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Journal articles on the topic 'Multi-Objective'

To see the other types of publications on this topic, follow the link: Multi-Objective.

Author: Grafiati

Published: 4 June 2021

Last updated: 25 May 2024

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1

Dimitrov, Dimitar, Pierre-Brice Wieber, and Adrien Escande. "Multi-Objective Control of Robots." Journal of the Robotics Society of Japan 32, no. 6 (2014): 512–18. http://dx.doi.org/10.7210/jrsj.32.512.

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2

Neralić, Luka, and Sanjo Zlobec. "LFS functions in multi-objective programming." Applications of Mathematics 41, no. 5 (1996): 347–66. http://dx.doi.org/10.21136/am.1996.134331.

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3

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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4

Tekin, Cem, and Eralp Turgay. "Multi-objective Contextual Multi-armed Bandit With a Dominant Objective." IEEE Transactions on Signal Processing 66, no. 14 (July 15, 2018): 3799–813. http://dx.doi.org/10.1109/tsp.2018.2841822.

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5

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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6

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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7

Brunello, Andrea, Enrico Marzano, Angelo Montanari, and Guido Sciavicco. "Decision Tree Pruning via Multi-Objective Evolutionary Computation." International Journal of Machine Learning and Computing 7, no. 6 (December 2017): 167–75. http://dx.doi.org/10.18178/ijmlc.2017.7.6.641.

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8

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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9

M.Jayalakshmi, M. Jayalakshmi, and P. Pandian P.Pandian. "Solving Fully Fuzzy Multi-Objective Linear Programming Problems." International Journal of Scientific Research 3, no. 4 (June 1, 2012): 1–6. http://dx.doi.org/10.15373/22778179/apr2014/174.

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10

Guo, Yi Nan, Yuan Yuan Cao, and Dan Dan Liu. "Multi-Population Multi-Objective Cultural Algorithm." Advanced Materials Research 156-157 (October 2010): 52–55. http://dx.doi.org/10.4028/www.scientific.net/amr.156-157.52.

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In existing multi-population multi-objective cultural algorithms, information are exchanged among sub-populations by individuals. However, migrated individuals can not reflect the evolution information enough, which limits the evolution performance.In order to enhance the migration efficiency, a novel multi-population multi-objective cultural algorithm adopting knowledge migration is proposed. Implicit knowledge extracted from the evolution process of each sub-population directly reflects the information about dominant search space. By migrating the knowledge among sub-populations at the constant interval, the algorithm realizes more effective interaction with less communication cost. Taken benchmark functions as the examples, simulation results indicate that the algorithm can effectively obtain the Pareto-optimal sets of multi-objective optimization problems. The distribution performance is also improved.

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11

Khishe, M., N. Orouji, and M. R. Mosavi. "Multi-Objective chimp Optimizer: An innovative algorithm for Multi-Objective problems." Expert Systems with Applications 211 (January 2023): 118734. http://dx.doi.org/10.1016/j.eswa.2022.118734.

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12

Tsai, Shang-Jeng, Tsung-Ying Sun, Chan-Cheng Liu, Sheng-Ta Hsieh, Wun-Ci Wu, and Shih-Yuan Chiu. "An improved multi-objective particle swarm optimizer for multi-objective problems." Expert Systems with Applications 37, no. 8 (August 2010): 5872–86. http://dx.doi.org/10.1016/j.eswa.2010.02.018.

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13

., Reza Farshadnia. "Multi-Objective Genetic Algorithm Assessment by Multi-Objective Constrained Test Problems." Information Technology Journal 1, no. 1 (January 1, 2002): 69–74. http://dx.doi.org/10.3923/itj.2002.69.74.

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14

Hsieh, Sheng-Ta, Shih-Yuan Chiu, and Shi-Jim Yen. "An Improved Multi-Objective Genetic Algorithm for Solving Multi-objective Problems." Applied Mathematics & Information Sciences 7, no. 5 (September 1, 2013): 1933–41. http://dx.doi.org/10.12785/amis/070531.

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15

Basu, M. "Multi-objective optimal reactive power dispatch using multi-objective differential evolution." International Journal of Electrical Power & Energy Systems 82 (November 2016): 213–24. http://dx.doi.org/10.1016/j.ijepes.2016.03.024.

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16

Peng, Shunshun, and Taolin Guo. "Multi-Objective Service Composition Using Enhanced Multi-Objective Differential Evolution Algorithm." Computational Intelligence and Neuroscience 2023 (March 4, 2023): 1–10. http://dx.doi.org/10.1155/2023/8184367.

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In recent years, the optimization of multi-objective service composition in distributed systems has become an important issue. Existing work makes a smaller set of Pareto-optimal solutions to represent the Pareto Front (PF). However, they do not support complex mapping of the Pareto-optimal solutions to quality of service (QoS) objective space, thus having limitations in providing a representative set of solutions. We propose an enhanced multi-objective differential evolution algorithm to seek a representative set of solutions with good proximity and distributivity. Specially, we propose a dual strategy to adjust the usage of different creation operators, to maintain the evolutionary pressure toward the true PF. Then, we propose a reference vector neighbor search to have a fine-grained search. The proposed approach has been tested on a real-world dataset that locates a representative set of solutions with proximity and distributivity.

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17

Jiaze Sun, Jiaze Sun, Nan Han Jiaze Sun, Jianbin Huang Nan Han, Jiahui Deng Jianbin Huang, and Yang Geng Jiahui Deng. "A Fast Response Multi-Objective Matching Algorithm for Ridesharing." 網際網路技術學刊 22, no. 5 (September 2021): 1107–16. http://dx.doi.org/10.53106/160792642021092205014.

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18

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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19

Dhillon, Javed, and Sanjay K. Jain. "Multi-Objective Generation and Emission Dispatch Using NSGA-II." International Journal of Engineering and Technology 3, no. 5 (2011): 460–66. http://dx.doi.org/10.7763/ijet.2011.v3.270.

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20

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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21

Faceli, Katti, André C. P. L. F. de Carvalho, and Marcílio C. P. de Souto. "Multi-objective clustering ensemble." International Journal of Hybrid Intelligent Systems 4, no. 3 (October 17, 2007): 145–56. http://dx.doi.org/10.3233/his-2007-4302.

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22

Ndiaye, Amadou, Patrick Castéra, Christophe Fernandez, and Franck Michaud. "Multi-objective preliminary ecodesign." International Journal on Interactive Design and Manufacturing (IJIDeM) 3, no. 4 (September 16, 2009): 237–45. http://dx.doi.org/10.1007/s12008-009-0080-x.

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23

Jowitt, P. W. "Multi-objective decision making." Applied Mathematical Modelling 10, no. 2 (April 1986): 144. http://dx.doi.org/10.1016/0307-904x(86)90087-9.

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24

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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25

de Oliveira, Valeriano Antunes, and Marko Antonio Rojas-Medar. "Multi-objective infinite programming." Computers & Mathematics with Applications 55, no. 9 (May 2008): 1907–22. http://dx.doi.org/10.1016/j.camwa.2007.08.029.

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26

Engeland, Kolbjørn, Isabelle Braud, Lars Gottschalk, and Etienne Leblois. "Multi-objective regional modelling." Journal of Hydrology 327, no. 3-4 (August 2006): 339–51. http://dx.doi.org/10.1016/j.jhydrol.2005.11.022.

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27

Roijers, Diederik M., and Shimon Whiteson. "Multi-Objective Decision Making." Synthesis Lectures on Artificial Intelligence and Machine Learning 11, no. 1 (April 20, 2017): 1–129. http://dx.doi.org/10.2200/s00765ed1v01y201704aim034.

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28

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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29

Gu, Shenkai, Ran Cheng, and Yaochu Jin. "Multi-objective ensemble generation." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 5, no. 5 (July 7, 2015): 234–45. http://dx.doi.org/10.1002/widm.1158.

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30

Pal, Monalisa, and Sanghamitra Bandyopadhyay. "Decomposition in decision and objective space for multi-modal multi-objective optimization." Swarm and Evolutionary Computation 62 (April 2021): 100842. http://dx.doi.org/10.1016/j.swevo.2021.100842.

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31

Luo, Zhongyang, Umair Sultan, Mingjiang Ni, Hao Peng, Bingwei Shi, and Gang Xiao. "Multi-objective optimization for GPU3 Stirling engine by combining multi-objective algorithms." Renewable Energy 94 (August 2016): 114–25. http://dx.doi.org/10.1016/j.renene.2016.03.008.

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32

Garcı́a-Pedrajas, N., C. Hervás-Martı́nez, and J. Muñoz-Pérez. "Multi-objective cooperative coevolution of artificial neural networks (multi-objective cooperative networks)." Neural Networks 15, no. 10 (December 2002): 1259–78. http://dx.doi.org/10.1016/s0893-6080(02)00095-3.

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33

Ghasemi, Ali, Khalil Valipour, and Akbar Tohidi. "Multi objective optimal reactive power dispatch using a new multi objective strategy." International Journal of Electrical Power & Energy Systems 57 (May 2014): 318–34. http://dx.doi.org/10.1016/j.ijepes.2013.11.049.

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34

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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35

Biswal, M. P., and Srikumar Acharya. "Multi-choice multi-objective linear programming problem." Journal of Interdisciplinary Mathematics 12, no. 5 (October 2009): 606–37. http://dx.doi.org/10.1080/09720502.2009.10700650.

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36

Agarwal, Manoj, Nitin Agrawal, Shikhar Sharma, Lovekesh Vig, and Naveen Kumar. "Parallel multi-objective multi-robot coalition formation." Expert Systems with Applications 42, no. 21 (November 2015): 7797–811. http://dx.doi.org/10.1016/j.eswa.2015.05.032.

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37

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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38

Bejan, Adrian, and Sylvie Lorente. "Constructal multi-scale and multi-objective structures." International Journal of Energy Research 29, no. 7 (2005): 689–710. http://dx.doi.org/10.1002/er.1100.

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39

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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40

Yannibelli, Virginia, and Analía Amandi. "Hybridizing a multi-objective simulated annealing algorithm with a multi-objective evolutionary algorithm to solve a multi-objective project scheduling problem." Expert Systems with Applications 40, no. 7 (June 2013): 2421–34. http://dx.doi.org/10.1016/j.eswa.2012.10.058.

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41

Nahar, Samsun, Md Asadujjaman, Khadiza Begum, Mahede-Ul-Hassan, and Md Abdul Alim. "Characteristics of Multi-Objective Linear Programming Problem and Multi-Objective Linear Fractional Programming Problem Taking Maximum Value of Multi-Objective Functions." Applied Mathematics 15, no. 01 (2024): 22–32. http://dx.doi.org/10.4236/am.2024.151003.

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42

Premkumar, M., Pradeep Jangir, R. Sowmya, Hassan Haes Alhelou, Seyedali Mirjalili, and B. Santhosh Kumar. "Multi-objective equilibrium optimizer: framework and development for solving multi-objective optimization problems." Journal of Computational Design and Engineering 9, no. 1 (December 30, 2021): 24–50. http://dx.doi.org/10.1093/jcde/qwab065.

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ABSTRACT This paper proposes a new Multi-Objective Equilibrium Optimizer (MOEO) to handle complex optimization problems, including real-world engineering design optimization problems. The Equilibrium Optimizer (EO) is a recently reported physics-based metaheuristic algorithm, and it has been inspired by the models used to predict equilibrium state and dynamic state. A similar procedure is utilized in MOEO by combining models in a different target search space. The crowding distance mechanism is employed in the MOEO algorithm to balance exploitation and exploration phases as the search progresses. In addition, a non-dominated sorting strategy is also merged with the MOEO algorithm to preserve the population diversity and it has been considered as a crucial problem in multi-objective metaheuristic algorithms. An archive with an update function is used to uphold and improve the coverage of Pareto with optimal solutions. The performance of MOEO is validated for 33 contextual problems with 6 constrained, 12 unconstrained, and 15 practical constrained engineering design problems, including non-linear problems. The result obtained by the proposed MOEO algorithm is compared with other state-of-the-art multi-objective optimization algorithms. The quantitative and qualitative results indicate that the proposed MOEO provides more competitive outcomes than the different algorithms. From the results obtained for all 33 benchmark optimization problems, the efficiency, robustness, and exploration ability to solve multi-objective problems of the MOEO algorithm are well defined and clarified. The paper is further supported with extra online service and guideline at https://premkumarmanoharan.wixsite.com/mysite.

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43

MOU, Jianhui. "Multi-objective Genetic Algorithm for Solving Multi-objective Flow-shop Inverse Scheduling Problems." Journal of Mechanical Engineering 52, no. 22 (2016): 186. http://dx.doi.org/10.3901/jme.2016.22.186.

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44

Bouzoubia, Samira, Abdesslem Layeb, and Salim Chikhi. "A multi-objective chemical reaction optimisation algorithm for multi-objective travelling salesman problem." International Journal of Innovative Computing and Applications 6, no. 2 (2014): 87. http://dx.doi.org/10.1504/ijica.2014.066498.

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45

Zeng, Sanyou Y., Lishan S. Kang, and Lixin X. Ding. "An Orthogonal Multi-objective Evolutionary Algorithm for Multi-objective Optimization Problems with Constraints." Evolutionary Computation 12, no. 1 (March 2004): 77–98. http://dx.doi.org/10.1162/evco.2004.12.1.77.

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In this paper, an orthogonal multi-objective evolutionary algorithm (OMOEA) is proposed for multi-objective optimization problems (MOPs) with constraints. Firstly, these constraints are taken into account when determining Pareto dominance. As a result, a strict partial-ordered relation is obtained, and feasibility is not considered later in the selection process. Then, the orthogonal design and the statistical optimal method are generalized to MOPs, and a new type of multi-objective evolutionary algorithm (MOEA) is constructed. In this framework, an original niche evolves first, and splits into a group of sub-niches. Then every sub-niche repeats the above process. Due to the uniformity of the search, the optimality of the statistics, and the exponential increase of the splitting frequency of the niches, OMOEA uses a deterministic search without blindness or stochasticity. It can soon yield a large set of solutions which converges to the Pareto-optimal set with high precision and uniform distribution. We take six test problems designed by Deb, Zitzler et al., and an engineering problem (W) with constraints provided by Ray et al. to test the new technique. The numerical experiments show that our algorithm is superior to other MOGAS and MOEAs, such as FFGA, NSGAII, SPEA2, and so on, in terms of the precision, quantity and distribution of solutions. Notably, for the engineering problem W, it finds the Pareto-optimal set, which was previously unknown.

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46

Li, Xiangtao, and Shijing Ma. "Multi-Objective Memetic Search Algorithm for Multi-Objective Permutation Flow Shop Scheduling Problem." IEEE Access 4 (2016): 2154–65. http://dx.doi.org/10.1109/access.2016.2565622.

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47

Dhiman, Gaurav, and Vijay Kumar. "Multi-objective spotted hyena optimizer: A Multi-objective optimization algorithm for engineering problems." Knowledge-Based Systems 150 (June 2018): 175–97. http://dx.doi.org/10.1016/j.knosys.2018.03.011.

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48

Gujarathi, Ashish M., and B. V. Babu. "Hybrid strategy of multi-objective differential evolution (H-MODE) for multi-objective optimisation." International Journal of Computational Intelligence Studies 2, no. 2 (2013): 157. http://dx.doi.org/10.1504/ijcistudies.2013.055223.

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49

Maeda, Takuya, Shinji Nishiwaki, Kazuhiro Izui, and Masataka Yoshimura. "A Method to Formulate Appropriate Multi-objective Functions for Multi-objective Topology Optimizations." Proceedings of the JSME annual meeting 2004.7 (2004): 213–14. http://dx.doi.org/10.1299/jsmemecjo.2004.7.0_213.

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50

Kaur, Mandeep, and Sanjay Kadam. "A novel multi-objective bacteria foraging optimization algorithm (MOBFOA) for multi-objective scheduling." Applied Soft Computing 66 (May 2018): 183–95. http://dx.doi.org/10.1016/j.asoc.2018.02.011.

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