1 |
E. Mezura-Montes and C. A. C. Coello, "A simple multimembered evolution strategy to solve constrained optimization problems," IEEE Transactions on Evolutionary Computation, vol. 9, no. 1, pp. 1-17, Feb., 2005.
DOI
|
2 |
G. Reynoso-Meza, X. Blasco and et al, "Multiobjective optimization algorithm for solving constrained single objective problems," in Proc. of IEEE Conf. on Evolutionary Computation, pp. 1-7, Jul. 18-23, 2010.
|
3 |
E. Mezura-Montes and C. A. C. Coello, "Constraint-handling in nature-inspired numerical optimization: Past, present and future," Swarm and Evolutionary Computation, vol. 1, no. 4, pp. 173-194, Dec., 2011.
DOI
|
4 |
R. Landa and G. Toscano-Pulido, "Goal-constraint: Incorporating preferences through an evolutionary-constraint based method," in Proc. of IEEE Conf. on Evolutionary Computation, pp. 741-747, Jun. 20-23, 2013.
|
5 |
H. Y. Meng, X. H. Zhang and et al,, "Differential evolution based on double populations for constrained multi-objective optimization problem," Chinese Journal of Computers, vol. 31, no. 2, pp. 228-235, Feb., 2008.
DOI
|
6 |
Y. G. Woldesenbet, G. G. Yen and et al, "Constraint handling in multiobjective evolutionary optimization," IEEE Transactions on Evolutionary Computation, vol. 13, no. 3, pp. 514-525, Jun., 2009.
DOI
|
7 |
S. Cheng, H. Zhan and et al, "An innovative hybrid multi-objective particle swarm optimization with or without constraints handling," Applied Soft Computing, vol. 47, pp. 370-388, Oct., 2016.
DOI
|
8 |
Q. Long, "A constraint handling technique for constrained multi-objective genetic algorithm," Swarm and Evolutionary Computation, vol. 15, no. 4, pp. 66-79, Apr., 2014.
DOI
|
9 |
C. Peng and Q. Hui, "Epsilon-constrained CCPSO with different improvement detection techniques for large-scale constrained optimization," in Proc. of 49th Hawaii Int. Conf. on System Sciences, pp. 1711-1718, Jan. 5-8, 2016.
|
10 |
Y. Wang, Z. X. Cai and et al, "Constrained optimization evolutionary algorithms," Journal of Software, vol. 20, no 1, pp. 11-29, Jan., 2009.
DOI
|
11 |
B. Xue, M. Zhang and et al, "A survey on evolutionary computation approaches to feature selection," IEEE Transactions on Evolutionary Computation, vol. 20, no. 4, pp. 606-626, Aug., 2016.
DOI
|
12 |
H. Gao, S. Zhang and et al, "Relay selection scheme based on quantum differential evolution algorithm in relay networks," KSII Transactions on Internet & Information Systems, vol. 11, no. 7, pp. 3501-3523, Jul., 2017.
DOI
|
13 |
Y. Kumar and G. Sahoo, "A two-step artificial bee colony algorithm for clustering," Neural Computing and Applications, vol. 28, no. 3, pp. 537-551, Mar., 2017.
DOI
|
14 |
J. Yang and J. Xie, "An improved quantum-behaved particle swarm optimization algorithm," Applied Intelligence, vol. 40, no. 3, pp. 479-496, Nov., 2014.
DOI
|
15 |
M. Daneshyari and G. G. Yen, "Constrained multiple-swarm particle swarm optimization within a cultural framework," IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans, vol. 42, no. 2, pp. 475-490, Mar., 2012.
DOI
|
16 |
Q. Ji, S. Zhang and et al, "Quantum bee colony optimization and non-dominated sorting quantum bee colony optimization based multi-relay selection scheme," KSII Transactions on Internet & Information Systems, vol. 11, no. 9, pp. 4357-4378, Sep., 2017.
DOI
|
17 |
S. Mirjalili and A. Hatamlou, "Multi-verse optimizer: a nature-inspired algorithm for global optimization," Neural Computing and Applications, vol. 27, no. 2, pp. 495-513, Feb., 2017.
|
18 |
M. Zhang, W. Jiang and et al, "A hybrid biogeography-based optimization and fuzzy C -means algorithm for image segmentation," Soft Computing, vol. 23, no. 6, pp. 2033-2046, Mar., 2019.
DOI
|
19 |
D. Chen, F. Zou and et al, "A teaching-learning-based optimization algorithm with producer scrounger model for global optimization," Soft Computing, vol. 19, no. 3, pp. 745-762,Mar., 2015.
DOI
|
20 |
K. Deb and H. Jain, "An evolutionary many-objective optimization algorithm using reference point based Nondominated Sorting Approach, Part I: solving problems with box constraints," IEEE Transactions on Evolutionary Computation, vol. 18, no. 4, pp. 577-601, Aug., 2014.
DOI
|
21 |
B. Chen, Y. Lin and et al, "Modified differential evolution algorithm using a new diversity maintenance strategy for multi-objective optimization problems," Applied Intelligence, vol. 43, no. 1, pp. 49-73, Jul., 2015.
DOI
|
22 |
H. Jain and K. Deb, "An evolutionary manyobjective optimization algorithm using reference-point based nondominated sorting approach, Part II: Handling constraints and extending to an adaptive approach," IEEE Transactions on Evolutionary Computation, vol. 18, no. 4, pp. 602-622, Aug., 2014.
DOI
|
23 |
W. Long,W. Z. Zhang and et al, "A hybrid cuckoo search algorithm with feasibility-based rule for constrained structural optimization," Journal of Central South University, vo. 21, no. 8, pp. 3197-3204, Aug., 2014.
DOI
|
24 |
A. Ponsich and A. L. Jaimes, "A Survey on multiobjective evolutionary algorithms for the solution of the portfolio optimization problem and other finance and economics applications," IEEE Transactions on Evolutionary Computation, vo. 17, no. 3, pp. 321-344, Jun., 2013.
DOI
|
25 |
K. Klamroth, R. Lacour and et al, "On the representation of the search region in multi-objective optimization," European Journal of Operational Research, vol. 245. no. 3, pp. 767-778, Sep., 2015.
DOI
|
26 |
L. Ke, Q. Zhang and et al, "Hybridization of decomposition and local search for multi-objective optimization," IEEE Transactions on Cybernetics, vol. 44, no. 10, pp. 1808-1820, Oct., 2014.
DOI
|
27 |
Y. Yuan, H. Xu and et al, "A new dominance relation-based evolutionary algorithm for many-objective optimization," IEEE Transactions on Evolutionary Computation, vol. 20, no. 1, pp. 16-37, Feb., 2016.
DOI
|
28 |
Q. Zhang and H. Li, "MOEA/D: A multi-objective evolutionary algorithm based on decomposition," IEEE Transactions on Evolutionary Computation, vol. 11, no. 6, pp. 712-731, Dec., 2007.
DOI
|
29 |
Q. Jiang, L. Wang and et al, "MOEA/D-ARA+SBX: A new multi-objective evolutionary algorithm based on decomposition with artificial raindrop algorithm and simulated binary crossover," Knowledge-Based Systems, vol. 107, no. 9, pp. 197-218, Sep., 2016.
DOI
|
30 |
Z. Wang, Q. Zhang and et al, "Adaptive replacement strategies for MOEA/D," IEEE Transactions on Cybernetics, vol. 46. no. 2, pp. 474-486, Feb., 2016.
DOI
|
31 |
C. Sah, S. Das and et al, "A fuzzy rule-based penalty function approach for constrained evolutionary optimization," IEEE Transactions on Cybernetics, vol. 46, no. 12, pp. 29-53, Dec., 2016.
|
32 |
J. Liu, K. L. Teo and et al, "An exact penalty function-based differential search algorithm for constrained global optimization," Soft Computing, vol. 20, no. 4, pp. 1305-1313, Apr., 2016.
DOI
|
33 |
S. Das and P. N. Suganthan, "Differential evolution: a survey of the state-of-the-art," IEEE Transactions on Evolutionary Computation, vol. 15, no. 1, pp. 4-31, Feb., 2011.
DOI
|
34 |
W. F. Gao, G. G. Yen and et al,, "A dual-population differential evolution with coevolution for constrained optimization," IEEE Transactions on Cybernetics, vol. 45, no. 5, pp. 1108-1121, May, 2015.
|
35 |
V. Melo and G. Iacca, "A modified covariance matrix adaptation evolution strategy with adaptive penalty function and restart for constrained optimization," Expert Systems with Applications, vol. 41, no. 16, pp. 7077-7094, Nov., 2014.
DOI
|
36 |
T. P. Runarsson and X. Yao, "Stochastic ranking for constrained evolutionary optimization," IEEE Transactions on Evolutionary Computation, vol. 4, no. 3, pp. 284-294, Sep., 2000.
DOI
|
37 |
T. P. Runarsson and X. Yao, "Search biases in constrained evolutionary optimization," IEEE Transactions on Systems Man & Cybernetics Part C, vol. 35, no. 2, pp. 233-243, May, 2005.
DOI
|
38 |
G. Toscano, R. Landa and et al, "On the use of stochastic ranking for parent selection in differential evolution for constrained optimization," Soft Computing, vol. 21, no. 16, pp. 4617-4633, Aug., 2017.
DOI
|
39 |
M. Asafuddoula, T. Ray and et al, "An adaptive constraint handling approach embedded MOEA/D," in Proc. of IEEE Conf. on Evolutionary Computation, pp. 1-8, Jun. 10-15, 2012.
|
40 |
M. A. Jan and R. A. Khanum, "A study of two penalty-parameterless constraint handling techniques in the framework of MOEA/D," Applied Soft Computing, vol. 13, no. 1, pp. 128-148, Jan., 2013.
DOI
|
41 |
Y. Y. Tan, Y. C. Jiao and et al, "A modification to MOEA/D-DE for multiobjective optimization problems with complicated Pareto sets," Information Sciences, vol. 213, no. 23, pp. 14-38, Dec., 2012.
DOI
|
42 |
K. Deb, A. Pratap and et al, "A fast and elitist multiobjective genetic algorithm: NSGA-II," IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182-197, Apr., 2002.
DOI
|
43 |
K. Deb, A. Pratap and et al, "Constrained test problems for multi-objective evolutionary optimization," in Proc. of the First Int. Conf. Evolutionary Multi-Criterion Optimization, pp. 284-298, Mar. 7-9, 2001.
|
44 |
Q. Lin , J. Li and et al, "A novel multi-objective particle swarm optimization with multiple search strategies," European Journal of Operational Research, vol. 247, no. 3, pp. 732-744, Dec., 2015.
DOI
|
45 |
L. Y. Tseng and C. Chen, "Multiple trajectory search for unconstrained/constrained multi-objective optimization," in Proc. of IEEE Conf. on Evolutionary Computation, pp. 1951-1958, May 18-21, 2009.
|
46 |
A. Vargha and H. D. Delaney, "The Kruskal-Wallis test and stochastic homogeneity," Journal of Educational & Behavioral Statistics, vol. 23, no. 2, pp. 170-192, Jun., 1998.
DOI
|
47 |
J. Bader and E. Zitzler, "HypE: an algorithm for fast hypervolume-based many-objective optimization," Evolutionary Computation, vol. 19, no. 1, pp. 45-76, Feb., 2011.
DOI
|
48 |
L. Jiao, J. Luo and et al, "A modified objective function method with feasible-guiding strategy to solve constrained multi-objective optimization problems," Applied Soft Computing, vol. 14, no. 1, pp. 363-380, Jan., 2014.
DOI
|
49 |
X. Li and G. Du, "BSTBGA: A hybrid genetic algorithm for constrained multi-objective optimization problems," Computers & Operations Research, vol. 40, no. 1, pp. 282-302, Jan., 2013.
DOI
|
50 |
H. L. Liu and X. Li, "The multiobjective evolutionary algorithm based on determined weight and sub-regional search," in Proc. of IEEE Conf. on Evolutionary Computation, pp. 1928-1934, May 18-21, 2009.
|