Innovation Modeling, Algorithms and Applications on Optimization

Dear Colleagues,

Entropy optimization has become a powerful tool for data analysis and problem solving that has an infinite array of real-world applications, including engineering, finance, logistics and computer science. However, it also faces challenges, such as the complexity of many problems, local optima and the need for accurate models and data. Researchers are developing new algorithms and techniques to efficiently solve complex problems and handle uncertain data. Artificial intelligence and machine learning are also being explored to automate optimization and learn from past solutions.

The Special Issue on entropy optimization aims to collect high-quality research papers related to innovation optimization techniques and their applications. The goal is to provide a platform for researchers to share their latest findings and insights in the field of entropy optimization. Topics of interest include algorithm development, theoretical analysis and real-world applications in various fields. The Special Issue also welcomes papers that address the challenges and limitations of optimization, such as scalability and uncertainty.

• Machine learning optimization: Applying machine learning techniques to optimization problems, such as using deep learning algorithms, to solve combinatorial optimization problems or using reinforcement learning algorithms to optimize control systems.
• Intelligent optimization algorithms: Optimization algorithms developed based on artificial intelligence technologies, which can automatically adapt to problem complexity and improve optimization efficiency and accuracy. These technologies include new entropy optimization algorithms based on genetic algorithms, ant colony algorithms, particle swarm optimization, etc.
• Multi-objective optimization: Extending optimization problems to situations with multiple objective functions, such as considering minimizing costs and maximizing production efficiency, to obtain multiple feasible solutions and select the optimal solution among them.
• Mixed-integer programming: Combining integer programming and linear programming to solve some practical decision-making problems, such as logistics transportation problems, factory scheduling problems, etc.
• Large-scale optimization: In the face of problems with large-scale data and complex constraint conditions, distributed computing, cloud computing and other technologies can be used to accelerate the speed of solving large-scale optimization problems, such as deep learning, transfer learning and adaption.
• Robust optimization: Developing optimization models that can handle uncertainty and variability in data by incorporating robustness and resilience constraints in the optimization problem.
• The applications of entropy optimization in practical engineering: This is similar to statistics, thermodynamics, pattern recognition, spectral analysis, queuing theory, parameter estimation problems, mission planning for aerospace, job shop scheduling, etc.

Prof. Dr. Zhongbao Zhou
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• entropy optimization
• innovation applications
• algorithms design
• modeling
• artificial intelligence