{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T20:59:25Z","timestamp":1765486765441,"version":"3.37.3"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,5,19]],"date-time":"2022-05-19T00:00:00Z","timestamp":1652918400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,5,19]],"date-time":"2022-05-19T00:00:00Z","timestamp":1652918400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100011174","name":"State Key Laboratory of Biogeology and Environmental Geology","doi-asserted-by":"publisher","award":["No. GBL21801"],"award-info":[{"award-number":["No. GBL21801"]}],"id":[{"id":"10.13039\/501100011174","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"crossref","award":["No. 61972136"],"award-info":[{"award-number":["No. 61972136"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Hubei Key Laboratory of Transportation Internet of Things","award":["No. WHUTIOT-2019001"],"award-info":[{"award-number":["No. WHUTIOT-2019001"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,2]]},"DOI":"10.1007\/s10489-022-03537-w","type":"journal-article","created":{"date-parts":[[2022,5,19]],"date-time":"2022-05-19T04:02:47Z","timestamp":1652932967000},"page":"2989-3001","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A modified multifactorial differential evolution algorithm with optima-based transformation"],"prefix":"10.1007","volume":"53","author":[{"given":"Lingyi","family":"Shi","sequence":"first","affiliation":[]},{"given":"Zhongbo","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Qinghua","family":"Su","sequence":"additional","affiliation":[]},{"given":"Yongfei","family":"Miao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,5,19]]},"reference":[{"issue":"1","key":"3537_CR1","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1109\/4235.585888","volume":"1","author":"T Back","year":"1997","unstructured":"Back T, Hammel U, Schwefel HP (1997) Evolutionary computation: Comments on the history and current state[J]. IEEE T Evolut Comput 1(1):3\u201317","journal-title":"IEEE T Evolut Comput"},{"key":"3537_CR2","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1016\/j.energy.2019.01.021","volume":"171","author":"TT Nguyen","year":"2019","unstructured":"Nguyen TT (2019) A high performance social spider optimization algorithm for optimal power flow solution with single objective optimization. Energy 171:218\u2013240","journal-title":"Energy"},{"issue":"2","key":"3537_CR3","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1109\/TEVC.2019.2918140","volume":"24","author":"Y Tian","year":"2019","unstructured":"Tian Y, Zhang X, Wang C, Jin Y (2019) An evolutionary algorithm for large-scale sparse multiobjective optimization problems. IEEE T Evolut Comput 24(2):380\u2013393","journal-title":"IEEE T Evolut Comput"},{"key":"3537_CR4","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1016\/j.ins.2020.10.024","volume":"553","author":"PTH Hanh","year":"2021","unstructured":"Hanh PTH, Thanh PD, Binh HTT (2021) Evolutionary algorithm and multifactorial evolutionary algorithm on clustered shortest-path tree problem. Inform Sciences 553:280\u2013304","journal-title":"Inform Sciences"},{"key":"3537_CR5","doi-asserted-by":"publisher","first-page":"104173","DOI":"10.1016\/j.engappai.2021.104173","volume":"100","author":"T Zhou","year":"2021","unstructured":"Zhou T, Hu Z, Zhou Q, Yuan S (2021) A novel grey prediction evolution algorithm for multimodal multiobjective optimization. Eng Appl Artif Intel 100:104173","journal-title":"Eng Appl Artif Intel"},{"key":"3537_CR6","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.apm.2019.10.026","volume":"79","author":"Z Hu","year":"2020","unstructured":"Hu Z, Xu X, Su Q, Zhu H, Guo J (2020) Grey prediction evolution algorithm for global optimization. Appl Math Model 79:145\u2013160","journal-title":"Appl Math Model"},{"key":"3537_CR7","doi-asserted-by":"publisher","first-page":"1555","DOI":"10.1016\/j.ins.2019.10.066","volume":"512","author":"G Li","year":"2020","unstructured":"Li G, Lin Q, Gao W (2020) Multifactorial optimization via explicit multipopulation evolutionary framework. Inform Sciences 512:1555\u20131570","journal-title":"Inform Sciences"},{"issue":"3","key":"3537_CR8","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1109\/TEVC.2015.2458037","volume":"20","author":"A Gupta","year":"2015","unstructured":"Gupta A, Ong YS, Feng L (2015) Multifactorial evolution: toward evolutionary multitasking. IEEE T Evolut Comput 20(3):343\u2013357","journal-title":"IEEE T Evolut Comput"},{"key":"3537_CR9","doi-asserted-by":"crossref","unstructured":"Bali KK, Gupta A, Feng L, Siew TP (2017) Linearized domain adaptation in evolutionary multitasking. IEEE Congress on Evolutionary Computation, pp 1295\u20131302","DOI":"10.1109\/CEC.2017.7969454"},{"issue":"1","key":"3537_CR10","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1109\/TEVC.2017.2785351","volume":"23","author":"J Ding","year":"2017","unstructured":"Ding J, Yang C, Jin Y, Chai T (2017) Generalized multitasking for evolutionary optimization of expensive problems. IEEE T Evolut Comput 23(1):44\u201358","journal-title":"IEEE T Evolut Comput"},{"key":"3537_CR11","doi-asserted-by":"publisher","first-page":"112798","DOI":"10.1016\/j.eswa.2019.07.015","volume":"138","author":"Z Liang","year":"2019","unstructured":"Liang Z, Zhang J, Feng L, Zhu Z (2019) A hybrid of genetic transform and hyper-rectangle search strategies for evolutionary multi-tasking. Expert Syst Appl 138:112798","journal-title":"Expert Syst Appl"},{"issue":"1","key":"3537_CR12","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1109\/TEVC.2019.2906927","volume":"24","author":"KK Bali","year":"2019","unstructured":"Bali KK, Ong YS, Gupta A, Tan PS (2019) Multifactorial evolutionary algorithm with online transfer parameter estimation: MFEA-II. IEEE T Evolut Comput 24(1):69\u201383","journal-title":"IEEE T Evolut Comput"},{"issue":"5","key":"3537_CR13","doi-asserted-by":"publisher","first-page":"858","DOI":"10.1109\/TEVC.2019.2893614","volume":"23","author":"M Gong","year":"2019","unstructured":"Gong M, Tang Z, Li H, Zhang J (2019) Evolutionary multitasking with dynamic resource allocating strategy. IEEE T Evolut Comput 23(5):858\u2013869","journal-title":"IEEE T Evolut Comput"},{"key":"3537_CR14","doi-asserted-by":"crossref","unstructured":"Liang Z, Liang W, Wang Z, Ma X, Liu L, Zhu Z (2021) Multiobjective Evolutionary Multitasking With Two-Stage Adaptive Knowledge Transfer Based on Population Distribution. IEEE T Syst Man Cy-S","DOI":"10.1109\/TSMC.2021.3096220"},{"key":"3537_CR15","doi-asserted-by":"crossref","unstructured":"Wen YW, Ting CK (2017) Parting ways and reallocating resources in evolutionary multitasking. IEEE Congress on Evolutionary Computation, pp 2404\u20132411","DOI":"10.1109\/CEC.2017.7969596"},{"issue":"1","key":"3537_CR16","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1109\/TEVC.2019.2904696","volume":"24","author":"X Zheng","year":"2019","unstructured":"Zheng X, Qin AK, Gong M, Zhou D (2019) Self-regulated evolutionary multitask optimization. IEEE T Evolut Comput 24(1):16\u201328","journal-title":"IEEE T Evolut Comput"},{"key":"3537_CR17","doi-asserted-by":"crossref","unstructured":"Feng L, Zhou W, Zhou L et al (2017) An empirical study of multifactorial PSO and multifactorial DE. IEEE Congress on Evolutionary Computation, pp 921\u2013928","DOI":"10.1109\/CEC.2017.7969407"},{"issue":"11","key":"3537_CR18","doi-asserted-by":"publisher","first-page":"4492","DOI":"10.1109\/TSMC.2018.2853719","volume":"50","author":"J Zhong","year":"2018","unstructured":"Zhong J, Feng L, Cai W, Ong YS (2018) Multifactorial genetic programming for symbolic regression problems. IEEE T Syst Man Cy-S 50(11):4492\u20134505","journal-title":"IEEE T Syst Man Cy-S"},{"key":"3537_CR19","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1016\/j.ins.2020.05.132","volume":"540","author":"TTB THuynh","year":"2020","unstructured":"THuynh TTB, Pham DT, Tran BT, Le CT, Le MHP, Swami A, Bui TL (2020) A multifactorial optimization paradigm for linkage tree genetic algorithm. Inform Sciences 540:325\u2013344","journal-title":"Inform Sciences"},{"issue":"5","key":"3537_CR20","doi-asserted-by":"publisher","first-page":"2563","DOI":"10.1109\/TCYB.2020.2974100","volume":"51","author":"L Zhou","year":"2020","unstructured":"Zhou L, Feng L, Tan KC, Zhong J, Zhu Z, Liu K, Chen C (2020) Toward adaptive knowledge transfer in multifactorial evolutionary computation. IEEE T Cybernetics 51(5):2563\u20132576","journal-title":"IEEE T Cybernetics"},{"key":"3537_CR21","unstructured":"Da B, Ong YS, Feng L et al (2017) Evolutionary multitasking for single-objective continuous optimization:, Benchmark problems, performance metric, and baseline results. arXiv preprint arXiv:1706.03470"},{"key":"3537_CR22","doi-asserted-by":"crossref","unstructured":"Zhou L, Feng L, Liu K, Chen C, Deng S, Xiang T, Jiang S (2019) Towards effective mutation for knowledge transfer in multifactorial differential evolution. IEEE Congress on Evolutionary Computation, pp 1541\u20131547","DOI":"10.1109\/CEC.2019.8790143"},{"key":"3537_CR23","doi-asserted-by":"crossref","unstructured":"Tizhoosh HR (2005) Opposition-based learning: a new scheme for machine intelligence. In: International conference on computational intelligence for modelling, control and automation and international conference on intelligent agents, web technologies and internet commerce, pp 695\u2013701","DOI":"10.1109\/CIMCA.2005.1631345"},{"issue":"12","key":"3537_CR24","doi-asserted-by":"publisher","first-page":"4434","DOI":"10.1007\/s10489-020-01793-2","volume":"50","author":"X Zhao","year":"2020","unstructured":"Zhao X, Fang Y, Liu L, Li J, Xu M (2020) An improved moth-flame optimization algorithm with orthogonal opposition-based learning and modified position updating mechanism of moths for global optimization problems. Appl Intell 50(12):4434\u20134458","journal-title":"Appl Intell"},{"key":"3537_CR25","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1016\/j.eswa.2017.07.043","volume":"90","author":"M Abd Elaziz","year":"2017","unstructured":"Abd Elaziz M, Oliva D, Xiong S (2017) An improved opposition-based sine cosine algorithm for global optimization. Expert Syst Appl 90:484\u2013500","journal-title":"Expert Syst Appl"},{"key":"3537_CR26","doi-asserted-by":"publisher","first-page":"30745","DOI":"10.1109\/ACCESS.2020.2973197","volume":"8","author":"C Dai","year":"2020","unstructured":"Dai C, Hu Z, Li Z, Xiong Z, Su Q (2020) An improved grey prediction evolution algorithm based on topological opposition-based learning. IEEE Access 8:30745\u201330762","journal-title":"IEEE Access"},{"issue":"2","key":"3537_CR27","doi-asserted-by":"publisher","first-page":"906","DOI":"10.1016\/j.asoc.2007.07.010","volume":"8","author":"S Rahnamayan","year":"2008","unstructured":"Rahnamayan S, Tizhoosh HR, Salama MM (2008) Opposition versus randomness in soft computing techniques. Appl Soft Comput 8(2):906\u2013918","journal-title":"Appl Soft Comput"},{"issue":"9","key":"3537_CR28","doi-asserted-by":"publisher","first-page":"2828","DOI":"10.1016\/j.asoc.2012.03.034","volume":"12","author":"S Rahnamayan","year":"2012","unstructured":"Rahnamayan S, Wang GG, Ventresca M (2012) An intuitive distance-based explanation of opposition-based sampling. Appl Soft Comput 12(9):2828\u20132839","journal-title":"Appl Soft Comput"},{"issue":"9","key":"3537_CR29","doi-asserted-by":"publisher","first-page":"3457","DOI":"10.1109\/TCYB.2018.2845361","volume":"49","author":"L Feng","year":"2018","unstructured":"Feng L, Zhou L, Zhong J, Gupta A, Ong YS, Tan KC, Qin AK (2018) Evolutionary multitasking via explicit autoencoding. IEEE T Cybernetics 49(9):3457\u20133470","journal-title":"IEEE T Cybernetics"},{"issue":"6","key":"3537_CR30","doi-asserted-by":"publisher","first-page":"1050","DOI":"10.1109\/TFUZZ.2020.2968863","volume":"28","author":"D Wu","year":"2020","unstructured":"Wu D, Tan X (2020) Multitasking genetic algorithm (MTGA) for fuzzy system optimization. IEEE T Fuzzy Syst 28(6):1050\u2013 1061","journal-title":"IEEE T Fuzzy Syst"},{"key":"3537_CR31","doi-asserted-by":"publisher","first-page":"874","DOI":"10.1016\/j.ins.2021.09.021","volume":"580","author":"Y Cai","year":"2021","unstructured":"Cai Y, Peng D, Liu P, Guo JM (2021) Evolutionary multi-task optimization with hybrid knowledge transfer strategy. Inform Sciences 580:874\u2013896","journal-title":"Inform Sciences"},{"key":"3537_CR32","doi-asserted-by":"crossref","unstructured":"Ma X, Yin J, Zhu A, Li X, Yu Y, Wang L, Zhu Z (2021) Enhanced Multifactorial Evolutionary Algorithm With Meme Helper-Tasks. IEEE T Cybernetics","DOI":"10.1109\/TCYB.2021.3050516"},{"key":"3537_CR33","unstructured":"Xue X, Zhang K, Tan KC et al (2020) Affine transformation-enhanced multifactorial optimization for heterogeneous problems. IEEE T Cybernetics"},{"key":"3537_CR34","doi-asserted-by":"crossref","unstructured":"Tang Z, Gong M, Wu Y, Qin AK, Tan KC (2021) A Multifactorial Optimization Framework Based on Adaptive Intertask Coordinate System. IEEE T Cybernetics","DOI":"10.1109\/TCYB.2020.3043509"},{"key":"3537_CR35","doi-asserted-by":"crossref","unstructured":"Li G, Zhang Q, Gao W (2018) Multipopulation evolution framework for multifactorial optimization. Inproceedings of the Genetic and Evolutionary Computation Conference Companion, pp 215\u2013216","DOI":"10.1145\/3205651.3205761"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03537-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-03537-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03537-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T11:50:57Z","timestamp":1673437857000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-03537-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,19]]},"references-count":35,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,2]]}},"alternative-id":["3537"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-03537-w","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2022,5,19]]},"assertion":[{"value":"22 March 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 May 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}