References(45)
[1]
S. Han, K. Zhu, M. Zhou, and X. Cai, Competition-driven multimodal multiobjective optimization and its application to feature selection for credit card fraud detection, IEEE Trans. Syst., Man, Cybern.: Syst., vol. 52, no. 12, pp. 7845–7857, 2022.
[2]
V. Barichard and J. K. Hao, Genetic tabu search for the Multi-Objective knapsack problem, Tsinghua Science and Technology, vol. 8, no. 1, pp. 8–13, 2003.
[3]
Y. Tian, R. Liu, X. Zhang, H. Ma, K. C. Tan, and Y. Jin, A multipopulation evolutionary algorithm for solving large-scale multimodal multiobjective optimization problems, IEEE Trans. Evol. Comput., vol. 25, no. 3, pp. 405–418, 2021.
[4]
H. Zhang, L. Ma, J. Wang, and L. Wang, Furnace-grouping problem modeling and multi-objective optimization for special aluminum, IEEE Trans. Emerg. Top. Comput. Intell., vol. 6, no. 3, pp. 544–555, 2022.
[5]
X. Ma, Y. Fu, K. Gao, L. Zhu, and A. Sadollah, A multi-objective scheduling and routing problem for home health care services via brain storm optimization, Complex System Modeling and Simulation, vol. 3, no. 1, pp. 32–46, 2023.
[6]
L. Wang, Z. Pan, and J. Wang, A review of reinforcement learning based intelligent optimization for manufacturing scheduling, Complex System Modeling and Simulation, vol. 1, no. 4, pp. 257–270, 2021.
[7]
R. Tanabe and H. Ishibuchi, A review of evolutionary multimodal multiobjective optimization, IEEE Trans. Evol. Comput., vol. 24, no. 1, pp. 193–200, 2020.
[8]
W. Gong, Z. Liao, X. Mi, L. Wang, and Y. Guo, Nonlinear equations solving with intelligent optimization algorithms: A survey, Complex System Modeling and Simulation, vol. 1, no. 1, pp. 15–32, 2021.
[9]
C. Yue, B. Qu, and J. Liang, A multiobjective particle swarm optimizer using ring topology for solving multimodal multiobjective problems, IEEE Trans. Evol. Comput., vol. 22, no. 5, pp. 805–817, 2018.
[10]
W. Li, X. Yao, T. Zhang, R. Wang, and L. Wang, Hierarchy ranking method for multimodal multiobjective optimization with local Pareto fronts, IEEE Trans. Evol. Comput., pp. vol. 27, no. 1, pp. 98–110, 2023.
[11]
J. J. Liang, C. T. Yue, and B. Y. Qu, Multimodal multi-objective optimization: A preliminary study, in Proc. 2016 IEEE Congress on Evolutionary Computation, Vancouver, Canada, 2016, pp. 2454–2461.
[12]
Q. Lin, W. Lin, Z. Zhu, M. Gong, J. Li, and C. A. C. Coello, Multimodal multiobjective evolutionary optimization with dual clustering in decision and objective spaces, IEEE Trans. Evol. Comput., vol. 25, no. 1, pp. 130–144, 2021.
[13]
Y. Liu, G. G. Yen, and D. Gong, A multimodal multiobjective evolutionary algorithm using two-archive and recombination strategies, IEEE Trans. Evol. Comput., vol. 23, no. 4, pp. 660–674, 2019.
[14]
W. Li, T. Zhang, R. Wang, and H. Ishibuchi, Weighted indicator-based evolutionary algorithm for multimodal multiobjective optimization, IEEE Trans. Evol. Comput., vol. 25, no. 6, pp. 1064–1078, 2021.
[15]
K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput., vol. 6, no. 2, pp. 182–197, 2002.
[16]
R. Tanabe and H. Ishibuchi, A framework to handle multimodal multiobjective optimization in decomposition-based evolutionary algorithms, IEEE Trans. Evol. Comput., vol. 24, no. 4, pp. 720–734, 2020.
[17]
Y. Peng and H. Ishibuchi, A diversity-enhanced subset selection framework for multimodal multiobjective optimization, IEEE Trans. Evol. Comput., vol. 26, no. 5, pp. 886–900, 2022.
[18]
Q. Fan and X. Yan, Solving multimodal multiobjective problems through zoning search, IEEE Trans. Syst., Man, Cybern.: Syst., vol. 51, no. 8, pp. 4836–4847, 2021.
[19]
G. Li, W. Wang, W. Zhang, W. You, F. Wu, and H. Tu, Handling multimodal multi-objective problems through self-organizing quantum-inspired particle swarm optimization, Inf. Sci., vol. 577, pp. 510–540, 2021.
[20]
Z. Li, J. Zou, S. Yang, and J. Zheng, A two-archive algorithm with decomposition and fitness allocation for multi-modal multi-objective optimization, Inf. Sci., vol. 574, pp. 413–430, 2021.
[21]
J. Liang, K. Qiao, C. Yue, K. Yu, B. Qu, R. Xu, Z. Li, and Y. Hu, A clustering-based differential evolution algorithm for solving multimodal multi-objective optimization problems, Swarm Evol. Comput., vol. 60, p. 100788, 2021.
[22]
B. Qu, G. Li, L. Yan, J. Liang, C. Yue, K. Yu, and O. D. Crisalle, A grid-guided particle swarm optimizer for multimodal multi-objective problems, Appl. Soft Comput., vol. 117, p. 108381, 2022.
[23]
K. Zhang, C. Shen, G. G. Yen, Z. Xu, and J. He, Two-stage double niched evolution strategy for multimodal multiobjective optimization, IEEE Trans. Evol. Comput., vol. 25, no. 4, pp. 754–768, 2021.
[24]
S. Han, K. Zhu, M. Zhou, and X. Cai, Information-utilization-method-assisted multimodal multiobjective optimization and application to credit card fraud detection, IEEE Trans. Comput. Soc. Syst., vol. 8, no. 4, pp. 856–869, 2021.
[25]
Y. Liu, H. Ishibuchi, G. G. Yen, Y. Nojima, and N. Masuyama, Handling imbalance between convergence and diversity in the decision space in evolutionary multimodal multiobjective optimization, IEEE Trans. Evol. Comput., vol. 24, no. 3, pp. 551–565, 2020.
[26]
C. Yue, P. N. Suganthan, J. Liang, B. Qu, K. Yu, Y. Zhu, and L. Yan, Differential evolution using improved crowding distance for multimodal multiobjective optimization, Swarm Evol. Comput., vol. 62, p. 100849, 2021.
[27]
Y. Tian, T. Zhang, J. Xiao, X. Zhang, and Y. Jin, A coevolutionary framework for constrained multiobjective optimization problems, IEEE Trans. Evol. Comput., vol. 25, no. 1, pp. 102–116, 2021.
[28]
J. Yuan, H. L. Liu, Y. S. Ong, and Z. He, Indicator-based evolutionary algorithm for solving constrained multiobjective optimization problems, IEEE Trans. Evol. Comput., vol. 26, no. 2, pp. 379–391, 2022.
[29]
Y. Tian, L. Si, X. Zhang, K. Tan, and Y. Jin, Local model-based Pareto front estimation for multiobjective optimization, IEEE Trans. Syst., Man, Cybern.: Syst., vol. 53, no. 1, pp. 623–634, 2023.
[30]
Y. Tian, R. Cheng, X. Zhang, and Y. Jin, PlatEMO: A MATLAB platform for evolutionary multi-objective optimization [Educational Forum], IEEE Comput. Intell. Mag., vol. 12, no. 4, pp. 73–87, 2017.
[31]
R. B. Agrawal, K. Deb, and R. B. Agrawal, Simulated binary crossover for continuous search space, Complex Syst., vol. 9, no. 3, pp. 115–148, 2000.
[32]
H. Ishibuchi, R. Imada, N. Masuyama, and Y. Nojima, Comparison of hypervolume, IGD and IGD+ from the viewpoint of optimal distributions of solutions, in Proc. 10th Int. Conf. Evolutionary Multi-Criterion Optimization, East Lansing, MI, USA, 2019, pp. 332–345.
[33]
Y. Tian, X. Xiang, X. Zhang, R. Cheng, and Y. Jin, Sampling reference points on the Pareto fronts of benchmark multi-objective optimization problems, in Proc. 2018 IEEE Congress on Evolutionary Computation, Rio de Janeiro, Brazil, 2018, pp. 1–6.
[34]
A. Zhou, Q. Zhang, and Y. Jin, Approximating the set of Pareto-optimal solutions in both the decision and objective spaces by an estimation of distribution algorithm, IEEE Trans. Evol. Comput., vol. 13, no. 5, pp. 1167–1189, 2009.
[35]
H. Ishibuchi, L. M. Pang, and K. Shang, Difficulties in fair performance comparison of multi-objective evolutionary algorithms [research frontier], IEEE Comput. Intell. Mag., vol. 17, no. 1, pp. 86–101, 2022.
[36]
J. Alcalá-Fdez, L. Sánchez, S. García, M. J. del Jesus, S. Ventura, J. M. Garrell, J. Otero, C. Romero, J. Bacardit, V. M. Rivas, et al., KEEL: A software tool to assess evolutionary algorithms for data mining problems, Soft Comput., vol. 13, no. 3, pp. 307–318, 2009.
[37]
E. Jiang, L. Wang, and J. Wang, Decomposition-based multi-objective optimization for energy-aware distributed hybrid flow shop scheduling with multiprocessor tasks, Tsinghua Science and Technology, vol. 26, no. 5, pp. 646–663, 2021.
[38]
W. Zhang, X. Chen, and J. Jiang, A multi-objective optimization method of initial virtual machine fault-tolerant placement for star topological data centers of cloud systems, Tsinghua Science and Technology, vol. 26, no. 1, pp. 95–111, 2021.
[39]
F. Zhao, X. Hu, L. Wang, and Z. Li, A memetic discrete differential evolution algorithm for the distributed permutation flow shop scheduling problem, Complex Intell. Syst., vol. 8, no. 1, pp. 141–161, 2022.
[40]
K. Gao, Y. Huang, A. Sadollah, and L. Wang, A review of energy-efficient scheduling in intelligent production systems, Complex Intell. Syst., vol. 6, no. 2, pp. 237–249, 2020.
[41]
H. Ma, H. Wei, Y. Tian, R. Cheng, and X. Zhang, A multi-stage evolutionary algorithm for multi-objective optimization with complex constraints, Inf. Sci., vol. 560, pp. 68–91, 2021.
[42]
J. Liang, X. Ban, K. Yu, B. Qu, K. Qiao, C. T. Yue, K. Chen, and K. C. Tan, A survey on evolutionary constrained multiobjective optimization, IEEE Trans. Evol. Comput., vol. 27, no. 2, pp. 201–221, 2023.
[43]
H. Ishibuchi, Y. Peng, and K. Shang, A scalable multimodal multiobjective test problem, in Proc. 2019 IEEE Congress on Evolutionary Computation, Wellington, New Zealand, 2019, pp. 310–317.
[44]
Z. Cui, J. Zhang, Y. Wang, Y. Cao, X. Cai, W. Zhang, and J. Chen, A pigeon-inspired optimization algorithm for many-objective optimization problems, Sci. China Inf. Sci., vol. 62, no. 7, p. 70212, 2019.
[45]
R. Cheng, M. Li, Y. Tian, X. Zhang, S. Yang, Y. Jin, and X. Yao, A benchmark test suite for evolutionary many-objective optimization, Complex Intell. Syst., vol. 3, no. 1, pp. 67–81, 2017.