Topic Editors
Intelligent Optimization Algorithm: Theory and Applications, 2nd Edition
Topic Information
Dear Colleagues,
Intelligent optimization algorithms (IOAs) are a branch of artificial intelligence that emphasize developing and using information learned from data to solve complex searching, learning, and simulation problems. Many real-world applications for complex industrial engineering or design problems could be modeled as searching, learning, and simulation problems. With the learning ability, IOAs are emerging approaches that utilize advanced computation power with meta-heuristics algorithms and massive data processing techniques. These approaches have been actively investigated and applied to many real-world applications, such as scheduling and logistics operations.
Intelligent optimization algorithms, learned from biological or social phenomena, are a collection of search and optimization techniques. IOAs include bio-inspired intelligent algorithms, evolutionary computation methods, and swarm intelligence, among others. With these methods, optimization problems, which can be represented in any form, do not need to be mathematically represented as continuous and differentiable functions. The only requirement for representing optimization problems is to evaluate each individual as the termed fitness value. Therefore, IOAs could be utilized to solve more general optimization problems, especially for issues that are difficult to solve with traditional hill-climbing algorithms.
Real-world applications have complex properties. Massive data are collected and used in scheduling tasks to optimize route selection, taxi dispatching, dynamic transit bus scheduling, and other mobility services to improve operational efficiency. Another example is logistics, where material movements within and between supply chain entities, including warehouses, factories, distribution centers, and retail shops, are improved and optimized with advanced data-oriented techniques. Many applications of IOAs have been reported. However, more research should be conducted on the theory of IOAs. More efficient algorithms could be designed with the understanding of the search process on IOAs.
Due to the complexity of real-world applications, no one panacea can solve all troubles. IOAs are practical approaches to handling such complexity, utilizing evolutionary computation, swarm intelligence, and other meta-heuristic methods based on domain expert knowledge and experience.
Scope of the topic:
Submissions involving real-world case studies are encouraged, particularly those focused on, but not limited to, the following topics:
- Artificial intelligence;
- Deep learning;
- Data mining;
- Data-driven optimization methods;
- Time-series forecasting;
- Time-series anomaly detection;
- Swarm intelligence;
- Intelligent computing;
- Bio-inspired algorithms and nature-inspired computing;
- Computational intelligence and evolutionary algorithms;
- Meta-heuristic algorithms;
- Intelligent optimization algorithms;
- Other related topics.
Dr. Shi Cheng
Dr. Chaomin Luo
Prof. Dr. Shangce Gao
Topic Editors
Keywords
- artificial intelligence
- deep learning
- swarm intelligence
- data-driven optimization methods
- time-series forecasting
- computational intelligence and evolutionary algorithms
- meta-heuristic algorithms
- intelligent optimization algorithms
- data mining
- time-series anomaly detection
Participating Journals
| Journal Name | Impact Factor | CiteScore | Launched Year | First Decision (median) | APC | |
|---|---|---|---|---|---|---|
Algorithms
|
2.6 | 5.4 | 2008 | 19.2 Days | CHF 1800 | Submit |
Applied Sciences
|
2.9 | 6.1 | 2011 | 16 Days | CHF 2400 | Submit |
AppliedMath
|
1.4 | 1.4 | 2021 | 20.6 Days | CHF 1200 | Submit |
Computation
|
2.6 | 5.2 | 2013 | 14.8 Days | CHF 1800 | Submit |
Mathematics
|
2.3 | 5.4 | 2013 | 17.3 Days | CHF 2600 | Submit |
Sci
|
4.1 | 5.4 | 2019 | 26.7 Days | CHF 1400 | Submit |
Symmetry
|
2.2 | 5.2 | 2009 | 15.8 Days | CHF 2400 | Submit |
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