Binary artificial algae algorithm for multidimensional knapsack problems

Applied Soft Computing Journal

Volumn 43, Issue , 2016, Pages 583-595

  Zhang, Xuedong ^a   Wu, Changzhi ^b   Li, Jing ^c   Wang, Xiangyu ^b,d   Yang, Zhijing ^e   Lee, Jae Myung ^f   Jung, Kwang Hyo ^f  

^a ANHUI UNIVERSITY OF FINANCE AND ECONOMICS   (China)

^b CURTIN UNIVERSITY   (Australia)

^c Department of Computer Science   (China)

^d Kyung Hee University   (South Korea)

^e GUANGDONG UNIVERSITY OF TECHNOLOGY   (China)

^f Pusan National University   (South Korea)

Author keywords

Artificial algae algorithm; Elite local search; Multidimensional knapsack problem; Pseudo utility ratio

Indexed keywords

ALGAE; ALGORITHMS; BINS; COMBINATORIAL OPTIMIZATION; EFFICIENCY; HEURISTIC ALGORITHMS; HEURISTIC METHODS; LOCAL SEARCH (OPTIMIZATION); PROBLEM SOLVING; REPAIR;

CONTINUOUS OPTIMIZATION PROBLEMS; LOCAL SEARCH; META HEURISTIC ALGORITHM; META-HEURISTIC METHODS; MULTIDIMENSIONAL KNAPSACK PROBLEMS; OPTIMIZATION PROBLEMS; PSEUDO-UTILITY RATIO; STATE-OF-THE-ART ALGORITHMS;

OPTIMIZATION;

EID: 84977654447     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2016.02.027     Document Type: Article

References (37)

1. [1] Ling, B.W.-K.L., Ho, C.Y.-F., Cao, J., Dai, Q., Efficient complex-valued finite word length allpass rational IIR PCLS filter design via functional inequality constrained integer programming with bit plane searching technique. Mediterr. J. Electron. Commun. 9 (2013), 588–593.
2. [2] Ling, B.W.-K.L., Tian, N., Ho, C.Y.-F., Siu, W.-C., Teo, K.-L., Dai, Q., Maximally decimated paraunitary linear phase FIR filter bank design via iterative SVD approach. IEEE Trans. Signal Process. 63 (2015), 466–481.
3. [3] Ling, B.W.-K.L., Ho, C.Y.-F., Teo, K.-L., Siu, W.-C., Cao, J., Dai, Q., Optimal design of cosine modulated nonuniform linear phase FIR filter bank via both stretching and shifting frequency response of single prototype filter. IEEE Trans. Signal Process. 62 (2014), 2517–2530.
4. [4] Subramaniam, S.R., Ling, B.W.-K.L., Georgakis, A., Filtering in rotated time-frequency domains with unknown noise statistics. IEEE Trans. Signal Process. 60 (2012), 489–493.
5. [5] Ling, B.W.-K.L., Ho, C.Y.-F., Subramaniam, S.R., Georgakis, A., Cao, J., Dai, Q., Optimal design of Hermitian transform and vectors of both mask and window coefficients for denoising applications with both unknown noise characteristics and distortions. Signal Process. 98 (2014), 1–22.
6. [6] Fréville, A., The multidimensional 0–1 knapsack problem: an overview. Eur. J. Oper. Res. 155 (2004), 1–21.
7. [7] Bertsimas, D., Demir, R., An approximate dynamic programming approach to multidimensional knapsack problems. Manag. Sci. 48 (2002), 550–565.
8. [8] Puchinger, J., Raidl, G.R., Pferschy, U., The multidimensional knapsack problem: structure and algorithms. INFORMS J. Comput. 22 (2010), 250–265.
9. [9] Varnamkhasti, M.J., Overview of the algorithms for solving the multidimensional knapsack problems. Adv. Stud. Biol. 4 (2012), 37–47.
10. [10] Balev, S., Yanev, N., Fréville, A., Andonov, R., A dynamic programming based reduction procedure for the multidimensional 0–1 knapsack problem. Eur. J. Oper. Res. 186 (2008), 63–76.
11. [11] Li, V.C., Liang, Y.-C., Chang, H.-F., Solving the multidimensional knapsack problems with generalized upper bound constraints by the adaptive memory projection method. Comput. Oper. Res. 39 (2012), 2111–2121.
12. [12] Vasquez, M., Hao, J.-K., et al. A hybrid approach for the 0–1 multidimensional knapsack problem. IJCAI, 2001, 328–333.
13. [13] Gallardo, J.E., Cotta, C., Fernández, A.J., Solving the multidimensional knapsack problem using an evolutionary algorithm hybridized with branch and bound. Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach, 2005, Springer, 21–30.
14. [14] Chu, P.C., Beasley, J.E., A genetic algorithm for the multidimensional knapsack problem. J. Heuristics 4 (1998), 63–86.
15. [15] Djannaty, F., Doostdar, S., A hybrid genetic algorithm for the multidimensional knapsack problem. Int. J. Contemp. Math. Sci. 3 (2008), 443–456.
16. [16] Sakawa, M., Kato, K., Genetic algorithms with double strings for 0–1 programming problems. Eur. J. Oper. Res. 144 (2003), 581–597.
17. [17] Hanafi, S., Freville, A., An efficient tabu search approach for the 0–1 multidimensional knapsack problem. Eur. J. Oper. Res. 106 (1998), 659–675.
18. [18] Qian, F., Ding, R., Simulated annealing for the 0/1 multidimensional knapsack problem. Numer. Math. Engl. Ser., 16, 2007, 320.
19. [19] Hembecker, F., Lopes, H.S., Godoy, W. Jr., Particle swarm optimization for the multidimensional knapsack problem. Adaptive and Natural Computing Algorithms, 2007, Springer, 358–365.
20. [20] Bansal, J.C., Deep, K., A modified binary particle swarm optimization for knapsack problems. Appl. Math. Comput. 218 (2012), 11042–11061.
21. [21] Baykasoğlu, A., Ozsoydan, F.B., An improved firefly algorithm for solving dynamic multidimensional knapsack problems. Expert Syst. Appl. 41 (2014), 3712–3725.
22. [22] Kong, X., Gao, L., Ouyang, H., Li, S., Solving large-scale multidimensional knapsack problems with a new binary harmony search algorithm. Comput. Oper. Res. 63 (2015), 7–22.
23. [23] Zhang, B., Pan, Q.-K., Zhang, X.-L., Duan, P.-Y., An effective hybrid harmony search-based algorithm for solving multidimensional knapsack problems. Appl. Soft Comput. 29 (2015), 288–297.
24. [24] Azad, M.A.K., Rocha, A.M.A., Fernandes, E.M., Improved binary artificial fish swarm algorithm for the 0–1 multidimensional knapsack problems. Swarm Evol. Comput. 14 (2014), 66–75.
25. [25] Azad, M.A.K., Rocha, A.M.A., Fernandes, E.M., Solving multidimensional 0–1 knapsack problem with an artificial fish swarm algorithm. Computational Science and Its Applications-ICCSA 2012, 2012, Springer, 72–86.
26. [26] Yang, X.-S., Cui, Z., Xiao, R., Gandomi, A.H., Karamanoglu, M., Swarm Intelligence and Bio-inspired Computation: Theory and Applications, Newnes. 2013.
27. [27] Binitha, S., Sathya, S.S., A survey of bio inspired optimization algorithms. Int. J. Soft Comput. Eng. 2 (2012), 137–151.
28. [28] Kennedy, J., Eberhart, R.C., A discrete binary version of the particle swarm algorithm. 1997 IEEE International Conference on Systems, Man, and Cybernetics, Computational Cybernetics and Simulation, vol. 5, 1997, IEEE, 4104–4108.
29. [29] Chih, M., Lin, C.-J., Chern, M.-S., Ou, T.-Y., Particle swarm optimization with time-varying acceleration coefficients for the multidimensional knapsack problem. Appl. Math. Model. 38 (2014), 1338–1350.
30. [30] Chih, M., Self-adaptive check and repair operator-based particle swarm optimization for the multidimensional knapsack problem. Appl. Soft Comput. 26 (2015), 378–389.
31. [31] Wang, L., Yang, R., Xu, Y., Niu, Q., Pardalos, P.M., Fei, M., An improved adaptive binary harmony search algorithm. Inf. Sci. 232 (2013), 58–87.
32. [32] Zou, D., Gao, L., Li, S., Wu, J., Solving 0–1 knapsack problem by a novel global harmony search algorithm. Appl. Soft Comput. 11 (2011), 1556–1564.
33. [33] Uymaz, S.A., Tezel, G., Yel, E., Artificial algae algorithm (AAA) for nonlinear global optimization. Appl. Soft Comput. 31 (2015), 153–171.
34. [34] Suganthan, P.N., Hansen, N., Liang, J.J., Deb, K., Chen, Y.-P., Auger, A., Tiwari, S., Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization, KanGAL report 2005005. 2005.
35. [35] Pirkul, H., A heuristic solution procedure for the multiconstraint zero-one knapsack problem. Nav. Res. Logist. 34 (1987), 161–172.
36. [36] Xiangyong Konga, H.O.S.L., Gaoa, Liqun, Solving large-scale multidimensional knapsack problems with a new binary harmony search algorithm. Comput. Oper. Res. 63 (2015), 7–22.
37. [37] Song, W.T., Schmeiser, B.W., Omitting meaningless digits in point estimates: the probability guarantee of leading-digit rules. Oper. Res. 57 (2009), 109–117.