Intelligent Optimization Algorithm: Theory and Applications

Topic Information

Dear Colleagues,

Intelligent optimization algorithms (IOAs) belong to a branch of artificial intelligence that emphasizes 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, involve the collection of search and optimization techniques. IOAs include bio-inspired intelligent algorithms, evolutionary computation methods, swarm intelligence, etc. With these methods, the 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 in the following topics (but not limited to):

• Artificial intelligence;
• Deep learning;
• Data mining;
• Data-driven optimization methods;
• Time-series forecasting;
• Time-series anomaly detection;
• Swarm intelligence;
• Intelligent computing;
• Bio-inspired algorithms, 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

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Algorithms algorithms	1.8	4.1	2008	18.9 Days	CHF 1600	Submit
AppliedMath appliedmath	-	-	2021	25.3 Days	CHF 1000	Submit
Computation computation	1.9	3.5	2013	18.6 Days	CHF 1800	Submit
Mathematics mathematics	2.3	4.0	2013	18.3 Days	CHF 2600	Submit
Symmetry symmetry	2.2	5.4	2009	17.3 Days	CHF 2400	Submit

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