Platemo: a matlab platform for evolutionary multi-objective optimization [educational forum]
Abstract
Over the last three decades, a large number of evolutionary algorithms have been developed for solving multi-objective optimization problems. However, there lacks an upto-date and comprehensive software platform for researchers to properly benchmark existing algorithms and for practitioners to apply selected algorithms to solve their real-world problems. The demand of such a common tool becomes even more urgent, when the source code of many proposed algorithms has not been made publicly available. To address these issues, we have developed a MATLAB platform for evolutionary multi-objective optimization in this paper, called PlatEMO, which includes more than 50 multiobjective evolutionary algorithms and more than 100 multi-objective test problems, along with several widely used performance indicators. With a user-friendly graphical user interface, PlatEMO enables users to easily compare several evolutionary algorithms at one time and collect statistical results in Excel or LaTeX files. More importantly, PlatEMO is completely open source, such that users are able to develop new algorithms on the basis of it. This paper introduces the main features of PlatEMO and illustrates how to use it for performing comparative experiments, embedding new algorithms, creating new test problems, and developing performance indicators. Source code of PlatEMO is now available at: http://bimk.ahu.edu.cn/index.php?s=/Index/Software/index.html.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
NSGA-II significantly improves the convergence speed of the iteration, reduces the computational complexity, and ensures the diversity of the population.
“It significantly improves the convergence speed of the iteration, reduces the computational complexity, and ensures the diversity of the population [25,26].”
From Research on high efficiency and high dynamic optimal matching of the electro-hydraulic servo pump control system based on NSGA-II (2023) · cites this paper as [26]
NSGA-II introduces fast non-dominant sorting, elite retention, and congestion comparison methods.
“the non-dominant sorting genetic algorithm based on the elite strategy (NSGA-II) introduces fast non-dominant sorting, elite retention, and congestion comparison methods.”
From Research on high efficiency and high dynamic optimal matching of the electro-hydraulic servo pump control system based on NSGA-II (2023) · cites this paper as [26]
Cites
A fast and elitist multiobjective genetic algorithm: NSGA-II
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.996017 · Cited by 50,053 · see citing works
Multi-Objective Optimization Using Evolutionary Algorithms
Deb, K., & Kalyanmoy, D. (2001) · Andalas University Repository (Andalas University) · Cited by 14,936 · see citing works
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
Zhang, Q., & Li, H. (2007) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2007.892759 · Cited by 10,069 · see citing works
Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach
Zitzler, E., & Thiele, L. (1999) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.797969 · Cited by 8,857 · see citing works
Deb, K., & Jain, H. (2013) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2013.2281535 · Cited by 7,213 · see citing works
Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms
Srinivas, N., & Deb, K. (1994) · Evolutionary Computation · doi:10.1162/evco.1994.2.3.221 · Cited by 6,935 · see citing works
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Zitzler, E., Deb, K., & Thiele, L. (2000) · Evolutionary Computation · doi:10.1162/106365600568202 · Cited by 5,728 · see citing works
Evolutionary programming made faster
Yao, X., Liu, Y., & Lin, G. (1999) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.771163 · Cited by 3,924 · see citing works
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Schaffer, J. D. (1985) · international conference on Genetic algorithms · Cited by 2,656 · see citing works
Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II
Li, H., & Zhang, Q. (2008) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2008.925798 · Cited by 2,572 · see citing works
MOPSO: a proposal for multiple objective particle swarm optimization
Coello, C. A. C., & Lechuga, M. S. (2002) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2002.1004388 · Cited by 2,457 · see citing works
Indicator-Based Selection in Multiobjective Search
Zitzler, E., & Künzli, S. (2004) · Parallel Problem Solving from Nature · doi:10.1007/978-3-540-30217-9_84 · Cited by 2,456 · see citing works
A niched Pareto genetic algorithm for multiobjective optimization
Horn, J., Nafpliotis, N., & Goldberg, D. E. (2002) · doi:10.1109/icec.1994.350037 · Cited by 2,447 · see citing works
Multiobjective evolutionary algorithms: A survey of the state of the art
Zhou, A., Qu, B., Li, H., Zhao, S.-Z., Suganthan, P. N., & Zhang, Q. (2011) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2011.03.001 · Cited by 2,260 · see citing works
HypE: An Algorithm for Fast Hypervolume-Based Many-Objective Optimization
Bader, J., & Zitzler, E. (2010) · Evolutionary Computation · doi:10.1162/evco_a_00009 · Cited by 2,111 · see citing works
A review of multiobjective test problems and a scalable test problem toolkit
Huband, S., Hingston, P., Barone, L., & While, L. (2006) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2005.861417 · Cited by 2,102 · see citing works
SMS-EMOA: Multiobjective selection based on dominated hypervolume
Beume, N., Naujoks, B., & Emmerich, M. (2006) · European Journal of Operational Research · doi:10.1016/j.ejor.2006.08.008 · Cited by 2,064 · see citing works
Scalable Test Problems for Evolutionary Multiobjective Optimization
Deb, K., Thiele, L., Laumanns, M., et al. (2005) · Springer eBooks · doi:10.1007/1-84628-137-7_6 · Cited by 2,030 · see citing works
GENETIC ALGORITHMS FOR MULTI-OBJECTIVE OPTIMIZATION: FORMULATION, DISCUSSION, AND GENERALIZATION
Fonseca, C. M., & Fleming, P. J. (1993) · Cited by 1,828 · see citing works
A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization
Cheng, R., Jin, Y., Olhofer, M., & Sendhoff, B. (2016) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2016.2519378 · Cited by 1,776 · see citing works
Self-citationKnowles, J. D. (2006) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2005.851274 · Cited by 1,261 · see citing works
A combined genetic adaptive search (GeneAS) for engineering design
Deb, K., & Goyal, M. K. (1996) · Cited by 1,221 · see citing works
An Evolutionary Many-Objective Optimization Algorithm Based on Dominance and Decomposition
Li, K., Deb, K., Zhang, Q., & Kwong, S. (2014) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2014.2373386 · Cited by 1,208 · see citing works
jMetal: A Java framework for multi-objective optimization
Durillo, J. J., & Nebro, A. J. (2011) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2011.05.014 · Cited by 1,140 · see citing works
A faster algorithm for calculating hypervolume
While, L., Hingston, P., Barone, L., et al. (2006) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2005.851275 · Cited by 1,115 · see citing works
A multi-objective genetic local search algorithm and its application to flowshop scheduling
Ishibuchi, H., & Murata, T. (1998) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/5326.704576 · Cited by 1,028 · see citing works
SPEA2: Improving the Strength Pareto Evolutionary Algorithm For Multiobjective Optimization
Zitzler, E., Giannakoglou, K., Tsahalis, D., et al. (2002) · Cited by 1,020 · see citing works
Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization.
Schott, J. R. (1995) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 1,016 · see citing works
PESA-II: region-based selection in evolutionary multiobjective optimization
Corne, D., Jerram, N. R., Knowles, J. D., & Oates, M. J. (2001) · Cited by 1,011 · see citing works
Multiobjective optimization Test Instances for the CEC 2009 Special Session and Competition
Zhang, Q., Zhou, A., Zhao, S.-Z., et al. (2009) · Cited by 951 · see citing works
A Grid-Based Evolutionary Algorithm for Many-Objective Optimization
Yang, S., Li, M., Liu, X., et al. (2013) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2012.2227145 · Cited by 898 · see citing works
A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimization
Zhang, X., Tian, Y., & Jin, Y. (2014) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2014.2378512 · Cited by 873 · see citing works
Self-citationRM-MEDA: A Regularity Model-Based Multiobjective Estimation of Distribution Algorithm
Zhang, Q., Zhou, A., & Jin, Y. (2008) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2007.894202 · Cited by 831 · see citing works
Self-citationLiu, H., Gu, F., & Zhang, Q. (2013) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2013.2281533 · Cited by 829 · see citing works
Covariance Matrix Adaptation for Multi-objective Optimization
Igel, C., Hansen, N., & Roth, S. (2007) · Evolutionary Computation · doi:10.1162/evco.2007.15.1.1 · Cited by 792 · see citing works
A New Dominance Relation-Based Evolutionary Algorithm for Many-Objective Optimization
Yuan, Y., Xu, H., Wang, B., et al. (2015) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2015.2420112 · Cited by 752 · see citing works
Shift-Based Density Estimation for Pareto-Based Algorithms in Many-Objective Optimization
Li, M., Yang, S., & Liu, X. (2013) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2013.2262178 · Cited by 722 · see citing works
An Evolutionary Approach to Multiobjective Clustering
Handl, J., & Knowles, J. D. (2007) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2006.877146 · Cited by 718 · see citing works
Zhang, X., Tian, Y., Cheng, R., & Jin, Y. (2016) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2016.2600642 · Cited by 666 · see citing works
Self-citationSMPSO: A new PSO-based metaheuristic for multi-objective optimization
Nebro, A. J., Durillo, J. J., García-Nieto, J., et al. (2009) · IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM) · doi:10.1109/mcdm.2009.4938830 · Cited by 653 · see citing works
GDE3: The third Evolution Step of Generalized Differential Evolution
Kukkonen, S., & Lampinen, J. (2005) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2005.1554717 · Cited by 649 · see citing works
Applying adaptive algorithms to epistatic domains
Davis, L. D. (1985) · International Joint Conference on Artificial Intelligence · Cited by 642 · see citing works
Chugh, T., Jin, Y., Miettinen, K., et al. (2016) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2016.2622301 · Cited by 636 · see citing works
Self-citationPerformance of Decomposition-Based Many-Objective Algorithms Strongly Depends on Pareto Front Shapes
Ishibuchi, H., Setoguchi, Y., Masuda, H., et al. (2016) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2016.2587749 · Cited by 602 · see citing works
Two_Arch2: An Improved Two-Archive Algorithm for Many-Objective Optimization
Wang, H., Jiao, L., & Yao, X. (2014) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2014.2350987 · Cited by 563 · see citing works
An Efficient Approach to Nondominated Sorting for Evolutionary Multiobjective Optimization
Zhang, X., Tian, Y., Cheng, R., et al. (2014) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2014.2308305 · Cited by 534 · see citing works
Self-citationMa, X., Liu, F., Qi, Y., et al. (2015) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2015.2455812 · Cited by 525 · see citing works
PISA — A Platform and Programming Language Independent Interface for Search Algorithms
Stefan Bleuler, A., Laumanns, M., Thiele, L., & Zitzler, E. (2003) · Lecture notes in computer science · doi:10.1007/3-540-36970-8_35 · Cited by 462 · see citing works
Test Problems for Large-Scale Multiobjective and Many-Objective Optimization
Cheng, R., Jin, Y., Olhofer, M., et al. (2016) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2016.2600577 · Cited by 430 · see citing works
Self-citationA Survey of Multiobjective Evolutionary Algorithms for Data Mining: Part I
Mukhopadhyay, A., Maulik, U., Bandyopadhyay, S., & Coello, C. A. C. (2013) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2013.2290086 · Cited by 423 · see citing works
A Decomposition-Based Evolutionary Algorithm for Many Objective Optimization
Asafuddoula, M., Ray, T., & Sarker, R. A. (2014) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2014.2339823 · Cited by 409 · see citing works
Preference-Inspired Coevolutionary Algorithms for Many-Objective Optimization
Wang, R., Purshouse, R. C., & Fleming, P. J. (2012) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2012.2204264 · Cited by 407 · see citing works
Balancing Convergence and Diversity in Decomposition-Based Many-Objective Optimizers
Yuan, Y., Xu, H., Wang, B., et al. (2015) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2015.2443001 · Cited by 400 · see citing works
A Multiobjective Evolutionary Algorithm Using Gaussian Process-Based Inverse Modeling
Cheng, R., Jin, Y., Narukawa, K., et al. (2015) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2015.2395073 · Cited by 377 · see citing works
Self-citationJiang, S., & Yang, S. (2017) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2016.2592479 · Cited by 376 · see citing works
Ponsich, A., Jáimes, A. L., & Coello, C. A. C. (2012) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2012.2196800 · Cited by 367 · see citing works
M-PAES: a memetic algorithm for multiobjective optimization
Knowles, J. D., & Corne, D. (2002) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2000.870313 · Cited by 361 · see citing works
Pareto or Non-Pareto: Bi-Criterion Evolution in Multiobjective Optimization
Li, M., Yang, S., & Liu, X. (2015) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2015.2504730 · Cited by 318 · see citing works
Zhou, A., Jin, Y., Zhang, Q., et al. (2006) · doi:10.1109/cec.2006.1688406 · Cited by 308 · see citing works
Self-citationWang, Y., Wu, L., & Yuan, X. (2009) · Soft Computing · doi:10.1007/s00500-008-0394-9 · Cited by 306 · see citing works
The r-Dominance: A New Dominance Relation for Interactive Evolutionary Multicriteria Decision Making
Saïd, L. B., Bechikh, S., & Ghédira, K. (2010) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2010.2041060 · Cited by 302 · see citing works
Behavior of Multiobjective Evolutionary Algorithms on Many-Objective Knapsack Problems
Ishibuchi, H., Akedo, N., & Nojima, Y. (2014) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2014.2315442 · Cited by 293 · see citing works
An evolutionary approach to the traveling salesman problem
Fogel, D. B. (1988) · Biological Cybernetics · doi:10.1007/bf00202901 · Cited by 290 · see citing works
Techniques for highly multiobjective optimisation: some nondominated points are better than others
Corne, D. W., & Knowles, J. D. (2007) · Proceedings of the 9th annual conference on Genetic and evolutionary computation · doi:10.1145/1276958.1277115 · Cited by 273 · see citing works
Diversity Assessment in Many-Objective Optimization
Wang, H., Jin, Y., & Yao, X. (2016) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2016.2550502 · Cited by 273 · see citing works
Self-citationSubstitute Distance Assignments in NSGA-II for Handling Many-objective Optimization Problems
Köppen, M., & Yoshida, K. (2007) · Springer eBooks · doi:10.1007/978-3-540-70928-2_55 · Cited by 264 · see citing works
Price, K. V., Storn, R., & Lampinen, J. (2005) · Springer eBooks · doi:10.1007/3-540-31306-0 · Cited by 259 · see citing works
g-dominance: Reference point based dominance for multiobjective metaheuristics
Molina, J., Santana, L. V., Hernández‐Díaz, A. G., et al. (2008) · European Journal of Operational Research · doi:10.1016/j.ejor.2008.07.015 · Cited by 259 · see citing works
Towards a Quick Computation of Well-Spread Pareto-Optimal Solutions
Deb, K., Mohan, M., & Mishra, S. (2003) · Lecture notes in computer science · doi:10.1007/3-540-36970-8_16 · Cited by 252 · see citing works
A genetic algorithms based multi-objective neural net applied to noisy blast furnace data
Pettersson, F., Chakraborti, N., & Saxén, H. (2005) · Applied Soft Computing · doi:10.1016/j.asoc.2005.09.001 · Cited by 252 · see citing works
The multi-objective next release problem
Zhang, Y., Harman, M., & Mansouri, S. A. (2007) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/1276958.1277179 · Cited by 250 · see citing works
A multi-objective particle swarm optimizer based on decomposition
Martinez, S. Z., & Coello, C. A. C. (2011) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/2001576.2001587 · Cited by 247 · see citing works
Bi-goal evolution for many-objective optimization problems
Li, M., Yang, S., & Liu, X. (2015) · Artificial Intelligence · doi:10.1016/j.artint.2015.06.007 · Cited by 240 · see citing works
Improved Metaheuristic Based on the R2 Indicator for Many-Objective Optimization
Gómez, R. H., & Coello, C. A. C. (2015) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/2739480.2754776 · Cited by 240 · see citing works
Ponweiser, W., Wagner, T., Biermann, D., & Vincze, M. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-87700-4_78 · Cited by 227 · see citing works
Performance Metric Ensemble for Multiobjective Evolutionary Algorithms
Yen, G. G., & He, Z. (2013) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2013.2240687 · Cited by 207 · see citing works
Lukasiewycz, M., Glaß, M., Reimann, F., & Teich, J. (2011) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/2001576.2001808 · Cited by 204 · see citing works
Specification of Genetic Search Directions in Cellular Multi-objective Genetic Algorithms
Murata, T., Ishibuchi, H., & Gen, M. (2001) · Lecture notes in computer science · doi:10.1007/3-540-44719-9_6 · Cited by 195 · see citing works
Biased Multiobjective Optimization and Decomposition Algorithm
Li, H., Zhang, Q., & Deng, J. (2016) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2015.2507366 · Cited by 191 · see citing works
A New Local Search-Based Multiobjective Optimization Algorithm
Chen, B., Zeng, W., Lin, Y., & Zhang, D. (2014) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2014.2301794 · Cited by 174 · see citing works
MSOPS-II: A general-purpose Many-Objective optimiser
Hughes, E. J. (2007) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2007.4424985 · Cited by 157 · see citing works
Many-Objective Evolutionary Algorithm: Objective Space Reduction and Diversity Improvement
He, Z., & Yen, G. G. (2015) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2015.2433266 · Cited by 156 · see citing works
Instance Generators and Test Suites for the Multiobjective Quadratic Assignment Problem
Knowles, J. D., & Corne, D. (2003) · Lecture notes in computer science · doi:10.1007/3-540-36970-8_21 · Cited by 152 · see citing works
Many-objective evolutionary optimization based on reference points
Liu, Y., Gong, D., Sun, X., et al. (2016) · Applied Soft Computing · doi:10.1016/j.asoc.2016.11.009 · Cited by 138 · see citing works
A multi-objective evolutionary algorithm based on an enhanced inverted generational distance metric
Tian, Y., Zhang, X., Cheng, R., et al. (2016) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2016.7748352 · Cited by 136 · see citing works
Self-citationA Many-Objective Evolutionary Algorithm With Enhanced Mating and Environmental Selections
Cheng, J., Yen, G. G., & Zhang, G. (2015) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2015.2424921 · Cited by 135 · see citing works
On Test Functions for Evolutionary Multi-objective Optimization
Okabe, T., Jin, Y., Olhofer, M., et al. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30217-9_80 · Cited by 115 · see citing works
Self-citationAutomatic Component-Wise Design of Multiobjective Evolutionary Algorithms
Bezerra, L. C. T., López‐Ibáñez, M., & Stützle, T. (2015) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2015.2474158 · Cited by 111 · see citing works
Pareto Fronts of Many-Objective Degenerate Test Problems
Ishibuchi, H., Masuda, H., & Nojima, Y. (2015) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2015.2505784 · Cited by 104 · see citing works
Garcı́a, J., Berlanga, A., & Molina, J. M. (2008) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2008.920677 · Cited by 90 · see citing works
Multiple trajectory search for unconstrained/constrained multi-objective optimization
Tseng, L., & Chen, C. (2009) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2009.4983179 · Cited by 83 · see citing works
ParadisEO-MOEO: A Framework for Evolutionary Multi-objective Optimization
Liefooghe, A., Basseur, M., Jourdan, L., et al. (2007) · Springer eBooks · doi:10.1007/978-3-540-70928-2_31 · Cited by 81 · see citing works
A fast approximation-guided evolutionary multi-objective algorithm
Wagner, M., & Neumann, F. (2013) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/2463372.2463448 · Cited by 80 · see citing works
ParadisEO-MO: from fitness landscape analysis to efficient local search algorithms
Humeau, J., Liefooghe, A., Talbi, E., et al. (2013) · Journal of Heuristics · doi:10.1007/s10732-013-9228-8 · Cited by 48 · see citing works
Yeung, S. H., Man, K. F., Luk, K., et al. (2008) · IEEE Transactions on Antennas and Propagation · doi:10.1109/tap.2007.915473 · Cited by 45 · see citing works
Lazzerini, B., Marcelloni, F., & Vecchio, M. (2009) · Applied Soft Computing · doi:10.1016/j.asoc.2009.08.024 · Cited by 38 · see citing works
Zhang, X., Tian, Y., Cheng, R., et al. (2016) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci.2016.7850210 · Cited by 15 · see citing works
Self-citationBrockhoff, D. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-15934-8_13 · Cited by 12 · see citing works
A hybrid development platform for evolutionary multi-objective optimization
Shen, R., Zheng, J., & Li, M. (2015) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2015.7257116 · Cited by 12 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
MOEA based memetic algorithms for multi-objective satellite range scheduling problem
Du, Y., Xing, L., Zhang, J., Chen, Y., & He, Y. (2019) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2019.100576 · Cited by 69 · see citing works
Yang, M., Yan, G., Zhang, Y., Zhang, T., & Ai, C. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e13805 · Cited by 19 · see citing works
“It significantly improves the convergence speed of the iteration, reduces the computational complexity, and ensures the diversity of the population [25,26].”
BackgroundMulti-Layer LEO Constellation Optimization Based on D-NSDE Algorithm
Wang, S., Zhuang, X., Wu, C., Fan, G., Li, M., Xu, T., & Zhao, X. (2025) · Remote Sensing · doi:10.3390/rs17060994 · Cited by 10 · see citing works
- Authors
- Tian, Y., Cheng, R., Zhang, X., & Jin, Y.
- Affiliations
- Anhui University (CN)
- University of Birmingham (GB)
- University of Surrey (GB)
- Venue
- IEEE Computational Intelligence Magazine
- Published
- 2017-11
- Volume
- 12
- Issue
- 4
- Pages
- 73-87
- ISSN
- 1556-603X (ISSN-L)
- OpenAlex
- W2764251381
- Cited by
- 2,777
- References
- 100
- Citations by year
- 2026: 369 · 2025: 565 · 2024: 494 · 2023: 415 · 2022: 319 · 2021: 240 · 2020: 182 · 2019: 123 · 2018: 63 · 2017: 5 · 2016: 1
- FWCI
- 75.83
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Computational Intelligence Magazine (publishedVersion)
- PUB – Publications at Bielefeld University (Bielefeld University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Advanced Multi-Objective Optimization Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Evolutionary Algorithms and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Keywords
- multi-objective optimization, multi-objective evolutionary algorithms, benchmarking, performance indicators, open-source platform, MATLAB, algorithm comparison, graphical user interface
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review