TY  - EJOU
AU  - Wang, Hai-Xiang 
AU  - Li, Chu-Xiang 
AU  - Wang, Zi-Jia 

TI  - A Hybrid Knowledge Transfer for Multitask Optimization
T2  - Computer Modeling in Engineering \& Sciences

PY  - 
VL  - 
IS  - 
SN  - 1526-1506

AB  - Evolutionary multitasking optimization (EMTO) is an emerging research direction in evolutionary computation (EC), with its core objective being the collaborative solution of multiple problems through inter-task knowledge transfer (KT). In classical EMTO algorithms, KT typically relies on the direct exchange or crossover of individuals between populations. However, such transfer strategies often follow singular rules or direct transplantation, which struggle to adequately adapt to the dynamically evolving distributional differences between tasks, and may lead to inefficient transfer or even negative transfer. To tackle this issue, this study presents HKTMTO, a multitask differential evolution algorithm built upon two complementary transfer patterns. The algorithm combines estimation of distribution transfer and adaptive elite transfer to construct a hybrid knowledge transfer framework, overcoming the limitations of traditional single individual-level transfer and improving transfer efficiency without triggering negative transfer. Extensive experiments on the CEC2017 multitask benchmark demonstrate the competitive performance of HKTMTO against mainstream EMTO methods.
KW  - Evolutionary multitask optimization (EMTO); knowledge transfer; elite transfer; distribution-based transfer; adaptive perturbation

DO  - 10.32604/cmes.2026.086077