{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T22:21:55Z","timestamp":1743114115625,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811534249"},{"type":"electronic","value":"9789811534256"}],"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\/978-981-15-3425-6_58","type":"book-chapter","created":{"date-parts":[[2020,4,1]],"date-time":"2020-04-01T19:02:58Z","timestamp":1585767778000},"page":"727-738","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Bacterial Foraging Framework for\u00a0Agent Based Modeling"],"prefix":"10.1007","author":[{"given":"Mijat","family":"Kustudic","sequence":"first","affiliation":[]},{"given":"Niu","family":"Ben","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,4,2]]},"reference":[{"key":"58_CR1","unstructured":"Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks, vol. 4, pp. 1942\u20131948 (1995)"},{"key":"58_CR2","doi-asserted-by":"crossref","unstructured":"Xiaohui, Y., Niu, B.: Hydrologic Cycle Optimization Part I: Background and Theory, Advances in Swarm Intelligence, pp. 341\u2013349 (2018)","DOI":"10.1007\/978-3-319-93815-8_33"},{"key":"58_CR3","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1023\/A:1008202821328","volume":"11","author":"R Storn","year":"1997","unstructured":"Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11, 341\u2013359 (1997)","journal-title":"J. Global Optim."},{"issue":"10","key":"58_CR4","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.compstruc.2012.07.010","volume":"110\u2013111","author":"H Eskandar","year":"2012","unstructured":"Eskandar, H., Sadollah, A., Bahreininejad, A., et al.: Water cycle algorithm - a novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput. Struct. 110\u2013111(10), 151\u2013166 (2012)","journal-title":"Comput. Struct."},{"issue":"1","key":"58_CR5","first-page":"108","volume":"214","author":"K Dervis","year":"2009","unstructured":"Dervis, K., Bahriye, A.: A comparative study of Artificial Bee Colony algorithm. Appl. Math. Comput. 214(1), 108\u2013132 (2009)","journal-title":"Appl. Math. Comput."},{"issue":"1","key":"58_CR6","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1038\/scientificamerican0792-66","volume":"267","author":"J Holland","year":"1992","unstructured":"Holland, J.: Genetic algorithms. Sci. Am. 267(1), 66\u201372 (1992)","journal-title":"Sci. Am."},{"issue":"4","key":"58_CR7","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1109\/MCI.2006.329691","volume":"1","author":"M Dorigo","year":"2006","unstructured":"Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization artificial ants as a computational intelligence technique. IEEE Comput. Intell. Mag. 1(4), 28\u201339 (2006)","journal-title":"IEEE Comput. Intell. Mag."},{"key":"58_CR8","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1109\/MCS.2002.1004010","volume":"22","author":"KM Passino","year":"2002","unstructured":"Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. Mag. 22, 52\u201367 (2002)","journal-title":"IEEE Control Syst. Mag."},{"issue":"11","key":"58_CR9","first-page":"257","volume":"7","author":"B Niu","year":"2011","unstructured":"Niu, B., Fan, Y., Wang, H., Li, L., Wang, X.: Novel bacterial foraging optimization with time-varying chemotaxis step. Int. J. Artif. Intell. 7(11), 257\u2013273 (2011)","journal-title":"Int. J. Artif. Intell."},{"key":"58_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1007\/11550822_49","volume-title":"Artificial Neural Networks: Biological Inspirations \u2013 ICANN 2005","author":"C Fernandes","year":"2005","unstructured":"Fernandes, C., Ramos, V., Rosa, A.C.: Varying the population size of artificial foraging swarms on time varying landscapes. In: Duch, W., Kacprzyk, J., Oja, E., Zadro\u017cny, S. (eds.) ICANN 2005. LNCS, vol. 3696, pp. 311\u2013316. Springer, Heidelberg (2005). \nhttps:\/\/doi.org\/10.1007\/11550822_49"},{"key":"58_CR11","doi-asserted-by":"crossref","unstructured":"Chen, H.N., Zhu, Y.L., Hu, K.Y.: Adaptive bacterial foraging algorithm. Abstract Appl. Anal. 2011 (2011). Article ID 108269","DOI":"10.1155\/2011\/108269"},{"key":"58_CR12","unstructured":"Tang, W.J., Wu, Q.H., Saunders, J.R.: Bacterial foraging algorithm for dynamic environments. In: IEEE Congress on Evolutionary Computation (2006)"},{"key":"58_CR13","unstructured":"Morrison, R.W., De Jong, K.A.: A test problem generator for non - stationary environments. In: Proceedings of the 1999 IEEE Congress on Evolutionary Computation, pp. 2047\u20132053. IEEE Press (1999)"},{"key":"58_CR14","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4615-0911-0","volume-title":"Evolutionary Optimization in Dynamic Environments","author":"J Branke","year":"2002","unstructured":"Branke, J.: Evolutionary Optimization in Dynamic Environments. Kluwer Academic Publishers, Massachusetts (2002)"},{"key":"58_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"556","DOI":"10.1007\/11751540_59","volume-title":"Computational Science and Its Applications - ICCSA 2006","author":"WJ Tang","year":"2006","unstructured":"Tang, W.J., Wu, Q.H., Saunders, J.R.: A novel model for bacterial foraging in varying environments. In: Gavrilova, M., et al. (eds.) ICCSA 2006. LNCS, vol. 3980, pp. 556\u2013565. Springer, Heidelberg (2006). \nhttps:\/\/doi.org\/10.1007\/11751540_59"},{"key":"58_CR16","doi-asserted-by":"crossref","unstructured":"Daas, M.S., Batouche, M.: Multi-bacterial foraging optimization for dynamic environments. In: International Conference of Soft Computing and Pattern Recognition (2014)","DOI":"10.1109\/SOCPAR.2014.7008012"},{"issue":"4","key":"58_CR17","first-page":"4","volume":"223","author":"J Conway","year":"1970","unstructured":"Conway, J.: The game of life. Sci. Am. 223(4), 4 (1970)","journal-title":"Sci. Am."},{"issue":"2","key":"58_CR18","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1177\/0049124113506405","volume":"44","author":"E Bruch","year":"2015","unstructured":"Bruch, E., Atwell, J.: Agent-based models in empirical social research. Sociol. Meth. Res. 44(2), 186\u2013221 (2015)","journal-title":"Sociol. Meth. Res."},{"volume-title":"Handbook of Computational Economics: Agent-Based Computational Economics","year":"2006","key":"58_CR19","unstructured":"Tesfatsion, L., Judd, K.L. (eds.): Handbook of Computational Economics: Agent-Based Computational Economics, vol. 2. Elsevier, Amsterdam (2006)"},{"issue":"4","key":"58_CR20","doi-asserted-by":"publisher","first-page":"864","DOI":"10.1086\/426412","volume":"110","author":"LE Cederman","year":"2005","unstructured":"Cederman, L.E.: Computational models of social forms: advancing generative process theory. Am. J. Sociol. 110(4), 864\u2013893 (2005)","journal-title":"Am. J. Sociol."},{"key":"58_CR21","doi-asserted-by":"crossref","unstructured":"Wang, C., Wang, J.: A modified floor field model combined with risk field for pedestrian simulation. Math. Probl. Eng. 1(10) (2016)","DOI":"10.1155\/2016\/9653860"},{"issue":"2\u20133","key":"58_CR22","first-page":"393","volume":"57","author":"LE Van","year":"2015","unstructured":"Van, L.E., Lijesen, M.: Agents playing Hotelling\u2019s game: an agent-based approach to a game theoretic model. Ann. Reg. Sci. 57(2\u20133), 393\u2013411 (2015)","journal-title":"Ann. Reg. Sci."},{"key":"58_CR23","unstructured":"Tecchia, F., Loscos, C., Conroy-Dalton, R., Chrysanthou, Y.L.: Agent Behavior Simulator (ABS): a platform for urban behavior development. In: First International Game Technology Conference and Idea Expo (GTEC 2001) (2001)"}],"container-title":["Communications in Computer and Information Science","Bio-inspired Computing: Theories and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-15-3425-6_58","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,4,2]],"date-time":"2020-04-02T01:36:20Z","timestamp":1585791380000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-981-15-3425-6_58"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9789811534249","9789811534256"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-15-3425-6_58","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":"2 April 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BIC-TA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Bio-Inspired Computing: Theories and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Zhengzhou","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":"22 November 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 November 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bicta2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2019.bicta.org","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":"197","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":"121","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":"0","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":"61% - 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":"3","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}