01443nas a2200193   4500000000100000000000100001008004100002260001200043653001700055100001700072700001700089700002600106245002000132856008100152300000800233490000600241520098800247022001401235       2023                            d  c03/202310aEditors Note1 aJiachen Yang1 aHoubing Song1 aMuhammad Khurram Khan00aEditor’s Note  uhttps://www.ijimai.org/journal/sites/default/files/2023-02/ijimai8_1_0_0.pdf  a4-40 v83 aWith the rapid development of information and communication technologies, artificial intelligence and IoTs, more and more advanced technologies, such as machine learning, reinforcement learning, neural networks and fuzzy systems, have been introduced into industrial practices. The application of advanced technologies has greatly promoted the process of industrial revolution. However, there is big gap between controlled simulation and real evolving environment, which results in the unsatisfactory performance of the typical algorithms in practical environments. For example, in Underwater IoTs, a dynamic and uncertain marine environment can cause equipment damage, resulting in huge financial losses. Therefore, improving the robustness and adaptability of algorithms and systems, and proposing new solutions in practical applications to meet the requirements of self-developing, self-organizing, and evolving systems is essential to promote intelligent industrial applications.  a1989-1660