Abstract
The set covering problem (SCP) is a well known classic combinatorial NP-hard problem, having practical application in many fields. To optimize the objective function of the SCP, many heuristic, meta heuristic, greedy and approximation approaches have been proposed in the recent years. In the development of swarm intelligence, the particle swarm optimization is a nature inspired optimization technique for continuous problems and for discrete problems we have the well known discrete particle swarm optimization (DPSO) method. Aiming towards the best solution for discrete problems, we have the recent method called jumping particle swarm optimization (JPSO). In this DPSO the improved solution is based on the particles attraction caused by attractor. In this paper, a new approach based on JPSO is proposed to solve the SCP. The proposed approach works in three phases: for selecting attractor, refining the feasible solution given by the attractor in order to reach the optimality and for removing redundancy in the solution. The proposed approach has been tested on the benchmark instances of SCP and compared with best known methods. Computational results show that it produces high quality solution in very short running times when compared to other algorithms.
Similar content being viewed by others
References
Aickelin Uwe (2002) An indirect genetic algorithm for set covering problems. J Oper Res Soc 53(10):1118–1126
Aliguliyev RM (2010) Clustering techniques and Discrete particle Swarm Optimization algorithm for Multi-document summarization. Comput Intell 26:420–448
Al-kazemi B, Mohan CK (2002) Multi-phase discrete particle swarm optimization. In: Fourth international workshop on frontiers in evolutionary algorithms, Kinsale, Ireland
Azimi ZN, Toth P, Galli L (2010) An electromagnetism metaheuristic for the unicost set covering problem. Eur J Oper Res 205:290–300
Balas E, Carrera MC (1996) A dynamic subgradient-based branch-and-bound procedure for set covering. Oper Res 44(6):875–890
Bautista J, Pereira J (2007) A GRASP algorithm to solve the unicost set covering problem. Comput Oper Res 34:3162–3173
Beasley JE (1990) A Lagrangian heuristic for set covering problems. Nav Res Logist 37(1):151–164
Beasley JE, Chu RC (1996) A genetic algorithm for the set covering problem. Eur J Oper Res 94:392–404
Brusco MJ, Jacobs LW, Thompson GM (1999) A morphing procedure to supplement a simulated annealing heuristic for cost and coverage correlated set covering problems. Ann Oper Res 86:611–627
Caprara A, Toth P, Fischetti M (2000) Algorithms for the set covering problem. Ann Oper Res 98:353–371
Ceria S, Nobili P, Sassano A (1998) A Lagrangian-based heuristic for large-scale set covering problems. Math Program 81:215–228
Chen AL, Yang GK, Wu ZM (2006) Hybrid discrete particle swarm optimization algorithm for capacitated vehicle routing problem. J Zhejiang Univ Sci A 7(4):607–614
Consoli S, Pérez JAM, Dowman KD, Mladenović N (2010) Discrete particle swarm optimization for the minimum labelling steiner tree problem. Nat Comput 9:29–46
Cormode G, Karloff H, Wirth A (2010) Set cover algorithms for very large datasets. In: ACM CIKM’10
Correa ES, Freitas AA, Johnson CG (2006) A new discrete particle swarm algorithm applied to attribute selection in a bioinformatic data set. In: Proceedings of the GECCO, pp 35–42
Demśar J (2006) Statistical comparison of classifiers over multiple data sets. J Mach Learn Res 7:1–30
Fisher ML, Kedia P (1990) Optimal solution of set covering/partitioning problems using dual heuristics. Manage Sci 36(6):674–688
Galinier P, Hertz A (2007) Solution techniques for the large set covering problem. Discrete Appl Math 155:312–326
García FJM, Pérez JAM (2008) Jumping frogs optimization: a new swarm method for discrete optimization, Technical Report DEIOC 3/2008, Department of Statistics, O.R and computing, University of La Laguna, Tenerife, Spain
Garey MR, Johnson DS (1979) Computers and intractability: a guide to the theory of NP-completeness. Freeman, San Francisco
Grossman T, Wool A (1997) Computational experience with approximation algorithms for the set covering problem. Eur J Oper Res 101:81–92
Gutiérrez JPC, Silva DL, Pérez JAM (2008) Exploring feasible and infeasible regions in the vehicle routing problem with time windows using a multi-objective particle swarm optimization approach. In: Proceedings of the international workshop on nature inspired cooperatives strategies for optimization, NICSO
Housos E, Elmoth T (1997) Automatic optimization of subproblems in scheduling airlines crews. Interfaces 27(5):68–77
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the 4th IEEE international conference on neural networks, Perth, Australia, pp 1942–1948
Kennedy J, Eberhart R (1997) A discrete binary version of the particle swarm algorithm. IEEE Conf Syst Man Cybern 5:4104–4108
Lan G, DePuy GW, Whitehouse GE (2007) An effective and simple heuristic for the set covering problem. Eur J Oper Res 176:1387–1403
Li Y, Cai Z (2012) Gravity-based heuristic for set covering problems and its application in fault diagnosis. J Syst Eng Electron 23:391–398
Li J, Kwan RSK (2004) A meta-heuristic with orthogonal experiment for the set covering problem. J Math Model Algorithms 3:263–283
Liaoa C-J, Tseng C-T, Luarn P (2007) A discrete version of particle swarm optimization for flowshop scheduling problems. Comput Oper Res 34:3099–3111
Martinoli PJA (2006) Discrete multi-valued particle swarm optimization. Proc IEEE Swarm Intell Symp 1:103–110
Moirangthem J, Dash SS, Ramas R (2012) Determination of minimum break point set using paricle swarm optimization for system-wide protective relay setting and coordination. Eur Trans Electr Power 22:1126–1135
Ohlsson M, Peterson C, Soderberg B (2001) An efficient mean field approach to the set covering problem. Eur J Oper Res 133:583–595
Pan Q-K, Tasgetiren MF, Liang Y-C (2008) A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem. Comput Oper Res 35:2807–2839
Pardalos PM et al (2006) Experimental analysis of approximation algorithms for the vertex cover and set covering problems. Comput Oper Res 33:3520–3534
Qiang L, Na QX, Shi-rang L (2009) A discrete particle swarm optimization algorithm with fully communicated information, ACM GEC, pp 393–400
Raja Balachandar S, Kannan K (2010) A meta-heuristic algorithm for set covering problem based on gravity. WASET 43:504–509
Ren Z-G, Feng Z-R, Ke L-J, Zhang Z-J (2010) New ideas for applying ant colony optimization to the set covering problem. Comput Ind Eng 58:774–784
Saxena A, Goyal V, Lejeune MA (2010) MIP reformulations of the probabilistic set covering problem. Math Program Ser A 121:1–31
Sherbaz AA, Kuseler T, Adams C, Marsalek R, Povalac K (2010) WiMAX parameters adaptation through a baseband processor using discrete particle swarm method. Int J Microw Wirel Technol 2(2):165–171
Shi XH, Liang YC, Lee HP, Lu C, Wang QX (2007) Particle swarm optimization-based algorithms for TSP and generalized TSP. Inf Process Lett 103:169–176
Solar M, Parada V, Urrutia R (2002) A parallel genetic algorithm to solve the set-covering problem. Comput Oper Res 29:1221–1235
Stützle T, Hoos HH (2000) Max–min ant system. Future Gener Comput Syst 16:889–914
Tasgetiren MF, Suganthan PN, Pan Q-K (2007) A discrete particle swarm optimization algorithm for the generalized traveling salesman problem. In: Proceedings of the GECCO, London, pp 158–165
Telelis OA, Zissimopoulos V (2005) Absolute O(logm) error in approximating random set covering: an average case analysis. Inf Process Lett 94:171–177
Toregas C, Swain R, ReVelle C, Bergman L (1971) The location of emergency service facilities. Oper Res Int J 19:1363–1373
Vasko FJ, Wilson GR (1984) Using a facility location algorithm to solve large set covering problems. Oper Res Lett 3(2):85–90
Vasko FJ, Wolf FE (1987) Optimal selection of ingot sizes via set covering. Oper Res 35(3):346–353
Whitehouse GE, DePuy GW, Moraga RJ (2002) Meta-RaPS approach for solving the resource allocation problem. In: Proceedings of the 2002 world automation congress, Orlando, FL
Wolsey LA (1998) Lagrangian duality. In: Wolsey (ed) Integer programming. Wiley, New York, pp 167–181
Hollander M, Wolfe DA (1973) Nonparametric statistical methods, 2nd ed. Wiley, New York
Nemenyi PB (1963) Distribution free multiple comparisons, Ph.D. thesis. Princeton University, New Jersey
Secrest BR (2001) Traveling salesman problem for surveillance mission using Particle Swarm Optimization, Master’s Thesis, School of Engineering and Management of the Air Force institute of Technology
Yagiura M, Kishida M, Ibaraki T (2006) A 3-flip neighborhood local search for the set covering problem. Eur J Oper Res 172:472–499
Yang S, Wang M, Jiao L (2004) A quantum particle swarm optimization. In: Proceedings of the CEC2004, the congress on evolutionary computing, vol 1, pp 320–324
Zhan Z-H, Zhang J, Du K, Xiao J (2012) Extended binary particle swarm optimization approach for disjoint set covers problem wireless sensor networks. IEEE Conf Technol Appl Artif Intell 13228691:327–331
Zhang C, Sun J, Wang Y, Yang Q (2007) An improved discrete particle swarm optimization algorithm for TSP, IEEE/WIC/ACM international conferences on web intelligence and intelligent agent technology—workshops, pp 35–38
Zhang H, Sun J, Liu J (2007) A new simplification method for terrain model using discrete particle swarm optimization. In: Proceedings of the 15th international symposium on advances in geographic information systems ACM GIS, pp 1–4
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Balaji, S., Revathi, N. A new approach for solving set covering problem using jumping particle swarm optimization method. Nat Comput 15, 503–517 (2016). https://doi.org/10.1007/s11047-015-9509-2
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11047-015-9509-2