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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Published Papers (2 papers)

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All Algorithms AppliedMath Computation Mathematics Symmetry

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29 pages, 7785 KiB  

Open AccessArticle

Data Value Assessment in Digital Economy Based on Backpropagation Neural Network Optimized by Genetic Algorithm

by Xujiang Qin, Qi He, Xin Zhang and Xiang Yang

Symmetry 2025, 17(5), 761; https://doi.org/10.3390/sym17050761 - 14 May 2025

Viewed by 135

Abstract

As a new form of economic activity driven by data resources and digital technologies, the digital economy underscores the strategic significance of data as a core production factor. This growing importance necessitates accurate and robust valuation methods. Data valuation poses core modeling challenges [...] Read more.

As a new form of economic activity driven by data resources and digital technologies, the digital economy underscores the strategic significance of data as a core production factor. This growing importance necessitates accurate and robust valuation methods. Data valuation poses core modeling challenges due to its nonlinear nature and the instability of neural networks, including gradient vanishing, parameter sensitivity, and slow convergence. To overcome these challenges, this study proposes a genetic algorithm-optimized BP (GA-BP) model, enhancing the efficiency and accuracy of data valuation. The BP neural network employs a symmetrical architecture, with neurons organized in layers and information transmitted symmetrically during both forward and backward propagation. Similarly, the genetic algorithm maintains a symmetric evolutionary process, featuring symmetric operations in both crossover and mutation. The empirical data used in this study are sourced from the Shanghai Data Exchange, comprising 519 data samples. Based on this dataset, the model incorporates 9 primary indicators and 21 secondary indicators to comprehensively assess data value, optimizing network weights and thresholds through the genetic algorithm. Experimental results show that the GA-BP model outperforms the traditional BP network in terms of mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R²), achieving a 47.6% improvement in prediction accuracy. Furthermore, GA-BP exhibits faster convergence and greater stability. When compared to other models such as long short-term memory (LSTM), convolutional neural networks (CNNs), and optimization-based BP variants like particle swarm optimization BP (PSO-BP) and whale optimization algorithm BP (WOA-BP), GA-BP demonstrates superior generalization and robustness. This approach provides valuable insights into the commercialization of data assets. Full article

(This article belongs to the Topic Intelligent Optimization Algorithm: Theory and Applications)

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21 pages, 1103 KiB  

Open AccessArticle

Multi-Objective Cauchy Particle Swarm Optimization for Energy-Aware Virtual Machine Placement in Cloud Datacenters

by Xuan Liu, Chenyan Wang, Shan Jiang, Yutong Gao, Chaomurilige and Bo Cheng

Symmetry 2025, 17(5), 742; https://doi.org/10.3390/sym17050742 - 13 May 2025

Viewed by 142

Abstract

With the continuous expansion of application scenarios for cloud computing, large-scale service deployments in cloud data centers are accompanied by a significant increase in resource consumption. Virtual machines (VMs) in data centers are allocated to physical machines (PMs) and require the resources provided [...] Read more.

With the continuous expansion of application scenarios for cloud computing, large-scale service deployments in cloud data centers are accompanied by a significant increase in resource consumption. Virtual machines (VMs) in data centers are allocated to physical machines (PMs) and require the resources provided by PMs to run various services. Apparently, a simple solution to minimize energy consumption is to allocate VMs as compactly as possible. However, the above virtual machine placement (VMP) strategy may lead to system performance degradation and service failures due to imbalanced resource load, thereby reducing the robustness of the cloud data center. Therefore, an effective VMP solution that comprehensively considers both energy consumption and other performance metrics in data centers is urgently needed. In this paper, we first construct a multi-objective VMP model aiming to simultaneously optimize energy consumption, resource utilization, load balancing, and system robustness, and we then build a joint optimization function with resource constraints. Subsequently, a novel energy-aware Cauchy particle swarm optimization (EA-CPSO) algorithm is proposed, which implements particle asymmetric disturbances and an energy-efficient population iteration strategy, aiming to minimize the value of the joint optimization function. Finally, our extensive experiments demonstrated that EA-CPSO outperforms existing methods. Full article

(This article belongs to the Topic Intelligent Optimization Algorithm: Theory and Applications)

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