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Unfortunately, knowledge transfer between constituent tasks may cause negative effect on algorithm performance, especially when the optimal solutions of all tasks are in different locations of the unified search space. To address this issue, an effective variable transformation strategy and the corresponding inverse transformation are proposed in multitasking optimization scenario. After using variable transformation strategy, the estimated optimal solutions of all tasks are both near the center point of the unified search space. More importantly, this strategy can enhance the task similarity, and then the effectiveness of knowledge transfer will probably be positive in this case, which can help us to improve the algorithm performance. Keeping this in mind, a multitasking evolutionary algorithm (named MTDE-VT) is realized as an instance by embedding the proposed variable transformation strategy into multitasking differential evolution. In MTDE-VT, the individuals in the original population are first transformed into new locations by the variable transformation strategy. Once the offspring is generated in the transformed unified search space, it must be transformed back to the original unified search space. The statistical analysis of experimental results on some multitasking optimization benchmark problems illustrates the superiority of the proposed MTDE-VT algorithm in terms of solution accuracy and robustness. Furthermore, the basic principle and the good parameter combination are also provided based on massive simulated data.<\/jats:p>","DOI":"10.1155\/2020\/8815117","type":"journal-article","created":{"date-parts":[[2020,10,11]],"date-time":"2020-10-11T12:56:32Z","timestamp":1602420992000},"page":"1-15","source":"Crossref","is-referenced-by-count":6,"title":["An Effective Variable Transformation Strategy in Multitasking Evolutionary Algorithms"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8212-1073","authenticated-orcid":true,"given":"Qingzheng","family":"Xu","sequence":"first","affiliation":[{"name":"College of Information and Communication, National University of Defense Technology, Xi\u2019an 710106, China"},{"name":"Youth Innovation Team of Shaanxi Universities, National University of Defense Technology, Xi\u2019an 710106, 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