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1

Jensen, Mikkel T. "Helper-objectives: Using multi-objective evolutionary algorithms for single-objective optimisation." Journal of Mathematical Modelling and Algorithms 3, no. 4 (2005): 323–47. http://dx.doi.org/10.1007/s10852-005-2582-2.

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2

Jensen, Mikkel T. "Helper-Objectives: Using Multi-Objective Evolutionary Algorithms for Single-Objective Optimisation." Journal of Mathematical Modelling and Algorithms 3, no. 4 (2004): 323–47. http://dx.doi.org/10.1023/b:jmma.0000049378.57591.c6.

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3

COELLO, CARLOS A. COELLO. "TREATING CONSTRAINTS AS OBJECTIVES FOR SINGLE-OBJECTIVE EVOLUTIONARY OPTIMIZATION." Engineering Optimization 32, no. 3 (2000): 275–308. http://dx.doi.org/10.1080/03052150008941301.

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4

M, Avinasi C., and Balamurugan Balamurugan. "An Optimization of AWJM Process by Using Single Objective Function." International Journal of Research Publication and Reviews 5, no. 3 (2024): 1097–101. http://dx.doi.org/10.55248/gengpi.5.0324.0648.

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5

Jabs, Christoph, Jeremias Berg, Andreas Niskanen, and Matti Järvisalo. "From Single-Objective to Bi-Objective Maximum Satisfiability Solving." Journal of Artificial Intelligence Research 80 (August 2, 2024): 1223–69. http://dx.doi.org/10.1613/jair.1.15333.

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The declarative approach is key to efficiently finding optimal solutions to various types of NP-hard real-world combinatorial optimization problems. Most work on practical declarative solvers—ranging from classical integer programming to finite-domain constraint optimization and maximum satisfiability (MaxSAT)—has focused on optimization under a single objective; fewer advances have been made towards efficient declarative techniques for multi-objective optimization problems. Motivated by significant recent advances in practical solvers for MaxSAT, in this work we develop BiOptSat, an exact dec
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6

Jiao, Ruwang, Sanyou Zeng, Jawdat S. Alkasassbeh, and Changhe Li. "Dynamic multi-objective evolutionary algorithms for single-objective optimization." Applied Soft Computing 61 (December 2017): 793–805. http://dx.doi.org/10.1016/j.asoc.2017.08.030.

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7

Segura, Carlos, Carlos A. Coello Coello, Gara Miranda, and Coromoto León. "Using multi-objective evolutionary algorithms for single-objective optimization." 4OR 11, no. 3 (2013): 201–28. http://dx.doi.org/10.1007/s10288-013-0248-x.

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8

Strack, Rita. "Single-objective light sheet microscopy." Nature Methods 18, no. 1 (2021): 28. http://dx.doi.org/10.1038/s41592-020-01027-w.

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9

Doerr, Benjamin, and Weijie Zheng. "Theoretical Analyses of Multi-Objective Evolutionary Algorithms on Multi-Modal Objectives." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 14 (2021): 12293–301. http://dx.doi.org/10.1609/aaai.v35i14.17459.

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Previous theory work on multi-objective evolutionary algorithms considers mostly easy problems that are composed of unimodal objectives. This paper takes a first step towards a deeper understanding of how evolutionary algorithms solve multi-modal multi-objective problems. We propose the OneJumpZeroJump problem, a bi-objective problem whose single objectives are isomorphic to the classic jump functions benchmark. We prove that the simple evolutionary multi-objective optimizer (SEMO) cannot compute the full Pareto front. In contrast, for all problem sizes n and all jump sizes k in [4..n/2-1], th
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10

Mladineo, Marko, Ivica Veža, and Nikola Gjeldum. "Single-objective and multi-objective optimization using the HUMANT algorithm." Croatian Operational Research Review 6, no. 2 (2015): 459–73. http://dx.doi.org/10.17535/crorr.2015.0035.

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11

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 (2020): 529–38. http://dx.doi.org/10.5373/jardcs/v12sp7/20202136.

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12

Ahmed, Bestoun S. "Generating Pairwise Combinatorial Interaction Test Suites Using Single Objective Dragonfly Optimisation Algorithm." Journal of Zankoy Sulaimani - Part A 19, no. 1 (2016): 69–78. http://dx.doi.org/10.17656/jzs.10586.

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13

Yilmaz, Zuleyha, and Fatih Basciftci. "Binary Artificial Bee Colony Algorithm to Solve Single Objective Resource Allocation Problem." International Journal of Future Computer and Communication 7, no. 1 (2018): 21–25. http://dx.doi.org/10.18178/ijfcc.2018.7.1.514.

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14

Kotb, R., A. M. Ewais, and A. M. Hemeida. "Single and Multi-Objective Optimization Algorithms." International Journal of Applied Energy Systems 1, no. 2 (2019): 77–84. http://dx.doi.org/10.21608/ijaes.2019.169954.

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15

Mehlhorn, Sandy, Michael Racer, Stephanie Ivey, and Martin Lipinski. "A Single-Objective Recovery Phase Model." International Journal of Information Technology Project Management 2, no. 3 (2011): 53–71. http://dx.doi.org/10.4018/jitpm.2011070104.

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The Federal Emergency Management Agency (FEMA) has identified the four phases of disaster related planning as mitigation, preparation, response, and recovery. The recovery phase is characterized by activity to return life to normal or improved levels. Very little research considers the recovery phase, which encompasses restoring services and rebuilding disaster stricken areas of the highway transportation network. Existing recovery phase models deal primarily with travel times and do not focus on specific routes for reconstruction. This research proposes a plan for repair and restoration of br
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16

Grosan, Crina, and Mihai Oltean. "Adaptive representation for single objective optimization." Soft Computing 9, no. 8 (2004): 594–605. http://dx.doi.org/10.1007/s00500-004-0402-7.

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17

Dara A. Hassan, Nezam M. Amiri, and Ayad M. Ramadan. "Solving Three Objectives Single-Machine Scheduling Problem Using Fuzzy Multi-Objective Linear Programming." Tikrit Journal of Pure Science 27, no. 6 (2023): 82–87. http://dx.doi.org/10.25130/tjps.v27i6.763.

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In this paper, three criteria scheduling problem of n jobs on a single machine is considered. Each of these n jobs is to be processed without interruption and becomes available for processing at time zero. The problem is to minimize three objectives simultaneously, which are the completion time, maximum tardiness, and maximum earliness. Here, we develop a fuzzy multi-objective linear programming (FMOLP) model for solving multi-objective scheduling problem in a fuzzy environment by using piecewise linear membership function (PLMF). A numerical example demonstrates the feasibility of applying th
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18

Kaya, Ersin, Barış Koçer, and Ahmet Arslan. "A single-objective genetic-fuzzy approach for multi-objective fuzzy problems." Journal of Intelligent & Fuzzy Systems 25, no. 3 (2013): 557–66. http://dx.doi.org/10.3233/ifs-120661.

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19

Zhang, Haotian, Xiaohong Guan, Yixin Wang, and Nan Nan. "Multi-objective optimization-assisted single-objective differential evolution by reinforcement learning." Swarm and Evolutionary Computation 94 (April 2025): 101866. https://doi.org/10.1016/j.swevo.2025.101866.

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20

Pal, Monalisa, and Sanghamitra Bandyopadhyay. "ESOEA: Ensemble of single objective evolutionary algorithms for many-objective optimization." Swarm and Evolutionary Computation 50 (November 2019): 100511. http://dx.doi.org/10.1016/j.swevo.2019.03.006.

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21

Chen, Peng, Jing Liang, Kangjia Qiao, et al. "A single-objective Sequential Search Assistance-based Multi-Objective Algorithm Framework." Swarm and Evolutionary Computation 95 (June 2025): 101916. https://doi.org/10.1016/j.swevo.2025.101916.

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22

Zagato, Elisa, Toon Brans, Steven Verstuyft, et al. "Microfabricated devices for single objective single plane illumination microscopy (SoSPIM)." Optics Express 25, no. 3 (2017): 1732. http://dx.doi.org/10.1364/oe.25.001732.

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23

Liu, Qi, Jiahao Liu, and Dunhu Liu. "Intelligent Multi-Objective Public Charging Station Location with Sustainable Objectives." Sustainability 10, no. 10 (2018): 3760. http://dx.doi.org/10.3390/su10103760.

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This paper investigates a multi-objective charging station location model with the consideration of the triple bottom line principle for green and sustainable development from economic, environmental and social perspectives. An intelligent multi-objective optimization approach is developed to handle this problem by integrating an improved multi-objective particle swarm optimization (MOPSO) process and an entropy weight method-based evaluation process. The MOPSO process is utilized to obtain a set of Pareto optimal solutions, and the entropy weight method-based evaluation process is utilized to
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24

Mirjalili, Seyed Mohammad, Behnaz Merikhi, Seyedeh Zahra Mirjalili, Milad Zoghi, and Seyedali Mirjalili. "Multi-objective versus single-objective optimization frameworks for designing photonic crystal filters." Applied Optics 56, no. 34 (2017): 9444. http://dx.doi.org/10.1364/ao.56.009444.

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25

Chaiyaratana, Nachol, Theera Piroonratana, and Nuntapon Sangkawelert. "Effects of diversity control in single-objective and multi-objective genetic algorithms." Journal of Heuristics 13, no. 1 (2006): 1–34. http://dx.doi.org/10.1007/s10732-006-9003-1.

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26

Segura, Carlos, Carlos A. Coello Coello, Gara Miranda, and Coromoto León. "Using multi-objective evolutionary algorithms for single-objective constrained and unconstrained optimization." Annals of Operations Research 240, no. 1 (2015): 217–50. http://dx.doi.org/10.1007/s10479-015-2017-z.

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27

Jovanović, Aleksandar, and Dušan Teodorović. "Multi-objective optimization of a single intersection." Transportation Planning and Technology 44, no. 2 (2021): 139–59. http://dx.doi.org/10.1080/03081060.2020.1868083.

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28

Xu, Tienan, Hanqi Lin, Yean J. Lim, Philip R. Nicovich, Katharina Gaus, and Woei Ming Lee. "Computational single-objective scanning light sheet (cSOLS)." APL Photonics 7, no. 8 (2022): 081302. http://dx.doi.org/10.1063/5.0091615.

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Single-objective scanning light sheet (SOLS) imaging has fueled major advances in volumetric bioimaging because it supports low phototoxic, high-resolution imaging over an extended period. The remote imaging unit in the SOLS does not use a conventional epifluorescence image detection scheme (a single tube lens). In this paper, we propose a technique called the computational SOLS (cSOLS) that achieves light sheet imaging without the remote imaging unit. Using a single microlens array after the tube lens (lightfield imaging), the cSOLS is immediately compatible with conventional epifluorescence
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29

OTA, Sadao. "Light Sheet Microscopy Using Single Objective Lens." Seibutsu Butsuri 57, no. 4 (2017): 200–201. http://dx.doi.org/10.2142/biophys.57.200.

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30

Zolpakar, Nor Atikah, Normah Mohd-Ghazali, and Robiah Ahmad. "Single-Objective Optimization of a Thermoacoustic Refrigerator." Applied Mechanics and Materials 819 (January 2016): 88–93. http://dx.doi.org/10.4028/www.scientific.net/amm.819.88.

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Optimization of energy-related systems with by-products that involve environmental degradation has never been so crucial today with depleting resources and global concerns over negative impacts on our environment. This paper reports the results of an optimization scheme on the coefficient of performance (COP) of a standing wave thermoacoustic refrigerator based on genetic algorithm. The environmentally friendly refrigerator operates without any CFCs, which has been associated with the depletion of ozone, a substance that prevents uv light from reaching the earth’s atmosphere. A single-objectiv
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31

Ono, Keiko, Masahito Kumano, and Masahiro Kimura. "Genetic Programming for Cooperative Single-Objective Optimization." Transactions of the Institute of Systems, Control and Information Engineers 28, no. 5 (2015): 173–80. http://dx.doi.org/10.5687/iscie.28.173.

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32

Le Moal, Eric, Emeric Mudry, Patrick C. Chaumet, Patrick Ferrand, and Anne Sentenac. "Isotropic single-objective microscopy: theory and experiment." Journal of the Optical Society of America A 28, no. 8 (2011): 1586. http://dx.doi.org/10.1364/josaa.28.001586.

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33

Le Moal, Eric, Emeric Mudry, Patrick C. Chaumet, Patrick Ferrand, and Anne Sentenac. "Two-photon fluorescence isotropic-single-objective microscopy." Optics Letters 37, no. 1 (2011): 85. http://dx.doi.org/10.1364/ol.37.000085.

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34

K.C.UDAIYAKUMAR, K. C. UDAIYAKUMAR, and M. CHANDRASEKARAN M.CHANDRASEKARAN. "Tabu Search Algorithm For Solving of Job Shop Scheduling with Single Objective - Makespan Minimization." Indian Journal of Applied Research 3, no. 12 (2011): 365–67. http://dx.doi.org/10.15373/2249555x/dec2013/111.

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35

Yaman, Sibel, and Chin-Hui Lee. "A Comparison of Single- and Multi-Objective Programming Approaches to Problems with Multiple Design Objectives." Journal of Signal Processing Systems 61, no. 1 (2008): 39–50. http://dx.doi.org/10.1007/s11265-008-0295-2.

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36

Pies, Ingo, Philipp Schreck, and Karl Homann. "Reconciling Single-Objective and Multi-Objective Theories of the Firm: A Constitutional Perspective." Academy of Management Proceedings 2018, no. 1 (2018): 15016. http://dx.doi.org/10.5465/ambpp.2018.15016abstract.

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37

Xiang, Xiaoshu, Ye Tian, Ran Cheng, Xingyi Zhang, Shengxiang Yang, and Yaochu Jin. "A benchmark generator for online dynamic single-objective and multi-objective optimization problems." Information Sciences 613 (October 2022): 591–608. http://dx.doi.org/10.1016/j.ins.2022.09.049.

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38

Kamali, Bahareh, S. Jamshid Mousavi, and K. C. Abbaspour. "Automatic calibration of HEC-HMS using single-objective and multi-objective PSO algorithms." Hydrological Processes 27, no. 26 (2012): 4028–42. http://dx.doi.org/10.1002/hyp.9510.

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39

Liu, Haitao, Wei Du, and Zhaoxia Guo. "A multi-population evolutionary algorithm with single-objective guide for many-objective optimization." Information Sciences 503 (November 2019): 39–60. http://dx.doi.org/10.1016/j.ins.2019.06.051.

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40

Haller, George, Nikolas Aksamit, and Alex P. Encinas-Bartos. "Quasi-objective coherent structure diagnostics from single trajectories." Chaos: An Interdisciplinary Journal of Nonlinear Science 31, no. 4 (2021): 043131. http://dx.doi.org/10.1063/5.0044151.

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41

Dalton, Chris J. "Objective Assessment of Single and Concatenated Compression Codecs." SMPTE Journal 105, no. 11 (1996): 698–701. http://dx.doi.org/10.5594/j06442.

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42

Bossek, Jakob. "smoof: Single- and Multi-Objective Optimization Test Functions." R Journal 9, no. 1 (2017): 103. http://dx.doi.org/10.32614/rj-2017-004.

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43

Zeng, Sanyou, Fei Zhao, and Qing Zhang. "Dynamic HypE for solving single objective optimisation problems." International Journal of Innovative Computing and Applications 10, no. 1 (2019): 51. http://dx.doi.org/10.1504/ijica.2019.10022308.

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44

Zhang, Qing, Fei Zhao, and Sanyou Zeng. "Dynamic HypE for solving single objective optimisation problems." International Journal of Innovative Computing and Applications 10, no. 1 (2019): 51. http://dx.doi.org/10.1504/ijica.2019.100548.

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45

Sinha, Ankur, Pekka Malo, and Kalyanmoy Deb. "Test Problem Construction for Single-Objective Bilevel Optimization." Evolutionary Computation 22, no. 3 (2014): 439–77. http://dx.doi.org/10.1162/evco_a_00116.

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In this paper, we propose a procedure for designing controlled test problems for single-objective bilevel optimization. The construction procedure is flexible and allows its user to control the different complexities that are to be included in the test problems independently of each other. In addition to properties that control the difficulty in convergence, the procedure also allows the user to introduce difficulties caused by interaction of the two levels. As a companion to the test problem construction framework, the paper presents a standard test suite of 12 problems, which includes eight
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46

Chen, Y. L. "Optimal Multi-Objective Single-Tuned Harmonic Filter Planning." IEEE Transactions on Power Delivery 20, no. 2 (2005): 1191–97. http://dx.doi.org/10.1109/tpwrd.2002.844282.

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47

Itonaga, Makoto, Fumihiko Ito, Ryo Saito, Toshiya Saito, Takuji Ohira, and Yoshinari Yokochi. "Single Objective Lens with Numerical Aperture of 0.95." Japanese Journal of Applied Physics 44, no. 5B (2005): 3420–22. http://dx.doi.org/10.1143/jjap.44.3420.

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48

Degirmenci, Beyza Unalan, and Dicle Altindal. "Objective Esthetic Evaluation of Implant-Supported Single Crowns." International Dental Journal 74 (October 2024): S164. http://dx.doi.org/10.1016/j.identj.2024.07.1077.

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49

Perez-Gonzalez, Paz, and Jose M. Framinan. "Single machine interfering jobs problem with flowtime objective." Journal of Intelligent Manufacturing 29, no. 5 (2015): 953–72. http://dx.doi.org/10.1007/s10845-015-1141-6.

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50

Reis Jesus, Luiz Henrique, and Leonardo Cunha Brito. "Interactive Evolution Strategies for Minimizing Single-objective Functions." IEEE Latin America Transactions 15, no. 5 (2017): 981–87. http://dx.doi.org/10.1109/tla.2017.7912596.

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