caa a22 4500 606160205 CHVBK 20210128100628.0 cr unu---uuuuu 210128e20150701xx s 000 0 eng 10.1007/s00521-014-1806-7 doi (NATIONALLICENCE)springer-10.1007/s00521-014-1806-7 Evolutionary population dynamics and grey wolf optimizer [Elektronische Daten] [Shahrzad Saremi, Seyedeh Mirjalili, Seyed Mirjalili] Evolutionary population dynamics (EPD) deal with the removal of poor individuals in nature. It has been proven that this operator is able to improve the median fitness of the whole population, a very effective and cheap method for improving the performance of meta-heuristics. This paper proposes the use of EPD in the grey wolf optimizer (GWO). In fact, EPD removes the poor search agents of GWO and repositions them around alpha, beta, or delta wolves to enhance exploitation. The GWO is also required to randomly reinitialize its worst search agents around the search space by EPD to promote exploration. The proposed GWO-EPD algorithm is benchmarked on six unimodal and seven multi-modal test functions. The results are compared to the original GWO algorithm for verification. It is demonstrated that the proposed operator is able to significantly improve the performance of the GWO algorithm in terms of exploration, local optima avoidance, exploitation, local search, and convergence rate. The Natural Computing Applications Forum, 2014 Grey wolf optimizer nationallicence Optimization nationallicence Evolutionary algorithms nationallicence Heuristic nationallicence Saremi Shahrzad School of Information and Communication Technology, Nathan Campus, Griffith University, Brisbane, QLD 4111, Australia aut Mirjalili Seyedeh Zharfa Pajohesh System (ZPS) Co., Unit 5, NO. 30, West 208 St., Third Sq. Tehranpars, P.O. Box 1653745696, Tehran, Iran aut Mirjalili Seyed Zharfa Pajohesh System (ZPS) Co., Unit 5, NO. 30, West 208 St., Third Sq. Tehranpars, P.O. Box 1653745696, Tehran, Iran aut Neural Computing and Applications Springer London 26/5(2015-07-01), 1257-1263 0941-0643 26:5<1257 2015 26 521 https://doi.org/10.1007/s00521-014-1806-7 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-014-1806-7 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Saremi Shahrzad School of Information and Communication Technology, Nathan Campus, Griffith University, Brisbane, QLD 4111, Australia aut NATIONALLICENCE 700 1- Mirjalili Seyedeh Zharfa Pajohesh System (ZPS) Co., Unit 5, NO. 30, West 208 St., Third Sq. Tehranpars, P.O. Box 1653745696, Tehran, Iran aut NATIONALLICENCE 700 1- Mirjalili Seyed Zharfa Pajohesh System (ZPS) Co., Unit 5, NO. 30, West 208 St., Third Sq. Tehranpars, P.O. Box 1653745696, Tehran, Iran aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/5(2015-07-01), 1257-1263 0941-0643 26:5<1257 2015 26 521