{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T14:46:46Z","timestamp":1726066006787},"publisher-location":"Singapore","reference-count":20,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811527661"},{"type":"electronic","value":"9789811527678"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-981-15-2767-8_22","type":"book-chapter","created":{"date-parts":[[2020,1,25]],"date-time":"2020-01-25T20:02:35Z","timestamp":1579982555000},"page":"234-248","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Constrained Optimization via Quantum Genetic Algorithm for Task Scheduling Problem"],"prefix":"10.1007","author":[{"given":"Zihan","family":"Yan","sequence":"first","affiliation":[]},{"given":"Hong","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Huiming","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Zexi","family":"Deng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,1,26]]},"reference":[{"key":"22_CR1","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1016\/j.ins.2014.02.122","volume":"270","author":"Y Xu","year":"2014","unstructured":"Xu, Y., Li, K., Hu, J., Li, K.: A genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues. Inf. Sci. 270, 255\u2013287 (2014)","journal-title":"Inf. Sci."},{"key":"22_CR2","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1007\/978-3-642-01085-9_16","volume-title":"Foundations of Computational Intelligence Volume 3","author":"Fatma A. Omara","year":"2009","unstructured":"Omara, F.A., Arafa, M.M.: Genetic algorithms for task scheduling problem. In: Abraham, A., Hassanien, A.E., Siarry, P., Engelbrecht, A. (eds.) Foundations of Computational Intelligence Volume 3. SCI, vol. 203, pp. 479\u2013507. Springer, Heidelberg (2009). \nhttps:\/\/doi.org\/10.1007\/978-3-642-01085-9_16"},{"issue":"3","key":"22_CR3","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1109\/TSMCA.2009.2013333","volume":"39","author":"B Tessema","year":"2009","unstructured":"Tessema, B., Yen, G.G.: An adaptive penalty formulation for constrained evolutionary optimization. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 39(3), 565\u2013578 (2009)","journal-title":"IEEE Trans. Syst. Man Cybern. Part A Syst. Hum."},{"issue":"3","key":"22_CR4","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1109\/71.993206","volume":"13","author":"H Topcuoglu","year":"2002","unstructured":"Topcuoglu, H., Hariri, S., Wu, M.-Y.: Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans. Parallel Distrib. Syst. 13(3), 260\u2013274 (2002)","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"22_CR5","doi-asserted-by":"crossref","unstructured":"Amini, A., Wah, T.Y., Saybani, M.R., Yazdi, S.R.A.S.: A study of density-grid based clustering algorithms on data streams. In: 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), vol. 3, pp. 1652\u20131656. IEEE (2011)","DOI":"10.1109\/FSKD.2011.6019867"},{"issue":"8","key":"22_CR6","doi-asserted-by":"publisher","first-page":"1146","DOI":"10.1016\/j.jpdc.2008.04.001","volume":"68","author":"K Shin","year":"2008","unstructured":"Shin, K., Cha, M., Jang, M., Jung, J., Yoon, W., Choi, S.: Task scheduling algorithm using minimized duplications in homogeneous systems. J. Parallel Distrib. Comput. 68(8), 1146\u20131156 (2008)","journal-title":"J. Parallel Distrib. Comput."},{"issue":"2","key":"22_CR7","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1109\/4235.996017","volume":"6","author":"K Deb","year":"2002","unstructured":"Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182\u2013197 (2002)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"22_CR8","unstructured":"Ishibuchi, H., Tsukamoto, N., Nojima, Y.: Evolutionary many-objective optimization: a short review. In: 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp. 2419\u20132426. IEEE (2008)"},{"key":"22_CR9","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.asoc.2015.09.036","volume":"38","author":"Y-C Chuang","year":"2016","unstructured":"Chuang, Y.-C., Chen, C.-T., Hwang, C.: A simple and efficient real-coded genetic algorithm for constrained optimization. Appl. Soft Comput. 38, 87\u2013105 (2016)","journal-title":"Appl. Soft Comput."},{"key":"22_CR10","unstructured":"Joines, J.A., Houck, C.R.: On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GA\u2019s. In: Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence, pp. 579\u2013584. IEEE (1994)"},{"issue":"19","key":"22_CR11","doi-asserted-by":"publisher","first-page":"6777","DOI":"10.1016\/j.eswa.2015.04.070","volume":"42","author":"J Matias","year":"2015","unstructured":"Matias, J., et al.: Adaptive penalty and barrier function based on fuzzy logic. Expert Syst. Appl. 42(19), 6777\u20136783 (2015)","journal-title":"Expert Syst. Appl."},{"key":"22_CR12","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.ins.2013.04.001","volume":"241","author":"C-H Lin","year":"2013","unstructured":"Lin, C.-H.: A rough penalty genetic algorithm for constrained optimization. Inf. Sci. 241, 119\u2013137 (2013)","journal-title":"Inf. Sci."},{"key":"22_CR13","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1007\/978-3-319-76351-4_13","volume-title":"Hybrid Intelligent Systems","author":"R Bangroo","year":"2018","unstructured":"Bangroo, R., Kumar, N., Sharma, R.: A model for multi-processor task scheduling problem using quantum genetic algorithm. In: Abraham, A., Muhuri, P.K., Muda, A.K., Gandhi, N. (eds.) HIS 2017. AISC, vol. 734, pp. 126\u2013135. Springer, Cham (2018). \nhttps:\/\/doi.org\/10.1007\/978-3-319-76351-4_13"},{"issue":"1","key":"22_CR14","first-page":"62","volume":"20","author":"J Yang","year":"2003","unstructured":"Yang, J., Li, B., Zhuang, Z.: Research of quantum genetic algorith and its application in blind source separation. J. Electron. 20(1), 62\u201368 (2003)","journal-title":"J. Electron."},{"issue":"4","key":"22_CR15","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1109\/4235.797969","volume":"3","author":"E Zitzler","year":"1999","unstructured":"Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans. Evol. Comput. 3(4), 257\u2013271 (1999)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"6","key":"22_CR16","doi-asserted-by":"publisher","first-page":"1796","DOI":"10.1109\/TPDS.2015.2462835","volume":"27","author":"S Chen","year":"2015","unstructured":"Chen, S., Li, Z., Yang, B., Rudolph, G.: Quantum-inspired hyper-heuristics for energy-aware scheduling on heterogeneous computing systems. IEEE Trans. Parallel Distrib. Syst. 27(6), 1796\u20131810 (2015)","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"22_CR17","doi-asserted-by":"crossref","unstructured":"Gandhi, T., Alam, T., et al.: Quantum genetic algorithm with rotation angle refinement for dependent task scheduling on distributed systems. In: 2017 Tenth International Conference on Contemporary Computing (IC3), pp. 1\u20135. IEEE (2017)","DOI":"10.1109\/IC3.2017.8284295"},{"issue":"6","key":"22_CR18","doi-asserted-by":"publisher","first-page":"516","DOI":"10.1109\/TPDS.2005.71","volume":"16","author":"Z Shao","year":"2005","unstructured":"Shao, Z., Zhuge, Q., Xue, C., Sha, E.-M.: Efficient assignment and scheduling for heterogeneous DSP systems. IEEE Trans. Parallel Distrib. Syst. 16(6), 516\u2013525 (2005)","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"22_CR19","doi-asserted-by":"crossref","unstructured":"Asafuddoula, M., Ray, T., Sarker, R., Alam, K.: An adaptive constraint handling approach embedded MOEA\/D. In: 2012 IEEE Congress on Evolutionary Computation, pp. 1\u20138. IEEE (2012)","DOI":"10.1109\/CEC.2012.6252868"},{"issue":"8","key":"22_CR20","doi-asserted-by":"publisher","first-page":"1374","DOI":"10.1109\/TPDS.2010.208","volume":"22","author":"YC Lee","year":"2010","unstructured":"Lee, Y.C., Zomaya, A.Y.: Energy conscious scheduling for distributed computing systems under different operating conditions. IEEE Trans. Parallel Distrib. Syst. 22(8), 1374\u20131381 (2010)","journal-title":"IEEE Trans. Parallel Distrib. Syst."}],"container-title":["Communications in Computer and Information Science","Parallel Architectures, Algorithms and Programming"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-15-2767-8_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,1,25]],"date-time":"2020-01-25T20:06:33Z","timestamp":1579982793000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-981-15-2767-8_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9789811527661","9789811527678"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-981-15-2767-8_22","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"26 January 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAAP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Parallel Architectures, Algorithms and Programming","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guangzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 December 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"paap2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/sdcs.sysu.edu.cn\/paap2019","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"121","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"39","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"8","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"32% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}