control of personnel makes it a problem to optimize the design of vehicle parameters after field measurement. The energy absorption characteristics and deformation mode of the front structure of the vehicle determine the acceleration or force response of the vehicle body during the impact, which plays an important role in occupant protection. The traditional multi-objective optimization method is to transform multi-objective problems into single objective optimization problems through weighted combination, objective planning, efficiency coefficient and other methods. This method requires a strong prior knowledge. The purpose of this paper is to combine the experimental design with the Multi-objective Particle Swarm Optimization (MPSO) method to achieve the optimization of the crash worthiness of automobile structure. This method can effectively overcome the defect of low precision caused by the conventional response surface method in the whole design space. In this paper, the multi-objective particle swarm optimization method is applied to the research of Crash worthiness optimization of automobile structure, which expands the application field of the multi-objective particle swarm optimization method, and also has a very big role in the optimization of other complex systems. It can be seen from the experiment that the speed of multi-objective particle swarm optimization is much faster than that of other methods. Only 100 iterations can get the relative better results. The case study on the front structure of a car shows that the method has a good result. It is of great significance to apply the method to the optimization design of the crash worthiness of the car structure to improve the crash safety of the car under the influence of COVID-19 epidemic.

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S. Kannan, P., S. Durairaj, and Er K. Samuktha. "New Concept of Percentage Total Deviation Method for Multi-Objective Optimization." International Journal of Science and Research (IJSR) 12, no. 10 (October 5, 2023): 93–96. http://dx.doi.org/10.21275/sr23930181656.

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39

Fun Khor, Eik. "General Multi-Objective Performance Expression for Population-Based Search and Optimization." International Journal of Science and Research (IJSR) 13, no. 5 (May 5, 2024): 711–22. http://dx.doi.org/10.21275/sr24511075354.

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40

Xiao, Jin-ke, Wei-min Li, Xin-rong Xiao, and Cheng-zhong LV. "A novel immune dominance selection multi-objective optimization algorithm for solving multi-objective optimization problems." Applied Intelligence 46, no. 3 (November 9, 2016): 739–55. http://dx.doi.org/10.1007/s10489-016-0866-z.

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41

Deb, Kalyanmoy, and Himanshu Gupta. "Introducing Robustness in Multi-Objective Optimization." Evolutionary Computation 14, no. 4 (December 2006): 463–94. http://dx.doi.org/10.1162/evco.2006.14.4.463.

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In optimization studies including multi-objective optimization, the main focus is placed on finding the global optimum or global Pareto-optimal solutions, representing the best possible objective values. However, in practice, users may not always be interested in finding the so-called global best solutions, particularly when these solutions are quite sensitive to the variable perturbations which cannot be avoided in practice. In such cases, practitioners are interested in finding the robust solutions which are less sensitive to small perturbations in variables. Although robust optimization is dealt with in detail in single-objective evolutionary optimization studies, in this paper, we present two different robust multi-objective optimization procedures, where the emphasis is to find a robust frontier, instead of the global Pareto-optimal frontier in a problem. The first procedure is a straightforward extension of a technique used for single-objective optimization and the second procedure is a more practical approach enabling a user to set the extent of robustness desired in a problem. To demonstrate the differences between global and robust multi-objective optimization principles and the differences between the two robust optimization procedures suggested here, we develop a number of constrained and unconstrained test problems having two and three objectives and show simulation results using an evolutionary multi-objective optimization (EMO) algorithm. Finally, we also apply both robust optimization methodologies to an engineering design problem.

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42

Hu, Yabao, Hanning Chen, Maowei He, Liling Sun, Rui Liu, and Hai Shen. "Multi-Swarm Multi-Objective Optimizer Based on p-Optimality Criteria for Multi-Objective Portfolio Management." Mathematical Problems in Engineering 2019 (January 21, 2019): 1–22. http://dx.doi.org/10.1155/2019/8418369.

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Portfolio management is an important technology for reasonable investment, fund management, optimal asset allocation, and effective investment. Portfolio optimization problem (POP) has been recognized as an NP-hard problem involving numerous objectives as well as constraints. Applications of evolutionary algorithms and swarm intelligence optimizers for resolving multi-objective POP (MOPOP) have attracted considerable attention of researchers, yet their solutions usually convert MOPOP to POP by means of weighted coefficient method. In this paper, a multi-swarm multi-objective optimizer based on p-optimality criteria called p-MSMOEAs is proposed that tries to find all the Pareto optimal solutions by optimizing all objectives at the same time, rather than through the above transforming method. The proposed p-MSMOEAs extended original multiple objective evolutionary algorithms (MOEAs) to cooperative mode through combining p-optimality criteria and multi-swarm strategy. Comparative experiments of p-MSMOEAs and several MOEAs have been performed on six mathematical benchmark functions and two portfolio instances. Simulation results indicate that p-MSMOEAs are superior for portfolio optimization problem to MOEAs when it comes to optimization accuracy as well as computation robustness.

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Ono, Satoshi, Yohei Yoshitake, and Shigeru Nakayama. "Robust optimization using multi-objective particle swarm optimization." Artificial Life and Robotics 14, no. 2 (November 2009): 174–77. http://dx.doi.org/10.1007/s10015-009-0647-4.

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44

Mirjalili, Seyedeh Zahra, Seyedali Mirjalili, Shahrzad Saremi, Hossam Faris, and Ibrahim Aljarah. "Grasshopper optimization algorithm for multi-objective optimization problems." Applied Intelligence 48, no. 4 (August 4, 2017): 805–20. http://dx.doi.org/10.1007/s10489-017-1019-8.

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45

Teng, Suyan, Loo Hay Lee, and Ek Peng Chew. "Multi-objective ordinal optimization for simulation optimization problems." Automatica 43, no. 11 (November 2007): 1884–95. http://dx.doi.org/10.1016/j.automatica.2007.03.011.

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46

Kawarabayashi, Masaru, Junichi Tsuchiya, and Keiichiro Yasuda. "Integrated Optimization by Multi-Objective Particle Swarm Optimization." IEEJ Transactions on Electrical and Electronic Engineering 5, no. 1 (January 2010): 79–81. http://dx.doi.org/10.1002/tee.20496.

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47

Yang, Yufei, and Changsheng Zhang. "A Multi-Objective Carnivorous Plant Algorithm for Solving Constrained Multi-Objective Optimization Problems." Biomimetics 8, no. 2 (March 26, 2023): 136. http://dx.doi.org/10.3390/biomimetics8020136.

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Satisfying various constraints and multiple objectives simultaneously is a significant challenge in solving constrained multi-objective optimization problems. To address this issue, a new approach is proposed in this paper that combines multi-population and multi-stage methods with a Carnivorous Plant Algorithm. The algorithm employs the ϵ-constraint handling method, with the ϵ value adjusted according to different stages to meet the algorithm’s requirements. To improve the search efficiency, a cross-pollination is designed based on the trapping mechanism and pollination behavior of carnivorous plants, thus balancing the exploration and exploitation abilities and accelerating the convergence speed. Moreover, a quasi-reflection learning mechanism is introduced for the growth process of carnivorous plants, enhancing the optimization efficiency and improving its global convergence ability. Furthermore, the quadratic interpolation method is introduced for the reproduction process of carnivorous plants, which enables the algorithm to escape from local optima and enhances the optimization precision and convergence speed. The proposed algorithm’s performance is evaluated on several test suites, including DC-DTLZ, FCP, DASCMOP, ZDT, DTLZ, and RWMOPs. The experimental results indicate competitive performance of the proposed algorithm over the state-of-the-art constrained multi-objective optimization algorithms.

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48

Bansal, Rohit, Deepak Kumar, and Sushil Kumar. "Multi-objective Multi-Join Query Optimization using Modified Grey Wolf Optimization." International Journal of Advanced Intelligence Paradigms 17, no. 1/2 (2020): 1. http://dx.doi.org/10.1504/ijaip.2020.10019251.

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49

Khojah, Heba Abdulrahman, and Mohamed Atef Mosa. "Multi-objective optimization for multi-satellite scheduling task." Journal of Soft Computing Exploration 3, no. 1 (March 30, 2022): 19–30. http://dx.doi.org/10.52465/joscex.v3i1.71.

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The satellites scheduling mission play an effective role in enhancing the role of ground station control and monitoring systems. In this search, SGSEO is re-formulated into a multi-objective optimization task. Therefore, the Gravitational Search Algorithm GSA is exploited to attain several essential objectives for generating tight scheduling. Moreover, particle swarm optimization model PSO is consolidated with GSA in a novel form for strengthening its ability of local search and slow the speed of convergence. On the other side, to make the most of the satellite resources in the right direction, we have observed targets that have fewer observational opportunities to keep them from being lost. The PageRank algorithm is used to fulfil this issue by ranking the candidate's strips. Finally, the effect of different parameters of the proposed approach was studied by experimental outcomes and compared with previous methods. It has shown that the performance of the proposed approach is superior to its peers from other methods.

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Delhoum, Zohra Sabrina. "Contribution to multi-level multi-objective linear optimization." Journal of Information and Optimization Sciences 42, no. 6 (August 18, 2021): 1383–95. http://dx.doi.org/10.1080/02522667.2021.1925451.

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