{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,23]],"date-time":"2025-04-23T05:10:02Z","timestamp":1745385002445,"version":"3.40.4"},"publisher-location":"India","reference-count":19,"publisher":"Springer India","isbn-type":[{"type":"print","value":"9788132210375"},{"type":"electronic","value":"9788132210382"}],"license":[{"start":{"date-parts":[[2012,12,4]],"date-time":"2012-12-04T00:00:00Z","timestamp":1354579200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2012,12,4]],"date-time":"2012-12-04T00:00:00Z","timestamp":1354579200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2013]]},"DOI":"10.1007\/978-81-322-1038-2_26","type":"book-chapter","created":{"date-parts":[[2012,12,3]],"date-time":"2012-12-03T20:49:11Z","timestamp":1354567751000},"page":"299-314","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Diversity Measures in Artificial Bee Colony"],"prefix":"10.1007","author":[{"given":"Harish","family":"Sharma","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jagdish Chand","family":"Bansal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K. V.","family":"Arya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2012,12,4]]},"reference":[{"key":"26_CR1","unstructured":"M. Dorigo and G. Di Caro. Ant colony optimization: a new meta-heuristic. In Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on, volume 2. IEEE, 1999."},{"key":"26_CR2","doi-asserted-by":"crossref","unstructured":"J. Kennedy and R. Eberhart. Particle swarm optimization. In Neural Networks, 1995. Proceedings., IEEE International Conference on, volume 4, pages 1942\u20131948. IEEE, 1995.","DOI":"10.1109\/ICNN.1995.488968"},{"key":"26_CR3","unstructured":"K.V. Price, R.M. Storn, and J.A. Lampinen. Differential evolution: a practical approach to global optimization. Springer Verlag, 2005."},{"key":"26_CR4","unstructured":"J. Vesterstrom and R. Thomsen. A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. In Evolutionary Computation, 2004. CEC2004. Congress on, volume 2, pages 1980\u20131987. IEEE, 2004."},{"issue":"3","key":"26_CR5","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1109\/MCS.2002.1004010","volume":"22","author":"K.M. Passino","year":"2002","unstructured":"K.M. Passino. Biomimicry of bacterial foraging for distributed optimization and control. Control Systems Magazine, IEEE, 22(3):52\u201367, 2002.","journal-title":"Control Systems Magazine, IEEE"},{"key":"26_CR6","unstructured":"D. Karaboga. An idea based on honey bee swarm for numerical optimization. Techn. Rep. TR06, Erciyes Univ. Press, Erciyes, 2005."},{"key":"26_CR7","first-page":"02","volume":"67","author":"A.P. Engelbrecht","year":"2005","unstructured":"A.P. Engelbrecht. Fundamentals of computational swarm intelligence. Recherche, 67:02, 2005.","journal-title":"Recherche"},{"key":"26_CR8","unstructured":"TM Blackwell. Particle swarms and population diversity i: Analysis. In GECCO, pages 103\u2013107, 2003."},{"key":"26_CR9","first-page":"15","volume-title":"A survey of ant colony and particle swarm meta-heuristics and their application to discrete optimization problems","author":"T Hendtlass","year":"2001","unstructured":"T. Hendtlass and M. Randall. A survey of ant colony and particle swarm meta-heuristics and their application to discrete optimization problems. In Proceedings of the Inaugural Workshop on, Artificial Life, pp. 15\u201325, 2001."},{"key":"26_CR10","doi-asserted-by":"crossref","unstructured":"T. Krink, J.S. VesterstrOm, and J. Riget. Particle swarm optimisation with spatial particle extension. In Evolutionary Computation, 2002. CEC\u201902. Proceedings of the 2002 Congress on, volume 2, pages 1474\u20131479. IEEE, 2002.","DOI":"10.1109\/CEC.2002.1004460"},{"key":"26_CR11","doi-asserted-by":"crossref","unstructured":"J.S. Vesterstrom, J. Riget, and T. Krink. Division of labor in particle swarm optimisation. In Evolutionary Computation, 2002. CEC\u201902. Proceedings of the 2002 Congress on, volume 2, pages 1570\u20131575. IEEE, 2002.","DOI":"10.1109\/CEC.2002.1004476"},{"key":"26_CR12","unstructured":"A. Ratnaweera, S. Halgamuge, and H. Watson. Particle swarm optimization with self-adaptive acceleration coefficients. In Proc. 1st Int. Conf. Fuzzy Syst. Knowl. Discovery, pages 264\u2013268, 2003."},{"key":"26_CR13","doi-asserted-by":"crossref","unstructured":"O. Olorunda and AP Engelbrecht. Measuring exploration\/exploitation in particle swarms using swarm diversity. In Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on, pages 1128\u20131134. IEEE, 2008.","DOI":"10.1109\/CEC.2008.4630938"},{"issue":"1","key":"26_CR14","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.amc.2009.03.090","volume":"214","author":"D. Karaboga","year":"2009","unstructured":"D. Karaboga and B. Akay. A comparative study of artificial bee colony algorithm. Applied Mathematics and Computation, 214(1):108\u2013132, 2009.","journal-title":"Applied Mathematics and Computation"},{"key":"26_CR15","unstructured":"J. Riget and J.S. Vesterstr\u00f8m. A diversity-guided particle swarm optimizer-the arpso. Dept. Comput. Sci., Univ. of Aarhus, Aarhus, Denmark, Tech. Rep, 2:2002, 2002."},{"key":"26_CR16","doi-asserted-by":"crossref","unstructured":"K. Diwold, A. Aderhold, A. Scheidler, and M. Middendorf. Performance evaluation of artificial bee colony optimization and new selection schemes. Memetic Computing, pages 1\u201314, 2011.","DOI":"10.1007\/s12293-011-0065-8"},{"key":"26_CR17","doi-asserted-by":"crossref","unstructured":"M. El-Abd. Performance assessment of foraging algorithms vs. evolutionary algorithms. Information Sciences, 2011.","DOI":"10.1016\/j.ins.2011.09.005"},{"key":"26_CR18","doi-asserted-by":"crossref","unstructured":"D. Karaboga and B. Akay. A modified artificial bee colony (abc) algorithm for constrained optimization problems. Applied Soft Computing, 2010.","DOI":"10.1016\/j.asoc.2010.12.001"},{"key":"26_CR19","doi-asserted-by":"crossref","unstructured":"B. Akay and D. Karaboga. A modified artificial bee colony algorithm for real-parameter optimization. Information Sciences, 2010.","DOI":"10.4249\/scholarpedia.6915"}],"container-title":["Advances in Intelligent Systems and Computing","Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012)"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-81-322-1038-2_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,23]],"date-time":"2025-04-23T04:29:09Z","timestamp":1745382549000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-81-322-1038-2_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,12,4]]},"ISBN":["9788132210375","9788132210382"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-81-322-1038-2_26","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"type":"print","value":"2194-5357"},{"type":"electronic","value":"2194-5365"}],"subject":[],"published":{"date-parts":[[2012,12,4]]},"assertion":[{"value":"4 December 2012","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}