Optimization stands as a foundational research discipline, permeating various domains such as engineering, and management, and beyond, where many problems inherently entail optimization.The development of algorithms tailored to solve optimization problems not only holds significant theoretical implications but also promises substantial practical applications.Conventional approaches to continuous optimization predominantly rely on gradient methods, integrating analytical techniques with numerical computations to form a patterned, iterative solution framework.Combinatorial optimization, on the other hand, relies on problem-specific designs, spawning numerous research branches due to the diverse nature of problems.Challenges such as intricate design, expert knowledge dependency, and multiple constraints further complicate matters.Conventional methods mandate the execution of an entire algorithmic pipeline for each optimization instance, thereby entailing fixed computational complexities.Once programmed, the efficiency of these algorithms, in terms of computational accuracy and complexity, remains static.Moreover, prior solving experiences offer limited transferability across instances of the same problem.However, practical scenarios often exhibit inherent similarities between problem instances or across different problems within the same domain.Traditional algorithms fail to systematically leverage these properties, let alone simultaneously address all instances of such problems.Presently, the rapid advancement of machine learning methodologies, particularly deep learning and reinforcement learning, has catalyzed progress across various disciplines.Optimization serves as a pivotal underpinning of machine learning, with the ultimate objective often distilled into solving optimization problems-a facet of the burgeoning field of Science for AI.Conversely, AI technologies, including machine learning, have profoundly influenced scientific development, ushering in a new research paradigm-the AI for Science paradigm.Leveraging AI methods to tackle optimization problems has garnered increasing attention from scholars globally.In 2019, the National Natural Science Foundation of China launched a significant research initiative titled "AI Methods for Optimization Problems", aimed at fostering research in this domain within China.Over the past five years, through the concerted efforts of project teams and numerous researchers, a series of noteworthy achievements have been realized.This special issue seeks to disseminate the latest research outcomes on AI methods for optimization problems, further stimulating research in this area.It spotlights exemplary research findings from the aforementioned major project groups while also welcoming submissions from other outstanding researchers.The manuscripts encompass topics ranging from AI methods for continuous optimization to multi-objective optimization, combinatorial optimization, and integer programming.Over 60 experts in AI, optimization, and related fields were invited by the guest editors to participate in the rigorous review process, with each submission undergoing scrutiny by 2-3 experts.After meticulous review spanning over a year, 11 papers were selected for inclusion in this special issue following successive stages of initial review, re-review, and final review.
In CNC machining, it is desired to obtain high machining accuracy with high processing velocity as far as possible, however it is difficult to realize, and especially machining NURBS curve with large curvature.Based on scheduled feedrate on curvature of NURBS, a new NURBS curve interpolation algorithm was proposed.First, the curvature extreme of NURBS curve was calculated, and the critical value was defined according to machining dynamical requirements.Second, the NURBS curve was split several sub-segments, feedrate scanning algorithm based on S curve acceleration/deceleration was used to schedule the feedrate within each sub-segments and the connection points between adjacent sub-segments.Last, during real-time interpolation, with the second-order Taylor expansion, the interpolation parameter was computed.The results of interpolation case indicated the proposed method not obtain high interpolation accuracy with high interpolation feedrate, but the interpolation output acceleration and jerk within the machine tool's dynamical requirements.
With an in-depth study for the consumption of sports and its role,the solution to the development of the sports consumption conforming to two-oriented society has been proposed in it.
Based on the circular failure chart ,an optimization method for slope design was put forward .The comparison of the calculation results proved this optimization method can decrease the excavation quantities or inprove the factor of safety.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)