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The third direction of our work is introducing the L1 norm distance as the weights for updating the mean value of crossover rate and scale factor. Theoretically, compared with L2 norm, L1-norm is more efficient to suppress outliers in the difference vector. These modifications are first integrated with the mutation strategy of JADE, then a modified version, named JADEfcr, is proposed. In addition, to improve the optimization ability further, another variant LJADEfcr by using a linear population reduction mechanism is considered. So as to confirm and examine the performance of JADEfcr and LJADEfcr, numerical experiments are conducted on 29 optimization problems defined by CEC2017 benchmark. For JADEfcr, its experimental results are made a comparison with twelve state-of-the-art algorithms. The comparative study demonstrates that in terms of robustness, stability and solution quality, JADEfcr are better and highly competitive with these well-known algorithms. For LJADEfcr, its results are compared with JADEfcr and other nine powerful algorithms including four recent algorithms and five top algorithms on CEC2017 competition. Experimental results indicate that LJADEfcr is superior and statistically competitive with these excellent algorithms in terms of robustness, stability and the quality of the obtained solutions.<\/jats:p>","DOI":"10.1007\/s40747-023-01159-4","type":"journal-article","created":{"date-parts":[[2023,7,27]],"date-time":"2023-07-27T10:02:21Z","timestamp":1690452141000},"page":"551-576","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Adaptive differential evolution with fitness-based crossover rate for global numerical optimization"],"prefix":"10.1007","volume":"10","author":[{"given":"Lianzheng","family":"Cheng","sequence":"first","affiliation":[]},{"given":"Jia-Xi","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Xing","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Ali Wagdy","family":"Mohamed","sequence":"additional","affiliation":[]},{"given":"Yun","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,27]]},"reference":[{"key":"1159_CR1","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1016\/j.ins.2015.04.031","volume":"316","author":"RJ Kuo","year":"2015","unstructured":"Kuo RJ, Zulvia FE (2015) The gradient evolution algorithm: a new metaheuristic. 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