An Evolutionary Multitasking Optimization Framework for Constrained Multiobjective Optimization Problems

• Published in: IEEE transactions on evolutionary computation Vol. 26; no. 2; pp. 263 - 277
• Main Authors: Qiao, Kangjia, Yu, Kunjie, Qu, Boyang, Liang, Jing, Song, Hui, Yue, Caitong
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Constrained multiobjective optimization; Constraints; Evolutionary algorithms; evolutionary multitasking (EMT); Genetic algorithms; Knowledge management; Knowledge transfer; Multiple objective analysis; Multitasking; multitasking optimization problem; Numerical models; Optimization; Performance enhancement; Sociology; Statistics; Task analysis
• ISSN: 1089-778X; 1941-0026
• DOI: 10.1109/TEVC.2022.3145582

When addressing constrained multiobjective optimization problems (CMOPs) via evolutionary algorithms, various constraints and multiple objectives need to be satisfied and optimized simultaneously, which causes difficulties for the solver. In this article, an evolutionary multitasking (EMT)-based constrained multiobjective optimization (EMCMO) framework is developed to solve CMOPs. In EMCMO, the optimization of a CMOP is transformed into two related tasks: one task is for the original CMOP, and the other task is only for the objectives by ignoring all constraints. The main purpose of the second task is to continuously provide useful knowledge of objectives to the first task, thus facilitating solving the CMOP. Specially, the genes carried by parent individuals or offspring individuals are dynamically regarded as useful knowledge due to the different complementarities of the two tasks. Moreover, the useful knowledge is found by the designed tentative method and transferred to improve the performance of the two tasks. To the best of our knowledge, this is the first attempt to use EMT to solve CMOPs. To verify the performance of EMCMO, an instance of EMCMO is obtained by employing a genetic algorithm as the optimizer. Comprehensive experiments are conducted on four benchmark test suites to verify the effectiveness of knowledge transfer. Furthermore, compared with other state-of-the-art constrained multiobjective optimization algorithms, EMCMO can produce better or at least comparable performance.