A parallel immune optimization algorithm for numeric function optimization
Immune optimization algorithms show good performance in obtaining optimal solutions especially in dealing with numeric optimization problems where such solutions are often difficult to determine by traditional techniques. This article presents the parallel suppression control algorithm (PSCA), a par...
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| Published in | Evolutionary intelligence Vol. 1; no. 3; pp. 171 - 185 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer-Verlag
01.10.2008
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1864-5909 1864-5917 |
| DOI | 10.1007/s12065-008-0014-8 |
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| Abstract | Immune optimization algorithms show good performance in obtaining optimal solutions especially in dealing with numeric optimization problems where such solutions are often difficult to determine by traditional techniques. This article presents the parallel suppression control algorithm (PSCA), a parallel algorithm for optimization based on artificial immune systems (AIS). PSCA is implemented in a parallel platform where the corresponding population of antibodies is partitioned into subpopulations that are distributed among the processes. Each process executes the immunity-based algorithm for optimizing its subpopulation. In the process of evolving the solutions, the activities of antibodies and the activities of the computation agents are regulated by the general suppression control framework (GSCF) which maintains and controls the interactions between the populations and processes. The proposed algorithm is evaluated with benchmark problems, and its performance is measured and compared with other conventional optimization approaches. |
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| AbstractList | Immune optimization algorithms show good performance in obtaining optimal solutions especially in dealing with numeric optimization problems where such solutions are often difficult to determine by traditional techniques. This article presents the parallel suppression control algorithm (PSCA), a parallel algorithm for optimization based on artificial immune systems (AIS). PSCA is implemented in a parallel platform where the corresponding population of antibodies is partitioned into subpopulations that are distributed among the processes. Each process executes the immunity-based algorithm for optimizing its subpopulation. In the process of evolving the solutions, the activities of antibodies and the activities of the computation agents are regulated by the general suppression control framework (GSCF) which maintains and controls the interactions between the populations and processes. The proposed algorithm is evaluated with benchmark problems, and its performance is measured and compared with other conventional optimization approaches. |
| Author | Lau, Henry Y. K. Tsang, Wilburn W. P. |
| Author_xml | – sequence: 1 givenname: Henry Y. K. surname: Lau fullname: Lau, Henry Y. K. email: hyklau@hku.hk, h0246582@hkusua.hku.hk organization: Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong – sequence: 2 givenname: Wilburn W. P. surname: Tsang fullname: Tsang, Wilburn W. P. organization: Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong |
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| CitedBy_id | crossref_primary_10_3390_app142412044 crossref_primary_10_1016_j_ijepes_2010_12_036 crossref_primary_10_20965_jaciii_2010_p0110 crossref_primary_10_1016_j_jocs_2017_04_015 crossref_primary_10_1007_s00500_015_1724_3 crossref_primary_10_1007_s00521_016_2507_1 |
| Cites_doi | 10.1007/3-540-45712-7_39 10.1007/11539117_126 10.1109/CEC.2002.1004472 10.1007/978-3-540-30220-9_34 10.1016/j.asoc.2006.12.004 10.2514/1.17873 10.1007/3-540-33521-8_51 10.1007/978-3-540-30220-9_22 10.1007/978-3-662-06369-9_4 10.1007/11536444_12 10.1007/978-3-540-30220-9_8 10.1007/11536444_2 10.5962/bhl.title.8281 10.1109/CEC.2004.1331139 10.1006/jpdc.2002.1854 10.1109/MCI.2006.329705 10.1109/TEVC.2002.1011539 10.1109/CEC.2003.1299565 10.1109/ICSMC.2004.1400870 10.1109/CEC.2004.1330978 10.1007/3-540-45105-6_26 10.1016/j.cageo.2006.09.002 |
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| Keywords | Parallel implementation Artificial immune systems Function optimization Immune optimization algorithm |
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| References | Kelsey J, Timmis J (2003) Immune inspired somatic contiguous hypermutation for function optimisation. In: Genetic and evolutionary computation conference (GECCO 2003), Chicago, USA, pp 207–218 Zhu C, Zhao B, Ye B, Yijia C (2005) An improved immune algorithm and its evaluation of optimization efficiency. In: International conference on natural computation (ICNC 2005), Changsha, China, pp 895–904 Ko AWY, Lau HYK, Lau TL (2005) General suppression control framework: application in selfbalancing robots. In: Fourth international conference on artificial immune systems (ICARIS-2005), Banff, Canada, pp 375–388 Cutello V, Nicosia G, Pavone M (2004) Exploring the capability of immune algorithms: a characterization of hypermutation operators. In: Third international conference on artificial immune systems (ICARIS-2004), Catania, Italy, pp 263–276 DasguptaDAdvances in artificial immune systemsIEEE Comput Intell Mag200614049 Vesterstrom J, Thomsen R (2004) A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. In: 2004 Congress on evolutionary computation (CEC 2004), Portland, USA de CastroLNVon ZubenFJlearning and optimization using the clonal selection principleIEEE Trans Evol Comput2002623925110.1109/TEVC.2002.1011539 Brownlee J (2007) Optimization Algorithm Toolkit (optalgtoolkit) Version 1.4. In: Optimization Algorithm Toolkit (OAT). Available via Sourceforge.net. http://sourceforge.net/projects/optalgtoolkit. Accessed on 12 February 2008 Kim J, Bentley PJ (1999) The human immune system and network intrusion detection. In: Seventh European congress on intelligent techniques and soft computing (EUFIT’99), Aachen, Germany Wang X, Gao XZ, Ovaska SJ (2004) Artificial immune optimization methods and applications—a survey. In: IEEE international conference on systems, man and cybernetics, 2004, Hague, The Netherlands, pp 3415–3420 Garrett SM (2004) Parameter-free, adaptive clonal selection. In: 2004 Congress on evolutionary computation (CEC 2004), Portland, USA, pp 1052–1058 Male D, Brostoff J, Roitt I, Rotth DB (2006) Immunolohy, 7th edn. Mosby HartETimmisJApplication areas of AIS: the past, the present and the futureAppl Soft Comput2008819120110.1016/j.asoc.2006.12.004 PlayfairJHLChainBMImmunology at a Glance2001CornwallBlackwell Luo Y, Li R, Tian F (2004) Application of artificial immune algorithm to multimodal function optimization. In: Fifth world congress on intelligent control and automation, 2004 (WCICA 2004), Hangzhou, China, pp 2248–2252 Ko AWY, Lau HYK, Lau TL (2004) A general suppression framework for distributed control. In: Tenth IEEE international conference on methods and models in automation and robotics, Miedzyzdroje, Poland JerneNKTowards a network theory of the immune systemAnnu Immunol1974125C373389 HeKZhengLDongSTangLWuJZhengCPGO: a parallel computing platform for global optimization based on genetic algorithmComput Geosci200633357366 CutelloVNicosiaGPaviaEA parallel immune algorithm for global optimizationAdv Soft Comput2006546747510.1007/3-540-33521-8_51 Ko AWY, Lau HYK, Lau TL (2004) An immuno control framework for decentralized mechatronic control. In: Third international conference on artificial immune systems (ICARIS-2004), Catania, Italy, pp 91–105 Cutello V, Nicosia G (2004) The clonal selection principle for in silico and in vitro computing. In: de Castro LN, Von Zuben FJ (eds) Recent developments in biologically inspired computing. Idea Group Publishing, pp 104–145 Watkins A, Timmis J (2004) Exploiting parallelism inherent in AIRS, an artificial immune classifier. In: Third international conference on artificial immune systems (ICARIS-2004), Catania, Italy, pp 427–438 Watkins A, Bi X, Phadke A (2003) Parallelizing an immune-inspired algorithm for efficient pattern recognition. intelligent engineering systems through artificial Neural Networks: Smart Engineering System Design: Neural Networks, Fuzzy Logic, Evolutionary Programming, Complex Systems and Artificial Life 13: 225–230 de Castro LN (2001) Demo file for Matlab. In: Artificial immune systems. Available via Department of Computer Engineering and Industrial Automation, State University of Campinas. http://www.dca.fee.unicamp.br/~lnunes/immune.html. Accessed on 1 April 2007 Greensmith J, Aickelin U, Cayzer S (2005) Introducing dendritic cells as a novel immune-inspired algorithm for anomaly detection. In: Fourth international conference on artificial immune systems (ICARIS-2005), Banff, Canada, pp 153–167 Timmis J, Knight T, de Castro LN, Hart E (2004) An overview of artificial immune systems. In: Paton R, Bolouri H, Holcombe M, Parish JH, Tateson R (eds) Computation in cells and tissues: perspectives and tools of thought. Springer, Heidelberg, pp 51–86 Sompayrac L (1999) How the immune system works. Blackwell, Cornwall VenterGSobieszczanski-SobieskiJA parallel particle swarm optimization algorithm accelerated by asynchronous evaluationsJ Aerosp Comput Inf Commun20063312313710.2514/1.17873 Kepner J (2001) MatlabMPI. In: Parallel programming with MatlabMPI. Available via Lincoln Laboratory, Massachusetts Institute of Technology. http://www.ll.mit.edu/MatlabMPI. Accessed on 1 April 2007 Liu P, Lau F, Lewis MJ, Wang C-L (2002) A new asynchronous parallel evolutionary algorithm for function optimization. In: Seventh international conference on parallel problem solving from nature, Granada, Spain, pp 401–410 Cutello V, Narzisi G, Nicosia G, Pavone M (2005) Clonal selection algorithms: a comparative case study using effective mutation potentials. In: Fourth international conference on artificial immune systems (ICARIS-2005), Banff, Canada, pp 13–28 MakKLLauPSKOrder pickings in an AS/RS with multiple I/O stations using an artificial immune system with aging antibodiesEng Lett200816122130 Watkins A (2005) Exploiting immunological metaphors in the development of serial, parallel, and distributed learning algorithms. University of Kent, Canterbury Dasgupta D, Zhou J (2003) Artificial immune system (AIS) research in the last five years. In: 2003 congress on evolutionary computation (CEC 2003). IEEE, Canberra, pp 123–130 Burnet FM (1959) The clonal selection theory of acquired immunity. Cambridge University Press, Cambridge Lau HYK, Wong VWK (2004) A strategic behavior-based intelligent transport system with artificial immune system. In: IEEE international conference on systems, man and cybernetics, 2004, Hague, The Netherlands, pp 3909–3914 de Castro LN, Timmis J (2002) Artificial immune systems: a new computational intelligence approach. Springer, Heidelberg Watkins A, Boggess L (2002) A new classifier based on resource limited artificial immune systems. In: 2002 IEEE world congress on computational intelligence, Honolulu, USA, pp 225–230 de Castro LN, Von Zuben FJ (2002) An artificial immune network for multimodal function optimization on dynamic environments. In: 2002 congress on evolutionary computation (CEC 2002), Honolulu, USA, pp 289–296 RandallMLewisAA parallel implementation of ant colony optimizationParallel Distrib Comput200262142114321063.6809510.1006/jpdc.2002.1854 14_CR15 14_CR37 14_CR36 14_CR13 14_CR35 14_CR12 14_CR34 14_CR33 14_CR10 14_CR32 14_CR30 D Dasgupta (14_CR4) 2006; 1 V Cutello (14_CR31) 2006; 5 JHL Playfair (14_CR11) 2001 14_CR19 14_CR18 14_CR17 14_CR39 14_CR16 14_CR38 14_CR1 14_CR40 14_CR2 LN Castro de (14_CR6) 2002; 6 K He (14_CR27) 2006; 33 14_CR9 E Hart (14_CR5) 2008; 8 14_CR25 14_CR7 M Randall (14_CR24) 2002; 62 14_CR8 14_CR23 G Venter (14_CR26) 2006; 3 14_CR21 14_CR3 14_CR20 KL Mak (14_CR22) 2008; 16 NK Jerne (14_CR14) 1974; 125 14_CR29 14_CR28 |
| References_xml | – reference: de CastroLNVon ZubenFJlearning and optimization using the clonal selection principleIEEE Trans Evol Comput2002623925110.1109/TEVC.2002.1011539 – reference: Dasgupta D, Zhou J (2003) Artificial immune system (AIS) research in the last five years. In: 2003 congress on evolutionary computation (CEC 2003). IEEE, Canberra, pp 123–130 – reference: Luo Y, Li R, Tian F (2004) Application of artificial immune algorithm to multimodal function optimization. In: Fifth world congress on intelligent control and automation, 2004 (WCICA 2004), Hangzhou, China, pp 2248–2252 – reference: Watkins A, Boggess L (2002) A new classifier based on resource limited artificial immune systems. In: 2002 IEEE world congress on computational intelligence, Honolulu, USA, pp 225–230 – reference: HeKZhengLDongSTangLWuJZhengCPGO: a parallel computing platform for global optimization based on genetic algorithmComput Geosci200633357366 – reference: de Castro LN, Timmis J (2002) Artificial immune systems: a new computational intelligence approach. Springer, Heidelberg – reference: PlayfairJHLChainBMImmunology at a Glance2001CornwallBlackwell – reference: Cutello V, Narzisi G, Nicosia G, Pavone M (2005) Clonal selection algorithms: a comparative case study using effective mutation potentials. In: Fourth international conference on artificial immune systems (ICARIS-2005), Banff, Canada, pp 13–28 – reference: Cutello V, Nicosia G (2004) The clonal selection principle for in silico and in vitro computing. In: de Castro LN, Von Zuben FJ (eds) Recent developments in biologically inspired computing. Idea Group Publishing, pp 104–145 – reference: Male D, Brostoff J, Roitt I, Rotth DB (2006) Immunolohy, 7th edn. Mosby – reference: Garrett SM (2004) Parameter-free, adaptive clonal selection. In: 2004 Congress on evolutionary computation (CEC 2004), Portland, USA, pp 1052–1058 – reference: Sompayrac L (1999) How the immune system works. Blackwell, Cornwall – reference: Watkins A, Timmis J (2004) Exploiting parallelism inherent in AIRS, an artificial immune classifier. In: Third international conference on artificial immune systems (ICARIS-2004), Catania, Italy, pp 427–438 – reference: Watkins A (2005) Exploiting immunological metaphors in the development of serial, parallel, and distributed learning algorithms. University of Kent, Canterbury – reference: Ko AWY, Lau HYK, Lau TL (2005) General suppression control framework: application in selfbalancing robots. In: Fourth international conference on artificial immune systems (ICARIS-2005), Banff, Canada, pp 375–388 – reference: Greensmith J, Aickelin U, Cayzer S (2005) Introducing dendritic cells as a novel immune-inspired algorithm for anomaly detection. In: Fourth international conference on artificial immune systems (ICARIS-2005), Banff, Canada, pp 153–167 – reference: Ko AWY, Lau HYK, Lau TL (2004) An immuno control framework for decentralized mechatronic control. In: Third international conference on artificial immune systems (ICARIS-2004), Catania, Italy, pp 91–105 – reference: Vesterstrom J, Thomsen R (2004) A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. In: 2004 Congress on evolutionary computation (CEC 2004), Portland, USA – reference: Burnet FM (1959) The clonal selection theory of acquired immunity. Cambridge University Press, Cambridge – reference: VenterGSobieszczanski-SobieskiJA parallel particle swarm optimization algorithm accelerated by asynchronous evaluationsJ Aerosp Comput Inf Commun20063312313710.2514/1.17873 – reference: Brownlee J (2007) Optimization Algorithm Toolkit (optalgtoolkit) Version 1.4. In: Optimization Algorithm Toolkit (OAT). Available via Sourceforge.net. http://sourceforge.net/projects/optalgtoolkit. Accessed on 12 February 2008 – reference: DasguptaDAdvances in artificial immune systemsIEEE Comput Intell Mag200614049 – reference: Kim J, Bentley PJ (1999) The human immune system and network intrusion detection. In: Seventh European congress on intelligent techniques and soft computing (EUFIT’99), Aachen, Germany – reference: MakKLLauPSKOrder pickings in an AS/RS with multiple I/O stations using an artificial immune system with aging antibodiesEng Lett200816122130 – reference: Liu P, Lau F, Lewis MJ, Wang C-L (2002) A new asynchronous parallel evolutionary algorithm for function optimization. In: Seventh international conference on parallel problem solving from nature, Granada, Spain, pp 401–410 – reference: JerneNKTowards a network theory of the immune systemAnnu Immunol1974125C373389 – reference: Timmis J, Knight T, de Castro LN, Hart E (2004) An overview of artificial immune systems. In: Paton R, Bolouri H, Holcombe M, Parish JH, Tateson R (eds) Computation in cells and tissues: perspectives and tools of thought. Springer, Heidelberg, pp 51–86 – reference: HartETimmisJApplication areas of AIS: the past, the present and the futureAppl Soft Comput2008819120110.1016/j.asoc.2006.12.004 – reference: Lau HYK, Wong VWK (2004) A strategic behavior-based intelligent transport system with artificial immune system. In: IEEE international conference on systems, man and cybernetics, 2004, Hague, The Netherlands, pp 3909–3914 – reference: de Castro LN, Von Zuben FJ (2002) An artificial immune network for multimodal function optimization on dynamic environments. In: 2002 congress on evolutionary computation (CEC 2002), Honolulu, USA, pp 289–296 – reference: CutelloVNicosiaGPaviaEA parallel immune algorithm for global optimizationAdv Soft Comput2006546747510.1007/3-540-33521-8_51 – reference: Kepner J (2001) MatlabMPI. In: Parallel programming with MatlabMPI. Available via Lincoln Laboratory, Massachusetts Institute of Technology. http://www.ll.mit.edu/MatlabMPI. Accessed on 1 April 2007 – reference: RandallMLewisAA parallel implementation of ant colony optimizationParallel Distrib Comput200262142114321063.6809510.1006/jpdc.2002.1854 – reference: Ko AWY, Lau HYK, Lau TL (2004) A general suppression framework for distributed control. In: Tenth IEEE international conference on methods and models in automation and robotics, Miedzyzdroje, Poland – reference: Watkins A, Bi X, Phadke A (2003) Parallelizing an immune-inspired algorithm for efficient pattern recognition. intelligent engineering systems through artificial Neural Networks: Smart Engineering System Design: Neural Networks, Fuzzy Logic, Evolutionary Programming, Complex Systems and Artificial Life 13: 225–230 – reference: Wang X, Gao XZ, Ovaska SJ (2004) Artificial immune optimization methods and applications—a survey. In: IEEE international conference on systems, man and cybernetics, 2004, Hague, The Netherlands, pp 3415–3420 – reference: Zhu C, Zhao B, Ye B, Yijia C (2005) An improved immune algorithm and its evaluation of optimization efficiency. In: International conference on natural computation (ICNC 2005), Changsha, China, pp 895–904 – reference: Cutello V, Nicosia G, Pavone M (2004) Exploring the capability of immune algorithms: a characterization of hypermutation operators. In: Third international conference on artificial immune systems (ICARIS-2004), Catania, Italy, pp 263–276 – reference: Kelsey J, Timmis J (2003) Immune inspired somatic contiguous hypermutation for function optimisation. In: Genetic and evolutionary computation conference (GECCO 2003), Chicago, USA, pp 207–218 – reference: de Castro LN (2001) Demo file for Matlab. In: Artificial immune systems. Available via Department of Computer Engineering and Industrial Automation, State University of Campinas. http://www.dca.fee.unicamp.br/~lnunes/immune.html. 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| Title | A parallel immune optimization algorithm for numeric function optimization |
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