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Mathematics
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Advances in Optimization Algorithms and Its Applications
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Advances in Optimization Algorithms and Its Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 1762

Special Issue Editor

Prof. Dr. Heming Jia

E-Mail Website
Guest Editor
Department of Information Engineering, Sanming University, Sanming, China
Interests: remora optimization algorithm (ROA); crayfish optimization algorithm (COA); catch fish optimization algorithm (CFOA); bio-inspired computing; nature-inspired computing; swarm intelligence; artificial intelligence; meta-heuristic modeling and optimization algorithms; evolutionary computations; multilevel image segmentation; feature selection; combinatorial problems

Special Issue Information

Dear Colleagues,

As the landscape of technological advancements continues to evolve at an unprecedented pace, the demand for efficient and effective optimization algorithms has grown exponentially across diverse engineering domains. These algorithms play a pivotal role in solving complex real-world problems, ranging from resource allocation and system design to parameter tuning and decision-making processes. Recognizing this need, the field of optimization algorithms has witnessed significant advancements, with bio-inspired approaches emerging as a prominent category due to their innate ability to mimic the dynamic and intelligent behaviors observed in nature.

The Special Issue, titled "Advances in Optimization Algorithms and Its Applications", will consolidate the latest research endeavors that have pushed the boundaries of optimization methodologies, particularly those inspired by biological systems. By harnessing the power of evolutionary processes, swarm intelligence, and other natural phenomena, these algorithms demonstrate remarkable potential in tackling intricate challenges characterized by non-convexity, nonlinearity, high dimensionality, and stringent constraints.

This Special Issue invites submissions from researchers worldwide, showcasing cutting-edge works that contribute to the theoretical foundations and practical applications of optimization algorithms. Contributions are sought to cover a broad spectrum of topics, including but not limited to the following:

Novel Bio-inspired Optimization Algorithms: Presentations of new optimization algorithms inspired by biological systems, such as genetic algorithms, particle swarm optimization, ant colony optimization, and artificial neural networks, with a focus on their unique mechanisms, performance enhancements, and theoretical analyses.

Hybrid and Multi-objective Optimization: Investigations into the fusion of bio-inspired algorithms with traditional optimization techniques or among different bio-inspired approaches, as well as strategies for solving multi-objective optimization problems, balancing conflicting objectives, and achieving Pareto optimality.

Applications in Engineering and Beyond: Demonstrations of how these advanced optimization algorithms are being applied to solve pressing engineering problems in areas like aerospace, the automotive industry, civil engineering, energy systems, and manufacturing. Additionally, explorations of their potential in other disciplines, such as finance, healthcare, and environmental management, are also encouraged.

Performance Evaluation and Benchmarking: Comparative studies evaluating the performances of bio-inspired optimization algorithms on standard and real-world benchmarks, highlighting their strengths, weaknesses, and suitability for specific problem types.

Computational Intelligence and Machine Learning Integration: Discussions on how computational intelligence techniques, including machine learning and deep learning, can be integrated with optimization algorithms to further enhance their capabilities and adaptability.

By featuring high-quality research in these and related areas, this Special Issue will foster interdisciplinary collaboration, promote knowledge sharing, and inspire future directions in the ever-evolving field of optimization algorithms and their applications.

Prof. Dr. Heming Jia
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. Mathematics is an international peer-reviewed open access semimonthly 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.

Keywords

  • bio-inspired optimization algorithms
  • optimization algorithms
  • meta-heuristics
  • swarm intelligence
  • engineering applications
  • engineering design problems
  • real-world applications
  • feature selection
  • image segmentation
  • constraint handling
  • benchmarks
  • novel approaches
  • complicated optimization problems
  • industrial problems

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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36 pages, 9610 KiB  
Article
Multi-Strategy Enhanced Secret Bird Optimization Algorithm for Solving Obstacle Avoidance Path Planning for Mobile Robots
by Libo Xu, Chunhong Yuan and Zuowen Jiang
Mathematics 2025, 13(5), 717; https://doi.org/10.3390/math13050717 - 23 Feb 2025
Viewed by 597
Abstract
Mobile robots play a pivotal role in advancing smart manufacturing technologies. However, existing Obstacle avoidance path Planning (OP) algorithms for mobile robots suffer from low stability and applicability. Therefore, this paper proposes an enhanced Secret Bird Optimization Algorithm (SBOA)-based OP algorithm for mobile [...] Read more.
Mobile robots play a pivotal role in advancing smart manufacturing technologies. However, existing Obstacle avoidance path Planning (OP) algorithms for mobile robots suffer from low stability and applicability. Therefore, this paper proposes an enhanced Secret Bird Optimization Algorithm (SBOA)-based OP algorithm for mobile robots to address these challenges, termed AGMSBOA. Firstly, an adaptive learning strategy is introduced, where individuals enhance the diversity of the algorithm’s population by summarizing relationships among candidates of varying quality, thereby strengthening the algorithm’s ability to locate globally optimal obstacle avoidance path regions. Secondly, a group learning strategy is incorporated by dividing the population into learning and teaching groups, enhancing the algorithm’s exploitation capabilities, improving the accuracy of obstacle avoidance path planning, and reducing actual runtime. Lastly, a multiple population evolution strategy is proposed, which balances the exploration/exploitation phases of the algorithm by analyzing the nature of different individuals, improving the algorithm’s ability to escape suboptimal obstacle avoidance path traps. Subsequently, AGMSBOA was used to solve the OP problem on five maps and two OP problems in real-world environments. The experiments illustrate that AGMSBOA achieves more than 5% performance improvement in path length and a 100–win rate in runtime metrics, as well as faster convergence and stability of the solution. Therefore, AGMSBOA proposed in this paper is an efficient, robust, and robust OP method for mobile robots. Full article
(This article belongs to the Special Issue Advances in Optimization Algorithms and Its Applications)
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39 pages, 13135 KiB  
Article
A Novel Improved Binary Optimization Algorithm and Its Application in FS Problems
by Boyuan Wu and Jia Luo
Mathematics 2025, 13(4), 675; https://doi.org/10.3390/math13040675 - 18 Feb 2025
Viewed by 538
Abstract
With the rapid advancement of artificial intelligence (AI) technology, the demand for vast amounts of data for training AI algorithms to attain intelligence has become indispensable. However, in the realm of big data technology, the high feature dimensions of the data frequently give [...] Read more.
With the rapid advancement of artificial intelligence (AI) technology, the demand for vast amounts of data for training AI algorithms to attain intelligence has become indispensable. However, in the realm of big data technology, the high feature dimensions of the data frequently give rise to overfitting issues during training, thereby diminishing model accuracy. To enhance model prediction accuracy, feature selection (FS) methods have arisen with the goal of eliminating redundant features within datasets. In this paper, a highly efficient FS method with advanced FS performance, called EMEPO, is proposed. It combines three learning strategies on the basis of the Parrot Optimizer (PO) to better ensure FS performance. Firstly, a novel exploitation strategy is introduced, which integrates randomness, optimality, and Levy flight to enhance the algorithm’s local exploitation capabilities, reduce execution time in solving FS problems, and enhance classification accuracy. Secondly, a multi-population evolutionary strategy is introduced, which takes into account the diversity of individuals based on fitness values to optimize the balance between exploration and exploitation stages of the algorithm, ultimately improving the algorithm’s capability to explore the FS solution space globally. Finally, a unique exploration strategy is introduced, focusing on individual diversity learning to boost population diversity in solving FS problems. This approach improves the algorithm’s capacity to avoid local suboptimal feature subsets. The EMEPO-based FS method is tested on 23 FS datasets spanning low-, medium-, and high-dimensional data. The results show exceptional performance in classification accuracy, feature reduction, execution efficiency, convergence speed, and stability. This indicates the high promise of the EMEPO-based FS method as an effective and efficient approach for feature selection. Full article
(This article belongs to the Special Issue Advances in Optimization Algorithms and Its Applications)
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