Titlebook: Advanced Intelligent Computing Technology and Applications; 19th International C De-Shuang Huang,Prashan Premaratne,Abir Hussain Conference

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Hyper-heuristic ,-Learning Algorithm for Flow-Shop Scheduling Problem with Fuzzy Processing Timesporate pathfinding and steering to help characters navigate through your world ..Create challenging levels with intelligent enemies by using decision trees, state machines, and scripted events ..Add multiplayer in your games using Node.js and the WebSocket API . . .This book will help you take your HTML5/Java978-1-4302-4711-1

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0302-9743 h applications. Therefore, the theme for this conference was "Advanced Intelligent Computing Technology and Applications". Papers that focused on this theme were solicited, addressing theories, methodologies, and applications in science and technology..978-981-99-4754-6978-981-99-4755-3Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Palgrave Studies in Animals and Literatureese bases, experiments are conducted and analyzed using Particle Swarm Optimization (PSO), and much useful information about its RC is revealed. Besides, this method can be used to analyze the RC of similar optimization algorithms as well.

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Earth Ethics and Creaturely Cohabitationto analyze the impact of different parameters and connection functions on the algorithm’s performance. The numerical results obtained from these experiments suggest that the algorithm can be applied to a number of complex models and the maximin optimal design of the quantile regression model can be

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The Future of Human Spaceflight in Japane allocated to tasks that have a greater impact on the overall application scheduling time, thus effectively reducing the application scheduling time. Therefore, we have proposed a task level-aware scheduling algorithm that allocates energy to each level based on the proportion of the minimum energy

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L. Cline,P. Finarelli,G. Gibbs,I. Prykeccelerate the convergence speed. Simulation experiments have been conducted on a set of randomly generated tasks, and the experimental results have demonstrated the efficiency of the proposed algorithm. The proposed MECMA algorithm has significantly reduced energy consumption and shortened task comp

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https://doi.org/10.1007/978-3-319-50758-3sed on the probabilistic transition matrix. A permutation is an individual in the high-level population. Then HHACOA can be quickly guided to reach regions where high-quality non-dominated solutions are concentrated. Finally, the simulation experiments and algorithm comparisons verify the effectiven

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Sarbani Roy,Chandreyee Chowdhuryt the communication costs. The experiments and analysis are based on randomly generated graphs with various parameters, including the degree of DAG regularity and the communication-computing cost ratio, as well as the number of processors and the degree of heterogeneity. The results showed that the