Advances of Optimization Theory and Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: 30 May 2027 | Viewed by 1475

Editors

School of Artificial Intelligence and Computer Science, Shaanxi Normal University, Xi'an, China
Interests: mobile computing; optimization theory; machine learning; large language models; federated learning

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Guest Editor
School of Information Science and Technology, Northwest University, Xi'an, China
Interests: edge computing; optimization theory; machine learning

E-Mail Website
Guest Editor
School of Artificial Intelligence and Computer Science, Shaanxi Normal University, Xi'an, China
Interests: software defect prediction; intelligent software engineering

Special Issue Information

Dear Colleagues,

This Special Issue, "Advances of Optimization Theory and Applications," invites researchers and practitioners to explore cutting-edge optimization techniques and their impactful applications across diverse fields, including but not limited to mobile computing environments, energy efficiency, application optimization, LLM-based optimization, federated learning, and edge computing. Optimization theory plays a pivotal role in enhancing performance, efficiency, and user experience across numerous applications, including real-time systems, machine learning, logistics, healthcare, finance, and network communication.

We seek original contributions that address theoretical advancements and innovative practical solutions, tackling unique challenges like limited computational resources, power constraints, latency sensitivity, and dynamic environments. Potential topics include adaptive resource allocation algorithms, energy-aware scheduling methods, low-latency optimization strategies, federated learning optimization, network design and optimization, logistics and supply chain optimization, and real-time data processing enhancements.

By encompassing a broad spectrum of applications, this Special Issue aims to bridge theoretical developments with practical implementations, fostering the creation of efficient, responsive, and intelligent systems across various domains. Contributions should provide rigorous analyses and novel methodologies and demonstrate clear applicability. We eagerly anticipate your valuable contributions, which will enhance both the theoretical foundations and practical applications in the dynamic field of optimization.

Dr. Jie Ren
Dr. Jie Zheng
Dr. Zhiqiang Li
Guest Editors

Manuscript Submission Information

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Keywords

  • application optimization
  • edge computing
  • mobile computing
  • federated learning
  • LLM-based optimization

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Published Papers (2 papers)

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Research

30 pages, 4874 KB  
Article
A Multi-Objective Intelligent Method for Generating Mine Ventilation Feature Graphs Based on the Adaptive NSGA-II Algorithm
by Zhenguo Yan, Bo Yang, Longcheng Zhang, Yuxin Huang, Chongwu Chen and Jianing Ruan
Mathematics 2026, 14(12), 2191; https://doi.org/10.3390/math14122191 - 18 Jun 2026
Viewed by 347
Abstract
Ventilation network feature graphs (Q-H graphs) are a key visualisation tool for mine ventilation systems, and their automated generation reduces to a combinatorial optimisation problem over independent-path permutations. Existing methods, however, exhibit three limitations: a single-dimensional evaluation criterion, inadequate nodal pressure-energy assignment, and [...] Read more.
Ventilation network feature graphs (Q-H graphs) are a key visualisation tool for mine ventilation systems, and their automated generation reduces to a combinatorial optimisation problem over independent-path permutations. Existing methods, however, exhibit three limitations: a single-dimensional evaluation criterion, inadequate nodal pressure-energy assignment, and unstable convergence in factorial-scale search spaces. This paper proposes an adaptive NSGA-II (A-NSGA-II) framework with coordinated enhancements at the evaluation, modelling, and algorithmic levels. A three-objective system that minimises split-block count, topological-spatial discrepancy, and layout fragmentation is established, together with an aggregate evaluation score (AES) for engineering decision-making; nodal pressure energies are reconstructed via the longest path on a directed acyclic graph; and topology-aware initialisation, Lagrange three-point interpolated adaptive operators, and periodic memetic local search are integrated within NSGA-II. Experiments on two mine ventilation networks (75 and 112 branches) over 30 independent trials show that A-NSGA-II consistently outperforms four benchmarks (NSGA-II, MOEA/D, SPEA2, and MOSA) in terms of split-block count, AES, and hypervolume; statistical tests confirm significant, large-effect HV advantages on the 112-branch network, while the 75-branch network shows a 56.6–71.5% reduction in HV standard deviation. Full article
(This article belongs to the Special Issue Advances of Optimization Theory and Applications)
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31 pages, 2206 KB  
Article
Coordinated Allocation of Multi-Type DERs and EVCSs in Distribution Networks Using a Multi-Stage GSA Framework
by Arindam Roy and Vimlesh Verma
Mathematics 2026, 14(5), 894; https://doi.org/10.3390/math14050894 - 6 Mar 2026
Viewed by 521
Abstract
This study introduces a multi-stage, multi-objective optimization framework based on the Gravitational Search Algorithm (GSA) for determining the optimal sizing and placement of distributed energy resources (DERs) and associated infrastructure. The proposed approach considers solar distributed generation (DG) units with battery storage systems [...] Read more.
This study introduces a multi-stage, multi-objective optimization framework based on the Gravitational Search Algorithm (GSA) for determining the optimal sizing and placement of distributed energy resources (DERs) and associated infrastructure. The proposed approach considers solar distributed generation (DG) units with battery storage systems (BSSs), wind DGs, shunt capacitors (SCs) and electric vehicle charging stations (EVCSs). With the rapid adoption of electric vehicles as part of global decarbonization efforts, integrating EVCSs into already stressed distribution networks poses significant operational challenges, often requiring system reinforcement supported by renewable-based DGs. The uncoordinated deployment of EVCSs and DGs can exacerbate power losses and deteriorate voltage profiles. To address these issues, the first stage of the methodology employs GSA to optimally allocate solar DGs with BSSs, wind DGs and SCs, targeting objectives such as minimizing power losses, enhancing voltage stability and alleviating substation loading. The second stage identifies optimal locations and maximum feasible capacities for EVCS integration. Finally, the third stage upgrades the network to mitigate the impacts of EVCS integration. The effectiveness of the proposed approach is validated through simulations on a practical 52-bus, 11 kV distribution network under hourly varying load, solar irradiance and wind velocity conditions for all seasons. The simulation results show an 85% reduction in power losses during peak hours, with nodal voltages maintained above 0.95 p.u. under all scenarios. Additionally, net-zero grid power exchange during peak periods confirms the full islanded operation. Full article
(This article belongs to the Special Issue Advances of Optimization Theory and Applications)
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