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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 3/2016

01-06-2016 | Original Article

Solving 0–1 Knapsack Problem using Cohort Intelligence Algorithm

Authors: Anand J. Kulkarni, Hinna Shabir

Published in: International Journal of Machine Learning and Cybernetics | Issue 3/2016

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Abstract

An emerging technique, inspired from the natural and social tendency of individuals to learn from each other referred to as Cohort Intelligence (CI) is presented. Learning here refers to a cohort candidate’s effort to self supervise its own behavior and further adapt to the behavior of the other candidate which it intends to follow. This makes every candidate improve/evolve its behavior and eventually the entire cohort behavior. This ability of the approach is tested by solving an NP-hard combinatorial problem such as Knapsack Problem (KP). Several cases of the 0–1 KP are solved. The effect of various parameters on the solution quality has been discussed.The advantages and limitations of the CI methodology are also discussed.
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Appendix
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Literature
1.
Deb K (2000) An efficient constraint handling method for genetic algorithms. Comput Meth Appl Mech Eng 186:311–338CrossRefMATH
2.
Ray T, Tai K, Seow KC (2001) Multiobjective design optimization by an evolutionary algorithm. Eng Optim 33(4):399–424CrossRef
3.
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 1:28–39CrossRef
5.
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 218:85–102MathSciNetCrossRefMATH
10.
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):204–210CrossRef
12.
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):29–41CrossRef
14.
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 59:345–358CrossRefMATH
23.
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):166–175CrossRef
24.
Zou D, Gao L, Li S, Wu J (2011) Solving 0–1 Knapsack Problem by a Novel Global Harmony Search algorithm. Appl Soft Comput 11:1556–1564CrossRef
25.
26.
Layeb A (2011) A novel Quantum Inspired Cuckoo Search for Knapsack Problems. Int J Bio-Inspired Comput 3(5):297–305CrossRef
27.
Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68CrossRef
28.
Mahdavi M, Fesanghary M, Damangir E (2007) An Improved Harmony Search algorithm for solving optimization problems. Appl Math Comput 188:1567–1579MathSciNetMATH
29.
Deb K (2000) An efficient constraint handling method for genetic algorithms. Comput Meth Appl Mech Eng 186:311–338CrossRefMATH
30.
Kulkarni AJ, Tai K (2011) Solving constrained optimization problems using probability collectives and a penalty function approach. Int J Comput Intell Appl 10(4):445–470CrossRefMATH
31.
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):281–292CrossRef
33.
Chen CJ (2012) Structural vibration suppression by using neural classifier with genetic algorithm. Int J Mach Learn Cybernet 3(3):215–221CrossRef
34.
Wang XZ, He Q, Chen DG, Yeung D (2005) A genetic algorithm for solving the inverse problem of support vector machines. Neurocomputing 68:225–238CrossRef
35.
Wang XZ, He YL, Dong LC, Zhao HY (2011) Particle swarm optimization for determining fuzzy measures from data. Inf Sci 181(19):4230–4252CrossRefMATH
Metadata
Title
Solving 0–1 Knapsack Problem using Cohort Intelligence Algorithm
Authors
Anand J. Kulkarni
Hinna Shabir
Publication date
01-06-2016
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 3/2016
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-014-0272-y

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