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Algorithms
Special Issues
Nature-Inspired Algorithms for Optimization
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Nature-Inspired Algorithms for Optimization

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 5616

Special Issue Editor

Dr. Rohit Salgotra

E-Mail Website
Guest Editor
Department of Computer Science, Swansea University, Swansea SA1 8EN, UK
Interests: nature-inspired algorithms/computing; computational intelligence; artificial intelligence; evolutionary computation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence is substantially contributing to optimization research. Evolutionary algorithms have been used in various domains to solve complex real-world problems. Designing adaptive properties of algorithms or making an algorithm self-adaptive with respect to the problem is the prime focus of modern optimization researchers. The aim of this Special Issue is to bring together researchers working on classical and new nature-inspired algorithms and to provide them with a forum to discuss the latest parametric adaptations in these algorithms and provide possible directions for future research.

Scope:

Original contributions in the adaptive properties of nature-inspired algorithms are welcome. The topics of interest include, but are not limited to:

  • Adapting the parameters of various algorithms;
  • New self-adaptive operators;
  • Reducing the computational complexity of the algorithm by population adaptations;
  • Hybrid algorithms;
  • Multi-algorithm strategies;
  • Comparison of these algorithms on standard/new benchmarks and highlighting the significance of using the new adaptive strategies;
  • Comparative surveys with new ideas on adaptations with dos and don’ts: i.e., best and worst practices, for performance evaluations, balancing the exploration and exploitation, and algorithm comparison;
  • Evaluations for real-world applications such as robustness, reliability, and implementation;
  • Statistical testing and validation of the proposed strategies;
  • Theoretical analysis with respect to the self-adaptive operators;
  • Application of new algorithms to real-world problems such as antenna array design, wireless sensor networks, time series forecasting, and others.

Dr. Rohit Salgotra
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. Algorithms is an international peer-reviewed open access monthly 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 1600 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

  • evolutionary algorithms
  • swarm intelligence
  • numerical optimization
  • benchmarking
  • theoretical and experimental analysis
  • adaptive properties

Published Papers (3 papers)

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Research

23 pages, 2236 KiB  
Article
Identification of Mechanical Parameters in Flexible Drive Systems Using Hybrid Particle Swarm Optimization Based on the Quasi-Newton Method
by Ishaq Hafez and Rached Dhaouadi
Algorithms 2023, 16(8), 371; https://doi.org/10.3390/a16080371 - 31 Jul 2023
Cited by 1 | Viewed by 1315
Abstract
This study presents hybrid particle swarm optimization with quasi-Newton (HPSO-QN), a hybrid optimization method for accurately identifying mechanical parameters in two-mass model (2MM) systems. These systems are commonly used to model and control high-performance electric drive systems with elastic joints, which are prevalent [...] Read more.
This study presents hybrid particle swarm optimization with quasi-Newton (HPSO-QN), a hybrid optimization method for accurately identifying mechanical parameters in two-mass model (2MM) systems. These systems are commonly used to model and control high-performance electric drive systems with elastic joints, which are prevalent in modern industrial production. The proposed method combines the global exploration capabilities of particle swarm optimization (PSO) with the local exploitation abilities of the quasi-Newton (QN) method to precisely estimate the motor and load inertias, shaft stiffness, and friction coefficients of the 2MM system. By integrating these two optimization techniques, the HPSO-QN method exhibits superior accuracy and performance compared to standard PSO algorithms. Experimental validation using a 2MM system demonstrates the effectiveness of the proposed method in accurately identifying and improving the mechanical parameters of these complex systems. The HPSO-QN method offers significant implications for enhancing the modeling, performance, and stability of 2MM systems and can be extended to other systems with flexible shafts and couplings. This study contributes to the development of accurate and effective parameter identification methods for complex systems, emphasizing the crucial role of precise parameter estimation in achieving optimal control performance and stability. Full article
(This article belongs to the Special Issue Nature-Inspired Algorithms for Optimization)
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16 pages, 1181 KiB  
Article
A Zoning Search-Based Multimodal Multi-Objective Brain Storm Optimization Algorithm for Multimodal Multi-Objective Optimization
by Jiajia Fan, Wentao Huang, Qingchao Jiang and Qinqin Fan
Algorithms 2023, 16(7), 350; https://doi.org/10.3390/a16070350 - 21 Jul 2023
Viewed by 1084
Abstract
For multimodal multi-objective optimization problems (MMOPs), there are multiple equivalent Pareto optimal solutions in the decision space that are corresponding to the same objective value. Therefore, the main tasks of multimodal multi-objective optimization (MMO) are to find a high-quality PF approximation in the [...] Read more.
For multimodal multi-objective optimization problems (MMOPs), there are multiple equivalent Pareto optimal solutions in the decision space that are corresponding to the same objective value. Therefore, the main tasks of multimodal multi-objective optimization (MMO) are to find a high-quality PF approximation in the objective space and maintain the population diversity in the decision space. To achieve the above objectives, this article proposes a zoning search-based multimodal multi-objective brain storm optimization algorithm (ZS-MMBSO). At first, the search space segmentation method is employed to divide the search space into some sub-regions. Moreover, a novel individual generation strategy is incorporated into the multimodal multi-objective brain storm optimization algorithm, which can improve the search performance of the search engineering. The proposed algorithm is compared with five famous multimodal multi-objective evolutionary algorithms (MMOEAs) on IEEE CEC2019 MMOPs benchmark test suite. Experimental results indicate that the overall performance of the ZS-MMBSO is the best among all competitors. Full article
(This article belongs to the Special Issue Nature-Inspired Algorithms for Optimization)
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23 pages, 4625 KiB  
Article
Optimized Approach for Localization of Sensor Nodes in 2D Wireless Sensor Networks Using Modified Learning Enthusiasm-Based Teaching–Learning-Based Optimization Algorithm
by Goldendeep Kaur, Kiran Jyoti, Nitin Mittal, Vikas Mittal and Rohit Salgotra
Algorithms 2023, 16(1), 11; https://doi.org/10.3390/a16010011 - 23 Dec 2022
Cited by 5 | Viewed by 1614
Abstract
Wireless Sensor Networks (WSNs) have a wonderful potential to interconnect with the physical world and collect data. Data estimation, long lifespan, deployment, routing, task scheduling, safety, and localization are the primary performance difficulties for WSNs. WSNs are made up of sensor nodes set [...] Read more.
Wireless Sensor Networks (WSNs) have a wonderful potential to interconnect with the physical world and collect data. Data estimation, long lifespan, deployment, routing, task scheduling, safety, and localization are the primary performance difficulties for WSNs. WSNs are made up of sensor nodes set up with minimal battery power to monitor and reveal the occurrences in the sensor field. Detecting the location is a difficult task, but it is a crucial characteristic in many WSN applications. Locating all of the sensor nodes efficiently to obtain the precise location of an occurrence is a critical challenge. Surveillance, animal monitoring, tracking of moving objects, and forest fire detection are just a few of the applications that demand precise location determination. To cope with localization challenges in WSNs, there is a variety of localization algorithms accessible in the literature. The goal of this research is to use various optimization strategies to solve the localization problem. In this work, a modified learning enthusiasm-based teaching–learning-based optimization (mLebTLBO) algorithm is used to cope with a 2D localization problem applying the notion of an exclusive anchor node and movable target nodes. A modified LebTLBO algorithm seeks to increase overall efficiency by assessing the exploration and exploitation abilities. The computational results reveal that this technique outperforms others with respect to localization errors in a 2D environment of WSN. Full article
(This article belongs to the Special Issue Nature-Inspired Algorithms for Optimization)
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