1.

Deb K (2000) An efficient constraint handling method for genetic algorithms. Comput Meth Appl Mech Eng 186:311–338

CrossRefMATH2.

Ray T, Tai K, Seow KC (2001) Multiobjective design optimization by an evolutionary algorithm. Eng Optim 33(4):399–424

CrossRef3.

Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, pp 1942–1948 (1995)

4.

Dorigo M, Birattari M, Stitzle T (2006) Ant colony optimization: artificial ants as a computational intelligence technique. IEEE Comput Intell Mag 1:28–39

CrossRef5.

Pham DT, Ghanbarzadeh A, Koc E, Otri S, Rahim S, Zaidi M (2005) The Bees algorithm, technical note. Manufacturing Engineering Centre, Cardiff University

6.

Feng Y, Jia K, He Y (2014) An improved hybrid encoding cuckoo search algorithm for 0–1 Knapsack Problems. Comput Intell Neurosci 2014:1–9

7.

Kulkarni AJ, Durugkar IP, Kumar MR (2013) Cohort intelligence: a self supervised learning behavior. In: Proc. of IEEE international conference on systems, man, and cybernetics, pp 396–400

8.

Liu L, Zhong WM, Qian F (2010) An improved chaos-particle swarm optimization algorithm. J East China Univ Sci Technol (Nat Sci Ed) 36:267–272

9.

Xu W, Geng Z, Zhu Q, Gu X (2013) A piecewise linear chaotic map and sequential quadratic programming based robust hybrid particle swarm optimization. Inf Sci 218:85–102

MathSciNetCrossRefMATH10.

Kennedy J (2003) Bare bones particle swarms. In: Proceedings of the IEEE swarm intelligence symposium (SIS’03), pp 80–87

11.

Mendes R, Kennedy J, Neves J (2004) The fully informed particle swarm: simpler. Maybe better. IEEE Trans Evol Comput 8(3):204–210

CrossRef12.

Chen L, Sun H, Wang S (2009) Solving continuous optimization using ant colony algorithm. In: Second international conference on future information technology and management engineering, pp 92–95

13.

Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B Cybern 26(1):29–41

CrossRef14.

Krishnasamy G, Kulkarni AJ, Paramesran R (2014) A hybrid approach for data clustering based on modified cohort intelligence and K-means. Exp Syst Appl 41(3):6009–6016

15.

Hernández PR, Dimopoulos NJ (2005) A new heuristic for solving the multichoice multidimensional Knapsack Problem. In: Proc. of IEEE transactions on systems, man, and cybernetics—part A : systems and humans, vol 35, no 5, pp 708–717

16.

Martello S, Toth P (1990) Knapsack Problems: algorithms and computer implementations. Wiley, New York, pp 1–296 (1990)

17.

Tavares J, Pereira FB, Costa E (2008) Multidimensional Knapsack Problem: a fitness landscape analysis. In: Proc. of IEEE transactions on systems, man, and cybernetics—part B. Cybernetics 38(3):604–616

18.

Moser M (1996) Declarative scheduling for optimally graceful QoS degradation. In: Proc. IEEE international conference multimedia computing systems, pp 86–93 (1996)

19.

Khan MS (1998) Quality adaptation in a multisession multimedia system: model, algorithms and architecture. Ph.D. dissertation, Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, Canada (1998)

20.

Warner D, Prawda J (1972) A mathematical programming model for scheduling nursing personnel in a hospital. Manag Sci (Application Series Part 1) 19(4):411–422

21.

Psinger D (1995) Algorithms for Knapsack Problems. PhD thesis, Department of Computer Science, University of Copenhagen, Copenhagen, Denmark (1995)

22.

Laporte G (1992) The vehicle routing problem: an overview of exact and approximate algorithms. Eur J Oper Res 59:345–358

CrossRefMATH23.

Granmo OC, Oommen BJ, Myrer SA, Olsen MG (2007) Learning automata-based solutions to the nonlinear fractional Knapsack Problem with applications to optimal resource allocation. Proc IEEE Trans Syst Man Cybern Part B Cybern 37(1):166–175

CrossRef24.

Zou D, Gao L, Li S, Wu J (2011) Solving 0–1 Knapsack Problem by a Novel Global Harmony Search algorithm. Appl Soft Comput 11:1556–1564

CrossRef25.

Layeb A (2013) A hybrid Quantum Inspired Harmony Search algorithm for 0–1 optimization problems. J Comput Appl Math 253:14–25

MathSciNetCrossRefMATH26.

Layeb A (2011) A novel Quantum Inspired Cuckoo Search for Knapsack Problems. Int J Bio-Inspired Comput 3(5):297–305

CrossRef27.

Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68

CrossRef28.

Mahdavi M, Fesanghary M, Damangir E (2007) An Improved Harmony Search algorithm for solving optimization problems. Appl Math Comput 188:1567–1579

MathSciNetMATH29.

Deb K (2000) An efficient constraint handling method for genetic algorithms. Comput Meth Appl Mech Eng 186:311–338

CrossRefMATH30.

Kulkarni AJ, Tai K (2011) Solving constrained optimization problems using probability collectives and a penalty function approach. Int J Comput Intell Appl 10(4):445–470

CrossRefMATH31.

Kulkarni AJ, Tai K (2011) A probability collectives approach with a feasibility-based rule for constrained optimization. Appl Comput Intell Soft Comput 2011, Article ID 980216

32.

Ma W, Wang M, Zhu X (2014) Improved particle swarm optimization based approach for bilevel programming problem-an application on supply chain model. Int J Mach Learn Cybernet 5(2):281–292

CrossRef33.

Chen CJ (2012) Structural vibration suppression by using neural classifier with genetic algorithm. Int J Mach Learn Cybernet 3(3):215–221

CrossRef34.

Wang XZ, He Q, Chen DG, Yeung D (2005) A genetic algorithm for solving the inverse problem of support vector machines. Neurocomputing 68:225–238

CrossRef35.

Wang XZ, He YL, Dong LC, Zhao HY (2011) Particle swarm optimization for determining fuzzy measures from data. Inf Sci 181(19):4230–4252

CrossRefMATH