{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T06:56:20Z","timestamp":1763535380473,"version":"3.41.2"},"reference-count":24,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2016,2,1]],"date-time":"2016-02-01T00:00:00Z","timestamp":1454284800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,2,1]]},"abstract":"<jats:sec>\n               <jats:title content-type=\"abstract-heading\">Purpose<\/jats:title>\n               <jats:p> \u2013 The purpose of this paper is to solve the problem that the standard particle swarm optimization (PSO) algorithm has a low success rate when applied to the optimization of multi-dimensional and multi-extreme value functions, the authors would introduce the extended memory factor to the PSO algorithm. Furthermore, the paper aims to improve the convergence rate and precision of basic artificial fish swarm algorithm (FSA), a novel FSA optimized by PSO algorithm with extended memory (PSOEM-FSA) is proposed. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Design\/methodology\/approach<\/jats:title>\n               <jats:p> \u2013 In PSOEM-FSA, the extended memory for PSO is introduced to store each particle\u2019 historical information comprising of recent places, personal best positions and global best positions, and a parameter called extended memory effective factor is employed to describe the importance of extended memory. Then, stability region of its deterministic version in a dynamic environment is analyzed by means of the classic discrete control theory. Furthermore, the extended memory factor is applied to five kinds of behavior pattern for FSA, including swarming, following, remembering, communicating and searching. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Findings<\/jats:title>\n               <jats:p> \u2013 The paper proposes a new intelligent algorithm. On the one hand, this algorithm makes the fish swimming have the characteristics of the speed of inertia; on the other hand, it expands behavior patterns for the fish to choose in the search process and achieves higher accuracy and convergence rate than PSO-FSA, owning to extended memory beneficial to direction and purpose during search. Simulation results verify that these improvements can reduce the blindness of fish search process, improve optimization performance of the algorithm. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Research limitations\/implications<\/jats:title>\n               <jats:p> \u2013 Because of the chosen research approach, the research results may lack persuasion. In the future study, the authors will conduct more experiments to understand the behavior of PSOEM-FSA. In addition, there are mainly two aspects that the performance of this algorithm could be further improved. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Practical implications<\/jats:title>\n               <jats:p> \u2013 The proposed algorithm can be used to many practical engineering problems such as tracking problems. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Social implications<\/jats:title>\n               <jats:p> \u2013 The authors hope that the PSOEM-FSA can increase a branch of FSA algorithm, and enrich the content of the intelligent algorithms to some extent. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title>\n               <jats:p> \u2013 The novel optimized FSA algorithm proposed in this paper improves the convergence speed and searching precision of the ordinary FSA to some degree.<\/jats:p>\n            <\/jats:sec>","DOI":"10.1108\/k-09-2014-0198","type":"journal-article","created":{"date-parts":[[2016,1,20]],"date-time":"2016-01-20T10:12:32Z","timestamp":1453284752000},"page":"210-222","source":"Crossref","is-referenced-by-count":28,"title":["An improved artificial fish swarm algorithm optimized by particle swarm optimization algorithm with extended memory"],"prefix":"10.1108","volume":"45","author":[{"given":"Qichang","family":"Duan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingxuan","family":"Mao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pan","family":"Duan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bei","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"key":"key2020121802260262800_b2","doi-asserted-by":"crossref","unstructured":"Banks, A.\n               , \n                  Vincent, J.\n                and \n                  Anyakoha, C.\n                (2007), \u201cA review of particle swarm optimization. Part I: background and development\u201d, \n                  Natural Computing\n               , Vol. 6 No. 4, pp. 467-484.","DOI":"10.1007\/s11047-007-9049-5"},{"key":"key2020121802260262800_b3","doi-asserted-by":"crossref","unstructured":"Banks, A.\n               , \n                  Vincent, J.\n                and \n                  Anyakoha, C.\n                (2008), \u201cReview of particle swarm optimization. Part II: hybridization, combinatorial, multicriteria and constrained optimization, and indicative applications\u201d, \n                  Natural Computing\n               , Vol. 7 No. 1, pp. 109-124.","DOI":"10.1007\/s11047-007-9050-z"},{"key":"key2020121802260262800_b14","doi-asserted-by":"crossref","unstructured":"Bing, D.\n                and \n                  Wen, D.\n                (2010), \u201cScheduling arrival aircrafts on multi-runway based on an improved artificial fish swarm algorithm\u201d, International Conference on Computational and Information Sciences, pp. 499-502.","DOI":"10.1109\/ICCIS.2010.338"},{"key":"key2020121802260262800_b17","unstructured":"Duan, Q.C.\n               , \n                  Huang, D.W.\n               , \n                  Lei, L.\n                and \n                  Duan, P.\n                (2011), \u201cSimulation analysis of particle swarm optimization algorithm with extended memory\u201d, \n                  Control and Decision\n               , Vol. 26 No. 7, pp. 1087-1100."},{"key":"key2020121802260262800_b16","unstructured":"Duan, Q.C.\n               , \n                  Tang, R.L.\n               , \n                  Xu, H.Y.\n                and \n                  Li, W.\n                (2013), \u201cSimulation analysis of the fish swarm algorithm optimized by PSO\u201d, \n                  Control and Decision\n               , Vol. 28 No. 9, pp. 1436-1440."},{"key":"key2020121802260262800_b15","doi-asserted-by":"crossref","unstructured":"Eberhart, R.C.\n                and \n                  Kennedy, J.\n                (1995), \u201cA new optimizer using particle swarm theory\u201d, Proceeding of the 6th International Symposium on Micro-Machine and Human Science, Nagoya, pp. 39-43.","DOI":"10.1109\/MHS.1995.494215"},{"key":"key2020121802260262800_b11","unstructured":"Hu, J.\n               , \n                  Zeng., X.\n                and \n                  Xiao, J.\n                (2010), \u201cArtificial fish swarm algorithm for function optimization\u201d, International Conference on Information Engineering and Computer Science, pp. 1-4."},{"key":"key2020121802260262800_b152","doi-asserted-by":"crossref","unstructured":"Huang, Z.H.\n                and \n                  Chen, Y.D.\n                (2013), \u201cAn improved artificial fish swarm algorithm based on hybrid behavior selection\u201d, International Journal of Control and Automation, Vol. 6 No. 5, pp. 103-116.","DOI":"10.14257\/ijca.2013.6.5.10"},{"key":"key2020121802260262800_b1","doi-asserted-by":"crossref","unstructured":"Kennedy, J.\n                and \n                  Eberhart, R.\n                (1995), \u201cParticle swarm optimization\u201d, Proceedings of IEEE International Conference on Neural Networks, Perth, pp. 1942-1948.","DOI":"10.1109\/ICNN.1995.488968"},{"key":"key2020121802260262800_b8","unstructured":"Li, X.L.\n               , \n                  Shao, Z.J.\n                and \n                  Qian, J.X.\n                (2002), \u201cAn optimizing method based on autonomous animats: fish-swarm algorithm\u201d, \n                  Systems Engineering-Theory & Practice\n               , Vol. 22 No. 11, pp. 32-38."},{"key":"key2020121802260262800_b12","unstructured":"Luo, Y.\n               , \n                  Wei, W.\n                and \n                  Wang, S.X.\n                (2010), \u201cThe optimization of PID controller parameters based on an improved artificial fish swarm algorithm\u201d, Proceeding of the 3rd International Workshop on Advanced Computational Intelligence, pp. 328-332."},{"key":"key2020121802260262800_b9","unstructured":"Reza, A.\n                (2014), \u201cEmpirical study of artificial fish swarm algorithm\u201d, \n                  International Journal of Computing, Communications and Networking\n               , Vol. 3 No. 1, pp. 01-07."},{"key":"key2020121802260262800_b153","doi-asserted-by":"crossref","unstructured":"Song, C.Y.\n               , \n                  Jiang, J.Q.\n               , \n                  Bai, S.Q.\n                and \n                  Bao, L.Y.\n                (2013), \u201cAn improved artificial fish swarm algorithm for cutting stock problem\u201d, \n                  Ninth International Conference on Natural Computation (ICNC)\n               , pp. 501-505.","DOI":"10.1109\/ICNC.2013.6818028"},{"key":"key2020121802260262800_b4","unstructured":"Song, L.W.\n               , \n                  Peng, M.F.\n                and \n                  Tian, C.L.\n                (2012), \u201cDiagnosis for analog circuits based on PSO-RBF neural network\u201d, \n                  Computer Application Research\n               , Vol. 29 No. 1, pp. 72-75."},{"key":"key2020121802260262800_b13","unstructured":"Song, X.\n               , \n                  Wang, C.\n               , \n                  Wang, J.\n                and \n                  Zhang, B.\n                (2010), \u201cA hierarchical routing protocol based on AFSO algorithm for WSN\u201d, International Conference on Computer Design and Applications, pp. 635-639."},{"key":"key2020121802260262800_b20","doi-asserted-by":"crossref","unstructured":"Tsai, H.C.\n                and \n                  Lin, Y.H.\n                (2011), \u201cModi\ufb01cation of the \ufb01sh swarm algorithm with particle swarm optimization formulation and communication behavior\u201d, \n                  Applied Soft Computing\n               , Vol. 11 No. 8, pp. 5367-5374.","DOI":"10.1016\/j.asoc.2011.05.022"},{"key":"key2020121802260262800_b19","unstructured":"Wang, C.R.\n               , \n                  Zhou, C.L.\n                and \n                  Ma, J.W.\n                (2005), \u201cAn improved arti\ufb01cial \ufb01sh-swarm algorithm and its application in feed-forward neural networks\u201d, Proceeding of the 4th International Conference on Machine Learning and Cybernetics (ICMLC\u201905), Guangzhou, August, pp. 2890-2894."},{"key":"key2020121802260262800_b5","doi-asserted-by":"crossref","unstructured":"Wang, G.G.\n               , \n                  Gandomi, A.H.\n                and \n                  Alavi, A.H.\n                (2013), \u201cA chaotic particle-swarm krill herd algorithm for global numerical optimization\u201d, \n                  Kybernetes\n               , Vol. 42 No. 6, pp. 962-978.","DOI":"10.1108\/K-11-2012-0108"},{"key":"key2020121802260262800_b6","doi-asserted-by":"crossref","unstructured":"Yang, G.\n               , \n                  Chen, D.\n                and \n                  Zhou, G.\n                (2006), \u201cA new hybrid algorithm of particle swarm optimization\u201d, Proceeding of the International Conference on Intelligent Computing (ICIC\u201906), Kunming, August, pp. 16-19.","DOI":"10.1007\/11816102_6"},{"key":"key2020121802260262800_b10","doi-asserted-by":"crossref","unstructured":"Yazdani, D.\n               , \n                  Golyari, S.\n                and \n                  Meybodi, M.R.\n                (2010), \u201cA new hybrid algorithm for optimization based on artificial fish swarm algorithm and cellular learning automata\u201d, Proceeding of the 5th International Symposium on Telecommunication (IST), Tehran, pp. 932-937.","DOI":"10.1109\/ISTEL.2010.5734156"},{"key":"key2020121802260262800_b18","unstructured":"Yu, Y.\n               , \n                  Tian, Y.F.\n                and \n                  Yin, Z.F.\n                (2005), \u201cMultiuser detector based on adaptive arti\ufb01cial \ufb01sh school algorithm\u201d, Proceeding of IEEE International Symposium on Communications and Information Technology (ISCIT\u2019 05), Beijing, October, pp. 1480-1484."},{"key":"key2020121802260262800_b21","doi-asserted-by":"crossref","unstructured":"Zhan, Z.H.\n               , \n                  Zhang, J.\n               , \n                  Li, Y.\n                and \n                  Chung, H.S.H.\n                (2009), \u201cAdaptive particle swarm optimization\u201d, \n                  IEEE Transaction on System, Man and Cybernetics, Part B: Cybernetics\n               , Vol. 39 No. 6, pp. 1362-1381.","DOI":"10.1109\/TSMCB.2009.2015956"},{"key":"key2020121802260262800_b155","doi-asserted-by":"crossref","unstructured":"Zhang, C.\n               , \n                  Zhang, F.M.\n               , \n                  Li, F.\n                and \n                  Wu, H.S.\n                (2014), \u201cImproved artificial fish swarm algorithm\u201d, Industrial Electronics and Applications (ICIEA), pp. 748-753.","DOI":"10.1109\/ICIEA.2014.6931262"},{"key":"key2020121802260262800_b7","doi-asserted-by":"crossref","unstructured":"Zhang, Q.\n               , \n                  Li, C.\n                and \n                  Liu, Y.\n                (2007), \u201cFast multi-swarm optimization with Cauchy mutation and crossover operation\u201d, Proceeding of International Symposium on Intelligence Computation and Applications (ISICA\u201907), Wuhan, September, pp. 21-23.","DOI":"10.1007\/978-3-540-74581-5_38"}],"container-title":["Kybernetes"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.emeraldinsight.com\/doi\/full-xml\/10.1108\/K-09-2014-0198","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/K-09-2014-0198\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/K-09-2014-0198\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T21:50:02Z","timestamp":1753393802000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/k\/article\/45\/2\/210-222\/272509"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,2,1]]},"references-count":24,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2016,2,1]]}},"alternative-id":["10.1108\/K-09-2014-0198"],"URL":"https:\/\/doi.org\/10.1108\/k-09-2014-0198","relation":{},"ISSN":["0368-492X"],"issn-type":[{"type":"print","value":"0368-492X"}],"subject":[],"published":{"date-parts":[[2016,2,1]]}}}