Integer cat swarm optimization algorithm for multiobjective integer problems
In the literature, several variants of cat swarm optimization (CSO) algorithm are reported. However, CSO for integer multiobjective optimization problems (MOPs) has not yet been investigated. Owing to the frequent occurrence of integer MOPs and their importance in practical design problems, in this...
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| Abstract | In the literature, several variants of cat swarm optimization (CSO) algorithm are reported. However, CSO for integer multiobjective optimization problems (MOPs) has not yet been investigated. Owing to the frequent occurrence of integer MOPs and their importance in practical design problems, in this work, we investigate a new CSO approach for solving purely integer MOPs. This new approach named as multiobjective integer cat swarm optimization (MO-ICSO) algorithm incorporates the modified version of the CSO algorithm for MOPs. This approach is comprised of the concepts of rounding the floating points to the nearest integer numbers and the probabilistic updating (PU) technique. It uses the idea of Pareto dominance for finding the non-dominated solutions and an external archive for storing these solutions. We demonstrate the power of this new approach via its quantitative analysis and sensitivity test of its several parameters using different performance metrics performed over multiobjective multidimensional knapsack problem and several standard test functions. The simulation results argue that the proposed MO-ICSO approach can be a better candidate for solving the integer MOPs. |
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| AbstractList | In the literature, several variants of cat swarm optimization (CSO) algorithm are reported. However, CSO for integer multiobjective optimization problems (MOPs) has not yet been investigated. Owing to the frequent occurrence of integer MOPs and their importance in practical design problems, in this work, we investigate a new CSO approach for solving purely integer MOPs. This new approach named as multiobjective integer cat swarm optimization (MO-ICSO) algorithm incorporates the modified version of the CSO algorithm for MOPs. This approach is comprised of the concepts of rounding the floating points to the nearest integer numbers and the probabilistic updating (PU) technique. It uses the idea of Pareto dominance for finding the non-dominated solutions and an external archive for storing these solutions. We demonstrate the power of this new approach via its quantitative analysis and sensitivity test of its several parameters using different performance metrics performed over multiobjective multidimensional knapsack problem and several standard test functions. The simulation results argue that the proposed MO-ICSO approach can be a better candidate for solving the integer MOPs. |
| Author | Ahmad, Ayaz Ali Murtza, Shahid Shafique, Jawad |
| Author_xml | – sequence: 1 givenname: Shahid surname: Ali Murtza fullname: Ali Murtza, Shahid organization: School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST) – sequence: 2 givenname: Ayaz orcidid: 0000-0002-2253-6004 surname: Ahmad fullname: Ahmad, Ayaz email: ayaz.ahmad@ciitwah.edu.pk, ayaz.uet@gmail.com organization: Department of Electrical and Computer Engineering, COMSATS University Islamabad – sequence: 3 givenname: Jawad surname: Shafique fullname: Shafique, Jawad organization: Department of Electronics Engineering, UET |
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| Cites_doi | 10.1080/15325008.2013.853215 10.1109/ICMMT.2010.5524774 10.1109/TAP.2006.882165 10.1109/ICNN.1995.488968 10.1162/evco.1999.7.3.205 10.1080/08839514.2018.1556419 10.3233/AIC-160708 10.1109/SoCPaR.2009.23 10.1109/DSD.2008.21 10.1016/j.eswa.2011.08.157 10.1109/IC4.2013.6653754 10.13182/NT15-130 10.1016/j.eswa.2011.04.054 10.1016/j.apenergy.2016.05.056 10.1007/s10462-017-9559-1 10.1016/j.cor.2006.12.030 10.1007/s10489-018-1190-6 10.1016/j.energy.2014.09.062 10.1017/S0890060400002821 10.1115/1.1876436 10.1016/j.rser.2010.12.008 10.1109/ICEC.1998.699326 10.1145/298151.298382 10.1016/j.asoc.2017.06.059 10.1109/ICSMC.1997.637339 10.1016/j.engappai.2018.05.003 10.1007/978-3-662-03199-5 10.1109/4235.996017 10.1016/j.jocs.2017.07.018 10.1007/3-540-61723-X_1014 10.1109/4235.797969 10.1007/978-3-030-10674-4 10.1007/s00607-017-0574-5 10.1109/CEC.2002.1004478 10.1007/s11227-017-2046-2 |
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| Keywords | Cat swarm optimization Swarm intelligence Multiobjective optimization Integer optimization |
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| References_xml | – reference: AbualigahLMQHanandehESApplying genetic algorithms to information retrieval using vector space modelInt J Comput Sci Eng Appl20155119 – reference: PetrieCJWebsterTACutkoskyMRUsing Pareto optimality to coordinate distributed agentsArtif Intell Eng Des Anal Manuf19959426928110.1017/S0890060400002821 – reference: Matsui T, Kato K, Sakawa M, Uno T, Matsumoto K (2008) Particle swarm optimization for nonlinear integer programming problems. In: Proceedings of international multiconference of engineers and computer scientists, pp 1874–1877 – reference: AbualigahLMKhaderATHanandehESA new feature selection method to improve the document clustering using particle swarm optimization algorithmJ Comput Sci20182545646610.1016/j.jocs.2017.07.018 – reference: Kennedy J, Eberhart RC (1997) A discrete binary version of the particle swarm algorithm. In: 1997 IEEE international conference on computational cybernetics and simulation, systems, man, and cybernetics, vol 5. IEEE, pp 4104–4108 – reference: PandaGPradhanPMMajhiBPanigrahiBKShiYLimM-HDirect and inverse modeling of plants using cat swarm optimizationHandbook of swarm intelligence2011BerlinSpringer1207.68361 – reference: TsaiP-WPanJ-SChenS-MLiaoB-YEnhanced parallel cat swarm optimization based on the Taguchi methodExpert Syst Appl20123963096319 – reference: IshfaqHAbidaPAyazAYasirQMQadri NadiaNJameelANsga-ii-based design space exploration for energy and throughput aware multicore architecturesCybern Syst2017486–7536550 – reference: Santosa B, Ningrum MK (2009) Cat swarm optimization for clustering. In: International conference of soft computing and pattern recognition, SOCPAR’09. 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