Submit to Applied Sciences Review for Applied Sciences Propose a Special Issue

Journal Browser

Novel Research and Application on Swarm Optimization and Bioinspired Optimization Algorithms

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (15 August 2023) | Viewed by 16707

Share This Special Issue

Special Issue Editors

Dr. Stylianos Pappas

E-Mail Website
Guest Editor

General Department, National and Kapodistrian University of Athens, Panepistimiopolis, 34400 Evoia, Greece
Interests: swarm optimization; artificial intelligence; bioinspired optimization algorithms, power systems; renewable energy sources; electric load forecasting; wind speed prediction; high voltage

Prof. Dr. Sokratis Katsikas

E-Mail Website
Guest Editor

Department of Information Security and Communication Technology, Norwegian University of Science and Technology, 2815 Gjøvik, Norway
Interests: information and cybersecurity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biological behaviors have recently been the foundation for many evolutionary optimization algorithms. One important category is swarm optimization algorithms (SOA), which are stochastic global optimization algorithms based on swarm intelligence. The major characteristic of SOA is that they do not use operators such as mutation, crossover, and replication but achieve evolution through antagonism and synergy between particles. The initial particle population is randomly generated, and each particle represents a possible solution. Through a simple and effective mechanism based on iterations of SOA, a random solution starts to form, and a global optimal solution is reached by following an optimal path and minimizing the cost. To date, SOA have been applied in many fields, such as engineering, evolutionary computing, biomedicine, navigation and communication, environmental sciences, cryptography, real time traffic control, and many more.

Dr. Stylianos Pappas
Prof. Dr. Sokratis Katsikas
Guest Editors

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. Applied Sciences 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 2400 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

swarm optimization
swarm robotics
swarm-based intelligence
bioinspired algorithms
estimation
modeling
evolutionary computing

Published Papers (11 papers)

Download All Papers

Research

18 pages, 7297 KiB

Open AccessArticle

Path Planning for Conformal Antenna Surface Detection Based on Improved Genetic Algorithm

by Yifan Ding, Xiaodong Du, Changrui Wang, Wei Tian, Chao Deng, Ke Li and Zihang Wang

Appl. Sci. 2023, 13(18), 10490; https://0-doi-org.brum.beds.ac.uk/10.3390/app131810490 - 20 Sep 2023

Cited by 1 | Viewed by 670

Abstract

The conformal antenna is a precision device installed on the wing of an aircraft, and its components are distributed on a curved surface. Quality detection is required after assembly. In solving the path planning problem for conformal antenna surface detection, the traditional genetic algorithm faces problems such as slow convergence and easily falling into a local optimal solution. To solve this problem, an improved genetic algorithm combining the historical optimal population (CHOP-IGA) is proposed. First, the algorithm uses the probability-based four-nearest-neighbor method to construct an initial population. Subsequently, the probabilities of the crossover and mutation operators are dynamically adjusted. Next, the algorithm applies the crossover and mutation operators to the population and performs mutation operations on each individual of the historical optimal population. Then, the fitness value is calculated and the next generation of individuals is selected from the parent, offspring, and historical optimal populations according to the elite selection strategy. Finally, the current best fitness is checked to determine whether updating the historical optimal population is necessary. When the termination condition is satisfied, the algorithm outputs the optimal result. Experiments showed that the algorithm satisfactorily solved the path planning problem for conformal antenna surface detection, with a 48.44% improvement in detection efficiency. Full article

(This article belongs to the Special Issue Novel Research and Application on Swarm Optimization and Bioinspired Optimization Algorithms)

► Show Figures

Figure 1

20 pages, 4961 KiB

Open AccessArticle

The Influences of Self-Introspection and Credit Evaluation on Self-Organized Flocking

by Qiang Zhao, Yu Luan, Shuai Li, Gang Wang, Minyi Xu, Chen Wang and Guangming Xie

Appl. Sci. 2023, 13(18), 10361; https://0-doi-org.brum.beds.ac.uk/10.3390/app131810361 - 16 Sep 2023

Viewed by 657

Abstract

For biological groups, the behaviors of individuals will have an impact on the alignment efficiency of the collective movement. Motivated by Vicsek’s pioneering research on self-organized particles and other related works about flocking behaviors, we propose two mathematical models based on the local information of individuals to include more realistic details in the interaction mechanism between individuals and the rest of the group during the flocking process. The local information of the individual refers to the local consistency, representing the degree of alignment with its neighbors. These two models are the self-introspection model, where the process of orientation adjustment of one individual is ruled by the degree of local consistency with the neighborhood, and the credit evaluation model, where the average orientation of the neighborhoods is weighed using the local consistency of the interacting individuals. Different metrics are calculated to analyze the effects of the model parameters and flocking parameters on groups. Simulation calculations indicate that the two improved models have certain advantages in terms of alignment efficiency for the group. Finally, the optimal model parameters are determined, and the effects of random noise on groups with a single behavior and mixed behaviors are analyzed. The results confirm that individuals with mixed behaviors still possess robustness against noise. This research would contribute to the further interdisciplinary cooperation that involves biology, ethology, and multi-agent complex systems. Full article

(This article belongs to the Special Issue Novel Research and Application on Swarm Optimization and Bioinspired Optimization Algorithms)

► Show Figures

Figure 1

42 pages, 1482 KiB

Open AccessArticle

Evolutionary Algorithms for Optimization Sequence of Cut in the Laser Cutting Path Problem

by Bonfim Amaro Junior, Guilherme Nepomuceno de Carvalho, Marcio Costa Santos, Placido Rogerio Pinheio and Joao Willian Lemos Celedonio

Appl. Sci. 2023, 13(18), 10133; https://0-doi-org.brum.beds.ac.uk/10.3390/app131810133 - 08 Sep 2023

Cited by 1 | Viewed by 679

Abstract

Efficiently cutting smaller two-dimensional parts from a larger surface area is a recurring challenge in many manufacturing environments. This point falls under the cut-and-pack (C&P) problems. This study specifically focused on a specialization of the cut path determination (CPD) known as the laser cutting path planning (LCPP) problem. The LCPP aims to determine a sequence of cutting and sliding movements for the head that minimizes the parts’ separation time. It is important to note that both cutting and glide speeds (moving the head without cutting) can vary depending on the equipment, despite their importance in real-world scenarios. This study investigates an adaptive biased random-key genetic algorithm (ABRKGA) and a heuristic to create improved individuals applied to LCPP. Our focus is on dealing with more meaningful instances that resemble real-world requirements. The experiments in this article used parameter values for typical laser cutting machines to assess the feasibility of the proposed methods compared to an existing strategy. The results demonstrate that solutions based on metaheuristics are competitive and that the inclusion of heuristics in the creation of the initial population benefits the execution of the evolutionary strategy in the treatment of practical problems, achieving better performance in terms of the quality of solutions and computational time. Full article

(This article belongs to the Special Issue Novel Research and Application on Swarm Optimization and Bioinspired Optimization Algorithms)

► Show Figures

Figure 1

17 pages, 4052 KiB

Open AccessArticle

Hybrid Algorithm of Improved Beetle Antenna Search and Artificial Fish Swarm

by Jian Ni, Jing Tang and Rui Wang

Appl. Sci. 2022, 12(24), 13044; https://0-doi-org.brum.beds.ac.uk/10.3390/app122413044 - 19 Dec 2022

Cited by 3 | Viewed by 1341

Abstract

The beetle antenna search algorithm (BAS) converges rapidly and runs in a short time, but it is prone to yielding values corresponding to local extrema when dealing with high-dimensional problems, and its optimization result is unstable. The artificial fish swarm algorithm (AFS) can achieve good convergence in the early stage, but it suffers from slow convergence speed and low optimization accuracy in the later stage. Therefore, this paper combines the two algorithms according to their respective characteristics and proposes a mutation and a multi-step detection strategy to improve the BAS algorithm and raise its optimization accuracy. To verify the performance of the hybrid composed of the AFS and BAS algorithms based on the Mutation and Multi-step detection Strategy (MMSBAS), AFS-MMSBAS is compared with AFS, the Multi-direction Detection Beetle Antenna Search (MDBAS) Algorithm, and the hybrid algorithm composed of the two (AFS-MDBAS). The experimental results show that, with respect to high-dimensional problems: (1) the AFS-MMSBAS algorithm is not only more stable than the MDBAS algorithm, but it is also faster in terms of convergence and operation than the AFS algorithm, and (2) it has a higher optimization capacity than the two algorithms and their hybrid algorithm. Full article

(This article belongs to the Special Issue Novel Research and Application on Swarm Optimization and Bioinspired Optimization Algorithms)

► Show Figures

Figure 1

22 pages, 2141 KiB

Open AccessArticle

A Compact Cat Swarm Optimization Algorithm Based on Small Sample Probability Model

by Zeyu He, Ming Zhao, Tie Luo and Yimin Yang

Appl. Sci. 2022, 12(16), 8209; https://0-doi-org.brum.beds.ac.uk/10.3390/app12168209 - 17 Aug 2022

Cited by 1 | Viewed by 913

Abstract

In this paper, a compact cat swarm optimization algorithm based on a Small Sample Probability Model (SSPCCSO) is proposed. In the same way as with previous algorithms, there is a tracking mode and a searching mode in the processing of searching for optimal solutions, but besides these, a novel differential operator is introduced in the searching mode, and it is proved that this could greatly enhance the search ability for the potential global best solution. Another highlight of this algorithm is that the gradient descent method is adopted to increase the convergence velocity and reduce the computation cost. More importantly, a small sample probability model is designed to represent the population of samples instead of the normal probability distribution. This representation method could run with low computing power of the equipment, and the whole algorithm only uses a cat with no historical position and velocity. Therefore, it is suitable for solving optimization problems with limited hardware. In the experiment, SSPCCSO is superior to other compact evolutionary algorithms in most benchmark functions and can also perform well compared to some population-based evolutionary algorithms. It provides a new means of solving small sample optimization problems. Full article

(This article belongs to the Special Issue Novel Research and Application on Swarm Optimization and Bioinspired Optimization Algorithms)

► Show Figures

Figure 1

30 pages, 5415 KiB

Open AccessArticle

An Adaptive Multiobjective Genetic Algorithm with Multi-Strategy Fusion for Resource Allocation in Elastic Multi-Core Fiber Networks

by Zhanqi Xu, Qian Xu, Jianxin Lv, Tao Ma and Tingting Chen

Appl. Sci. 2022, 12(14), 7128; https://0-doi-org.brum.beds.ac.uk/10.3390/app12147128 - 14 Jul 2022

Cited by 1 | Viewed by 1222

Abstract

Core switching on different links in optical networks enables network operators to allocate network resources more flexibly, so as to reduce the network request blocking ratio under limited resources. Facing a differentiated network environment and diversified user demands, network operators need to optimize multiple objectives that are independent and diversionary of each other, and to provide multiple resource allocation schemes whose objective values do not dominate each other. For the static routing, spectrum, and core assignment (RSCA) problem in elastic optical networks with multi-core fiber (MCF-EONs), there is no literature that simultaneously considers core switching and multiobjective optimization algorithms. This paper improves the existing models and algorithms to adapt to the RSCA problem. In this paper, the RSCA problem is formulated as an integer linear programming model to minimize both network request blocking and crosstalk ratios simultaneously by considering core switching and inter-core crosstalk. To solve the model efficiently, we, therefore, design a joint routing and core coding scheme supporting core switching and propose a multiobjective evolutionary algorithm based on decomposition with adaptation and multi-strategy fusion (MOEA/D-AMSF), which integrates the new mechanisms of hybrid initial population generation, adaptive crossover, and double-layer and multi-point mutation in different iteration stages. These new mechanisms accelerate algorithm convergence and enhance solution diversity. Simulation results show that the proposed algorithm can obtain more dominated and diverse solutions compared with the existing multiobjective algorithm without considering core switching. Full article

(This article belongs to the Special Issue Novel Research and Application on Swarm Optimization and Bioinspired Optimization Algorithms)

► Show Figures

Figure 1

18 pages, 3956 KiB

Open AccessArticle

Application of Multi-Objective Hyper-Heuristics to Solve the Multi-Objective Software Module Clustering Problem

by Haya Alshareef and Mashael Maashi

Appl. Sci. 2022, 12(11), 5649; https://0-doi-org.brum.beds.ac.uk/10.3390/app12115649 - 02 Jun 2022

Cited by 6 | Viewed by 1687

Abstract

Software maintenance is an important step in the software lifecycle. Software module clustering is a HHMO_CF_GDA optimization problem involving several targets that require minimization of module coupling and maximization of software cohesion. Moreover, multi-objective software module clustering involves assembling a specific group of modules according to specific cluster criteria. Software module clustering classifies software modules into different clusters to enhance the software maintenance process. A structure with low coupling and high cohesion is considered an excellent software module structure. In this study, we apply a multi-objective hyper-heuristic method to solve the multi-objective module clustering problem with three objectives: (i) minimize coupling, (ii) maximize cohesion, and (iii) ensure high modularization quality. We conducted several experiments to obtain optimal and near-optimal solutions for the multi-objective module clustering optimization problem. The experimental results demonstrated that the HHMO_CF_GDA method outperformed the individual multi-objective evolutionary algorithms in solving the multi-objective software module clustering optimization problem. The resulting software, in which HHMO_CF_GDA was applied, was more optimized and achieved lower coupling with higher cohesion and better modularization quality. Moreover, the structure of the software was more robust and easier to maintain because of its software modularity. Full article

(This article belongs to the Special Issue Novel Research and Application on Swarm Optimization and Bioinspired Optimization Algorithms)

► Show Figures

Figure 1

19 pages, 3381 KiB

Open AccessArticle

Population Symmetrization in Genetic Algorithms

by Grzegorz Kusztelak, Adam Lipowski and Jacek Kucharski

Appl. Sci. 2022, 12(11), 5426; https://0-doi-org.brum.beds.ac.uk/10.3390/app12115426 - 27 May 2022

Cited by 3 | Viewed by 1080

Abstract

The paper presents a memetic modification of the classical genetic algorithm by introducing a cyclic symmetrization of the population, symmetrizing the parental points around the current population leader. Such an operator provides a more spherical distribution of the population around the current leader, which significantly improves exploitation. The proposed algorithm was described, illustrated by examples, and theoretically analyzed. Its effectiveness was examined using a recognized benchmark, which includes the continuous functions test set on a multidimensional cube, to be minimized. Full article

(This article belongs to the Special Issue Novel Research and Application on Swarm Optimization and Bioinspired Optimization Algorithms)

► Show Figures

Figure 1

13 pages, 321 KiB

Open AccessArticle

A Two-Phase Evolutionary Method to Train RBF Networks

by Ioannis G. Tsoulos, Alexandros Tzallas and Evangelos Karvounis

Appl. Sci. 2022, 12(5), 2439; https://0-doi-org.brum.beds.ac.uk/10.3390/app12052439 - 25 Feb 2022

Cited by 2 | Viewed by 1499

Abstract

This article proposes a two-phase hybrid method to train RBF neural networks for classification and regression problems. During the first phase, a range for the critical parameters of the RBF network is estimated and in the second phase a genetic algorithm is incorporated to locate the best RBF neural network for the underlying problem. The method is compared against other training methods of RBF neural networks on a wide series of classification and regression problems from the relevant literature and the results are reported. Full article

(This article belongs to the Special Issue Novel Research and Application on Swarm Optimization and Bioinspired Optimization Algorithms)

► Show Figures

Figure 1

28 pages, 723 KiB

Open AccessArticle

Outlier Detection Based Feature Selection Exploiting Bio-Inspired Optimization Algorithms

by Souad Larabi-Marie-Sainte

Appl. Sci. 2021, 11(15), 6769; https://0-doi-org.brum.beds.ac.uk/10.3390/app11156769 - 23 Jul 2021

Cited by 8 | Viewed by 1943

Abstract

The curse of dimensionality problem occurs when the data are high-dimensional. It affects the learning process and reduces the accuracy. Feature selection is one of the dimensionality reduction approaches that mainly contribute to solving the curse of the dimensionality problem by selecting the relevant features. Irrelevant features are the dependent and redundant features that cause noise in the data and then reduce its quality. The main well-known feature-selection methods are wrapper and filter techniques. However, wrapper feature selection techniques are computationally expensive, whereas filter feature selection methods suffer from multicollinearity. In this research study, four new feature selection methods based on outlier detection using the Projection Pursuit method are proposed. Outlier detection involves identifying abnormal data (irrelevant features of the transpose matrix obtained from the original dataset matrix). The concept of outlier detection using projection pursuit has proved its efficiency in many applications but has not yet been used as a feature selection approach. To the author’s knowledge, this study is the first of its kind. Experimental results on nineteen real datasets using three classifiers (k-NN, SVM, and Random Forest) indicated that the suggested methods enhanced the classification accuracy rate by an average of 6.64% when compared to the classification accuracy without applying feature selection. It also outperformed the state-of-the-art methods on most of the used datasets with an improvement rate ranging between 0.76% and 30.64%. Statistical analysis showed that the results of the proposed methods are statistically significant. Full article

(This article belongs to the Special Issue Novel Research and Application on Swarm Optimization and Bioinspired Optimization Algorithms)

► Show Figures

Figure 1

20 pages, 738 KiB

Open AccessArticle

Optimization of Truck-Drone Parcel Delivery Using Metaheuristics

by Sarab AlMuhaideb, Taghreed Alhussan, Sara Alamri, Yara Altwaijry, Lujain Aljarbou and Haifa Alrayes

Appl. Sci. 2021, 11(14), 6443; https://0-doi-org.brum.beds.ac.uk/10.3390/app11146443 - 13 Jul 2021

Cited by 11 | Viewed by 3487

Abstract

This research addresses a variant of the traveling salesman problem in drone-based delivery systems known as the TSP-D. The TSP-D is a combinatorial optimization problem in which a truck and a drone collaborate to deliver parcels to customers, with the objective of minimizing the total delivery time. Determining the optimal solution is NP-hard; thus, the size of the problems that can be solved optimally is limited. Therefore, metaheuristics are used to solve the problem. Metaheuristics are adaptive and intelligent algorithms that have proved their success in many similar problems. In this study, a solution to the TSP-D problem using the greedy, randomized adaptive search procedure with two local search alternatives and a self-adaptive neighborhood selection scheme is presented. The proposed approach was tested on 200 instances with different properties from the publicly available “Instances of TSP with Drone” benchmark. Results were evaluated against state-of-the-art algorithms. Non-parametric statistical tests concluded that the proposed approach has comparable performance to the rival algorithms (