{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T06:39:06Z","timestamp":1757313546716},"reference-count":23,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"10","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. & Syst."],"published-print":{"date-parts":[[2021,10,1]]},"DOI":"10.1587\/transinf.2021edl8033","type":"journal-article","created":{"date-parts":[[2021,9,30]],"date-time":"2021-09-30T22:41:23Z","timestamp":1633041683000},"page":"1789-1792","source":"Crossref","is-referenced-by-count":11,"title":["Fitness-Distance Balance with Functional Weights: A New Selection Method for Evolutionary Algorithms"],"prefix":"10.1587","volume":"E104.D","author":[{"given":"Kaiyu","family":"WANG","sequence":"first","affiliation":[{"name":"Faculty of Engineering, University of Toyama"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sichen","family":"TAO","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, University of Toyama"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rong-Long","family":"WANG","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, University of Fukui"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuki","family":"TODO","sequence":"additional","affiliation":[{"name":"Faculty of Electrical, Information and Communication Engineering, Kanazawa University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shangce","family":"GAO","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, University of Toyama"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"publisher","unstructured":"[1] Y. Wang, Y. Yu, S. Cao, X. Zhang, and S. Gao, \u201cA review of applications of artificial intelligent algorithms in wind farms,\u201d Artificial Intelligence Review, vol.53, no.5, pp.3447-3500, Oct. 2020. 10.1007\/s10462-019-09768-7","DOI":"10.1007\/s10462-019-09768-7"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] S. Gao, M. Zhou, Y. Wang, J. Cheng, H. Yachi, and J. Wang, \u201cDendritic neuron model with effective learning algorithms for classification, approximation, and prediction,\u201d IEEE Trans. Neural Networks and Learning Systems, vol.30, no.2, pp.601-614, Feb. 2019. 10.1109\/TNNLS.2018.2846646","DOI":"10.1109\/TNNLS.2018.2846646"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] J. Sun, S. Gao, H. Dai, J. Cheng, M. Zhou, and J. Wang, \u201cBi-objective elite differential evolution algorithm for multivalued logic networks,\u201d IEEE Trans. Cybernetics, vol.50, no.1, pp.233-246, Jan. 2020. 10.1109\/TCYB.2018.2868493","DOI":"10.1109\/TCYB.2018.2868493"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] S. Forrest, \u201cGenetic algorithms,\u201d ACM Comput. Surv. (CSUR), vol.28, no.1, pp.77-80, March 1996. 10.1145\/234313.234350","DOI":"10.1145\/234313.234350"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] S. Gao, Y. Yu, Y. Wang, J. Wang, J. Cheng, and M. Zhou, \u201cChaotic local search-based differential evolution algorithms for optimization,\u201d IEEE Trans. Syst. Man Cybern.: Systems, vol.51, no.6, pp.3954-3967, June 2019. doi:10.1109\/TSMC.2019.2956121. 10.1109\/TSMC.2019.2956121","DOI":"10.1109\/TSMC.2019.2956121"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] Y. Yu, S. Gao, Y. Wang, and Y. Todo, \u201cGlobal optimum-based search differential evolution,\u201d IEEE\/CAA J. Automatica Sinica, vol.6, no.2, pp.379-394, March 2018. 10.1109\/JAS.2019.1911378","DOI":"10.1109\/JAS.2019.1911378"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] Z. Lei, S. Gao, S. Gupta, J. Cheng, and G. Yang, \u201cAn aggregative learning gravitational search algorithm with self-adaptive gravitational constants,\u201d Expert Systems with Applications, vol.152, p.113396, Aug. 2020. 10.1016\/j.eswa.2020.113396","DOI":"10.1016\/j.eswa.2020.113396"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] Y. Wang, S. Gao, Y. Yu, Z. Wang, J. Cheng, and T. Yuki, \u201cA gravitational search algorithm with chaotic neural oscillators,\u201d IEEE Access, vol.8, pp.25938-25948, 2020. 10.1109\/ACCESS.2020.2971505","DOI":"10.1109\/ACCESS.2020.2971505"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] A. Kumar, R.K. Misra, D. Singh, S. Mishra, and S. Das, \u201cThe spherical search algorithm for bound-constrained global optimization problems,\u201d Applied Soft Computing, vol.85, p.105734, Dec. 2019. 10.1016\/j.asoc.2019.105734","DOI":"10.1016\/j.asoc.2019.105734"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] S. Gao, K. Wang, S. Tao, T. Jin, H. Dai, and J. Cheng, \u201cA state-of-the-art differential evolution algorithm for parameter estimation of solar photovoltaic models,\u201d Energy Conversion and Management, vol.230, p.113784, Feb. 2021. 10.1016\/j.enconman.2020.113784","DOI":"10.1016\/j.enconman.2020.113784"},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] J. Cheng, G. Yuan, M. Zhou, S. Gao, C. Liu, H. Duan, and Q. Zeng, \u201cAccessibility analysis and modeling for IoV in an urban scene,\u201d IEEE Trans. Veh. Technol., vol.69, no.4, pp.4246-4256, April 2020. 10.1109\/TVT.2020.2970553","DOI":"10.1109\/TVT.2020.2970553"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] J. Wang, Y. Sun, Z. Zhang, and S. Gao, \u201cSolving multitrip pickup and delivery problem with time windows and manpower planning using multiobjective algorithms,\u201d IEEE\/CAA J. Automatica Sinica, vol.7, no.4, pp.1134-1153, July 2020. 10.1109\/JAS.2020.1003204","DOI":"10.1109\/JAS.2020.1003204"},{"key":"13","doi-asserted-by":"publisher","unstructured":"[13] Y. Wang, S. Gao, Y. Yu, Z. Cai, and Z. Wang, \u201cA gravitational search algorithm with hierarchy and distributed framework,\u201d Knowledge-Based Systems, vol.218, p.106877, April 2021. 10.1016\/j.knosys.2021.106877","DOI":"10.1016\/j.knosys.2021.106877"},{"key":"14","doi-asserted-by":"publisher","unstructured":"[14] Y. Wang, S. Gao, M. Zhou, and Y. Yu, \u201cA multi-layered gravitational search algorithm for function optimization and real-world problems,\u201d IEEE\/CAA J. Automatica Sinica, vol.8, no.1, pp.94-109, Jan. 2020. 10.1109\/JAS.2020.1003462","DOI":"10.1109\/JAS.2020.1003462"},{"key":"15","doi-asserted-by":"publisher","unstructured":"[15] A.E. Eiben and S.K. Smit, \u201cParameter tuning for configuring and analyzing evolutionary algorithms,\u201d Swarm and Evolutionary Computation, vol.1, no.1, pp.19-31, March 2011. 10.1016\/j.swevo.2011.02.001","DOI":"10.1016\/j.swevo.2011.02.001"},{"key":"16","doi-asserted-by":"publisher","unstructured":"[16] H.T. Kahraman, S. Aras, and E. Gedikli, \u201cFitness-distance balance (FDB): A new selection method for meta-heuristic search algorithms,\u201d Knowledge-Based Systems, vol.190, p.105169, Feb. 2020. 10.1016\/j.knosys.2019.105169","DOI":"10.1016\/j.knosys.2019.105169"},{"key":"17","doi-asserted-by":"publisher","unstructured":"[17] L. Cui, G. Li, Z. Zhu, Q. Lin, K.C. Wong, J. Chen, N. Lu, and J. Lu, \u201cAdaptive multiple-elites-guided composite differential evolution algorithm with a shift mechanism,\u201d Information Sciences, vol.422, pp.122-143, Jan. 2018. 10.1016\/j.ins.2017.09.002","DOI":"10.1016\/j.ins.2017.09.002"},{"key":"18","doi-asserted-by":"publisher","unstructured":"[18] D.T. Le, D.K. Bui, T.D. Ngo, Q.H. Nguyen, and H. Nguyen-Xuan, \u201cA novel hybrid method combining electromagnetism-like mechanism and firefly algorithms for constrained design optimization of discrete truss structures,\u201d Computers & Structures, vol.212, pp.20-42, Feb. 2019. 10.1016\/j.compstruc.2018.10.017","DOI":"10.1016\/j.compstruc.2018.10.017"},{"key":"19","doi-asserted-by":"publisher","unstructured":"[19] A.F. Kamaruzaman, A.M. Zain, S.M. Yusuf, and A. Udin, \u201cLevy flight algorithm for optimization problems-a literature review,\u201d Applied Mechanics and Materials, vol.421, pp.496-501, Sept. 2013. 10.4028\/www.scientific.net\/AMM.421.496","DOI":"10.4028\/www.scientific.net\/AMM.421.496"},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] R. Tanabe and A.S. Fukunaga, \u201cImproving the search performance of shade using linear population size reduction,\u201d 2014 IEEE Congress on Evol. Comput. (CEC), pp.1658-1665, IEEE, 2014. 10.1109\/CEC.2014.6900380","DOI":"10.1109\/CEC.2014.6900380"},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] A. Kumar, R.K. Misra, and D. Singh, \u201cImproving the local search capability of effective butterfly optimizer using covariance matrix adapted retreat phase,\u201d 2017 IEEE Congress on Evol. Comput. (CEC), pp.1835-1842, IEEE, 2017. 10.1109\/CEC.2017.7969524","DOI":"10.1109\/CEC.2017.7969524"},{"key":"22","doi-asserted-by":"publisher","unstructured":"[22] B. Morales-Casta\u00f1eda, D. Zaldivar, E. Cuevas, F. Fausto, and A. Rodr\u00edguez, \u201cA better balance in metaheuristic algorithms: Does it exist?,\u201d Swarm and Evol. Comput., vol.54, p.100671, May 2020. 10.1016\/j.swevo.2020.100671","DOI":"10.1016\/j.swevo.2020.100671"},{"key":"23","doi-asserted-by":"publisher","unstructured":"[23] J. Carrasco, S. Garc\u00eda, M. Rueda, S. Das, and F. Herrera, \u201cRecent trends in the use of statistical tests for comparing swarm and evolutionary computing algorithms: Practical guidelines and a critical review,\u201d Swarm and Evol. Comput., vol.54, p.100665, May 2020. 10.1016\/j.swevo.2020.100665","DOI":"10.1016\/j.swevo.2020.100665"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E104.D\/10\/E104.D_2021EDL8033\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,2]],"date-time":"2021-10-02T06:50:17Z","timestamp":1633157417000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E104.D\/10\/E104.D_2021EDL8033\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,1]]},"references-count":23,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2021]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2021edl8033","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,1]]},"article-number":"2021EDL8033"}}