Intelligence Optimization Algorithms and Applications

Dear Colleagues,

The new generation of artificial intelligence is driving the rapid development of industry, with advanced software and algorithms gradually integrating into various aspects of life. Large-scale and ultra-large-scale systems have emerged, with increasingly fine configurations and complex processes, which urgently require optimization. However, precise optimization methods are difficult to handle, forcing the research of more effective, reasonable, and feasible heuristic methods and swarm intelligence optimization methods, which attract significant attention and have been a useful tool for solving mathematic theory and real-world problems. Intelligence optimization algorithms can ignore the problems’ characters and obtain feasible solutions quickly. Therefore, they are widely used in mechanism optimization design, mechanism motion trajectory planning, workshop layout optimization, optimization related to machining process technology planning, and so on.

However, some key challenges need to be solved for intelligence optimization algorithms. In theory, there is no strict proof about the approximation ability of this method. In application, it may fail to obtain feasible solutions due to the complexity of problems. Furthermore, an algorithm that suits all types of problems is difficult to design due to their diversity.

Thus, in this Special Issue, we expect articles on topics including (but not limited to):

• Novel algorithms that have advantages in convergence speed, accuracy, and robustness compared with existing ones and their applications in engineering, processing, scheduling, planning, and so on.
• Combination of advanced technologies and intelligence optimization algorithms, for example, determining hyper parameters of algorithms with GAN, or extracting features in LLM with algorithms.
• Reviews including comparison, analysis, and evaluation of different algorithms or problems.
• Articles that focus on the derivation of an algorithm’s approximation ability, the relationship between algorithms and problems, and other mathematical and theoretical study.
• Mechanism optimization design, mechanism motion trajectory planning, workshop layout optimization, and optimization related to machining process technology planning.

Dr. Haibin Ouyang
Dr. Kunjie Yu
Guest Editors

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• optimization
• intelligence algorithms
• dynamic optimization
• multimodal optimization
• constrained optimization
• large-scale optimization and application
• path-planning
• distribution optimization
• mechanism optimization design
• network optimization
• transfer learning
• evolutionary optimization
• neural architecture optimization
• federated learning