Pigeon-inspired Optimization Algorithm Research Articles

Image Segmentation Model for Vicinagearth Security Technology of Unmanned Aerial Vehicle Using Improved Pigeon-Inspired Optimization

The vicinagearth security technology system covers a wide range of fields such as low-altitude security, underwater security, and cross-domain security. Among them, unmanned aerial vehicle (UAV) security will become one of the evolving forms of its security technology, and how to improve the segmentation and recognition ability of UAV visual reconnaissance system for maritime targets through improvement will become the key to low-altitude security. Due to the fact that maritime target images are characterized by complex weather, strong interference, high speed requirement and large data volume, the traditional segmentation methods are not suitable for maritime small-target (MST) segmentation and recognition. Therefore, this paper proposes a threshold image segmentation (TIS) method based on an improved pigeon-inspired optimization (PIO) algorithm to provide a better method for segmentation and recognition of MST. First, this study proposes CCPIO based on the horizontal crossover search (HCS) and vertical crossover search (VCS) strategy, which effectively improves the search efficiency of PIO and the ability to jump out of local optimum. And the optimization performance of CCPIO is effectively verified by comparing it with 10 peer algorithms through benchmark function experiments. Further, in this paper, the proposed CCPIO-TIS segmentation model is proposed by combining CCPIO with non-local means, 2D histogram, and Kapur’s entropy. The proposed CCPIO-TIS model is also used for the segmentation and recognition of real MST images, and the results of the experimental comparison and evaluation analysis show that the proposed model has higher quality segmentation results than 12 models of the same type. In summary, this study can provide an efficient and accurate artificial intelligence model for segmentation and recognition of maritime small-target.

Enhancing intrusion detection system using rectified linear unit function in pigeon inspired optimization algorithm

The increasing rate of cybercrime in the digital world highlights the importance of having a reliable intrusion detection system (IDS) to detect unauthorized attacks and notify administrators. IDS can leverage machine learning techniques to identify patterns of attacks and provide real-time notifications. In building a successful IDS, selecting the right features is crucial as it determines the accuracy of the predictions made by the model. This paper presents a new IDS algorithm that combines the rectified linear unit (ReLU) activation function with a pigeon-inspired optimizer in feature selection. The proposed algorithm was evaluated on network security layer - knowledge discovery in databases (NSL-KDD) datasets and demonstrated improved performance in terms of training speed and accuracy compared to previous IDS models. Thus, the use of the ReLU activation function and a pigeon-inspired optimizer in feature selection can significantly enhance the effectiveness of an IDS in detecting unauthorized attacks.

A Multi-Objective Pigeon-Inspired Optimization Algorithm for Community Detection in Complex Networks

Community structure is a very interesting attribute and feature in complex networks, which has attracted scholars’ attention and research on community detection. Many single-objective optimization algorithms have been migrated and modified to serve community detection problems. Due to the limitation of resolution, the final algorithm implementation effect is not ideal. In this paper, a multi-objective community detection method based on a pigeon-inspired optimization algorithm, MOPIO-Net, is proposed. Firstly, the PIO algorithm is discretized in terms of the solution space representation, position, and velocity-updating strategies to adapt to discrete community detection scenarios. Secondly, by minimizing the two objective functions of community score and community fitness at the same time, the community structure with a tight interior and sparse exterior is obtained. Finally, for the misclassification caused by boundary nodes, a mutation strategy is added to improve the accuracy of the final community recognition. Experiments on synthetic and real networks verify that the proposed algorithm is more accurate in community recognition compared to 11 benchmark algorithms, confirming the effectiveness of the proposed method.

Active disturbance rejection heading control of USV based on parameter tuning via an improved pigeon-inspired optimization

An improved active disturbance rejection control (ADRC) algorithm is proposed in this paper to enhance the heading control capabilities of unmanned surface vehicles (USVs) under wind and wave disturbances. The algorithm introduces two enhancements: parameter tuning and fitting, alongside the optimization of the nonlinear function in the ADRC algorithm. First, the parameter tuning employs an improved pigeon-inspired optimization (PIO) algorithm, which encompasses two strategies: the adaptive strategy and the wandering strategy. Parameter fitting ensures discretely optimized value transition into a continuous state, allowing dynamic parameter adjustments. Second, the optimization of the nonlinear function uses the D-value fitting method. Overall, the improved ADRC algorithm significantly enhances response speed to heading control commands for USVs, fortifying their resistance against wind and wave disturbances. Our proposed algorithm provides a new approach to achieve precise USV heading control.

Metropolis criterion pigeon-inspired optimization for multi-UAV swarm controller

This paper presents a new multiple unmanned aerial vehicle swarm controller based on Metropolis criterion. This paper presents the design of a controller, utilizing the improved Metropolis criterion pigeon-inspired optimization (IMCPIO) and proportional-integrational-derivative (PID) algorithms, and conducts comparative experiments. Simulation outcomes demonstrate the enhanced performance of the multi-unmanned aerial vehicle formation controller, which is based on IMCPIO, when compared to the basic pigeon-inspired optimization (PIO) algorithm and the genetic algorithm. The IMCPIO algorithm for the energy difference discrimination makes it a faster convergence and more stable effective optimization. Hence, the controller introduced in this study proves to be both practical and resilient.

Multi-objective optimization model for railway heavy-haul traffic: Addressing carbon emissions reduction and transport efficiency improvement

This paper establishes a multi-objective optimization model for railway heavy-haul trains, focusing on reducing carbon emissions and improving transport efficiency. The model integrates optimization of the route and the vehicle load rate, significantly reducing carbon emissions and enhancing transport efficiency. It addresses the challenges and characteristics of heavy-haul trains, introducing multi-objective optimization problems related to transport carbon emissions and efficiency. Using a pigeon-inspired optimization algorithm, the model considers joint constraints between carbon emissions and transport efficiency objectives. To overcome challenges in multi-objective transportation problems, the paper proposes a forward-learning pigeon-inspired optimization algorithm based on a surrogate-assisted model. This approach calculates the quality of the candidate solution using a surrogate model, reducing time costs. The algorithm employs a forward-learning strategy to enhance learning from non-dominant solutions. Experimental validation with benchmark functions confirms the effectiveness of the model and offers optimized solutions. The proposed method reduces carbon emissions while maintaining transport efficiency, contributing innovative ideas for the development of sustainable heavy-duty trains.

A Novel Framework for Forest Above-Ground Biomass Inversion Using Multi-Source Remote Sensing and Deep Learning

The estimation of forest above-ground biomass (AGB) can be significantly improved by leveraging remote sensing (RS) and deep learning (DL) techniques. In this process, it is crucial to obtain appropriate RS features and develop a suitable model. However, traditional methods such as random forest (RF) feature selection often fail to adequately consider the complex relationships within high-dimensional RS feature spaces. Moreover, challenges related to parameter selection and overfitting inherent in DL models may compromise the accuracy of AGB estimation. Therefore, this study proposes a novel framework based on freely available Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical data. Firstly, we designed new indices through the formula analogous with vegetation index calculation to integrate multidimensional spectral and structural information. Then, leveraging the simplicity of computational principles, a pigeon-inspired optimization algorithm (PIO) was introduced into a bi-directional long short-term memory neural network (PIO-BiLSTM), which achieved the set objective function through repeated iteration and validation to obtain the optimal model parameters. Finally, to verify the framework’s effect, we conducted experiments in two different tree species and compared another seven classical optimization algorithms and machine learning models. The results indicated that the new indices significantly improved the inversion accuracy of all models in both categories, and the PIO-BiLSTM model achieved the highest accuracy (Category-1: R2 = 0.8055, MAE = 8.8475 Mg·ha−1, RMSE = 12.2876 Mg·ha−1, relative RMSE = 18.1715%; Category-2: R2 = 0.7956, MAE = 1.7103 Mg·ha−1, RMSE = 2.2887 Mg·ha−1, relative RMSE = 9.3000%). Compared with existing methods, the proposed framework greatly reduced the labor costs in parameter selection, and its potential uncertainty also decreased by up to 9.0%. Furthermore, the proposed method has a strong generalization ability and is independent of tree species, indicating its great potential for future forest AGB inversion in wider regions with diverse forest types.

Fault Recovery of Distribution Network with Distributed Generation Based on Pigeon-Inspired Optimization Algorithm

In this paper, a fault recovery strategy for a distribution network based on a pigeon-inspired optimization (PIO) algorithm is proposed to improve the recoverability of the network considering the increased proportion of distributed energy resources. First, an improved Kruskal algorithm-based island partitioning scheme is proposed considering the electrical distance and important load level during the island partitioning process. Secondly, a mathematical model of fault recovery is established with the objectives of reducing active power losses and minimizing the number of switching actions. The conventional PIO algorithm is improved using chaos, reverse strategy, and Cauchy perturbation strategy, and the improved pigeon-inspired optimization (IPIO) algorithm is applied to solve the problem of fault recovery of the distribution network. Finally, simulation analysis is carried out to verify the effectiveness of the proposed PIO algorithm considering a network restauration problem after fault. The results show that compared with traditional algorithms, the proposed PIO algorithm has stronger global search capability, effectively improving the node voltage after restauration and reducing circuit loss.

Enhancing sparse regression modeling of hysteresis with optimized PIO algorithm in piezo actuator

Abstract The extensive application of piezo actuators is attributed to their high responsiveness and ability to achieve nanoscale steps. However, the accuracy and stability of motion are seriously affected by hysteresis caused by nonlinear characteristics. In this paper, a pigeon-inspired optimization (PIO) algorithm based on dynamic opposite learning (DOL) is proposed to address the issue of nonlinear modeling accuracy in piezo actuators by integrating the sparse identification of nonlinear dynamics (SINDy) method. Firstly, the DOL strategy is employed to introduce reverse pigeon flock into the PIO algorithm, thereby enhancing population diversity and optimization performance. Secondly, through combining the DOLPIO algorithm with the SINDy algorithm, sparse optimization for the penalty process in SINDy algorithm is conducted and the sparse coefficient is optimized based on modeling accuracy. Thirdly, the DOLPIO algorithm is utilized again to optimize the framework of optimized sparse penalty model in order to improve overall modeling accuracy. Finally, experiments are conducted on an established platform to validate the effectiveness of this algorithm.

Coverage Path Planning of UAV Based on Linear Programming—Fuzzy C-Means with Pigeon-Inspired Optimization

In contrast to rotorcraft, fixed-wing unmanned aerial vehicles (UAVs) encounter a unique challenge in path planning due to the necessity of accounting for the turning radius constraint. This research focuses on coverage path planning, aiming to determine optimal trajectories for fixed-wing UAVs to thoroughly explore designated areas of interest. To address this challenge, the Linear Programming—Fuzzy C-Means with Pigeon-Inspired Optimization algorithm (LP-FCMPIO) is proposed. Initially considering the turning radius constraint, a linear-programming-based model for fixed-wing UAV coverage path planning is established. Subsequently, to partition multiple areas effectively, an improved fuzzy clustering algorithm is introduced. Employing the pigeon-inspired optimization algorithm as the final step, an approximately optimal solution is sought. Simulation experiments demonstrate that the LP-FCMPIO, when compared to traditional FCM, achieves a more balanced clustering effect. Additionally, in contrast to traditional PIO, the planned flight paths display improved coverage of task areas, with an approximately 27.5% reduction in the number of large maneuvers. The experimental results provide validation for the effectiveness of the proposed algorithm.

Stacked Denoising Autoencoder Based Parkinson’s Disease Classification using Improved Pigeon-Inspired Optimization Algorithm

One of the most common neurological conditions caused by gradual brain degeneration is Parkinson's disease (PD). Although this neurological condition has no known treatment, early detection and therapy can help patients improve their quality of life. An essential patient's health record is made of medical images used to control, manage, and treat diseases. However, in computer-based diagnostics, disease classification is a difficult task. To overcome this problem, this paper introduces a stacked denoising Autoencoder (SDA) for Parkinson's disease classification. The main aim of this paper is to derive an optimal feature selection design for an effective PD classification. Improved Pigeon-Inspired Optimization (IPIO) algorithm is introduced to enhance the performance of the classifier. Thus, the classification result improved by the optimal features and also increased the sensitivity, accuracy, and specificity in the medical image diagnosis. The proposed scheme is implemented in PYTHON and compared with traditional feature selection models and other classification approaches. The experimental outcomes show that the proposed method yields a superior classification of PD than the current state-of-the-art method

A Sequential Hybrid Optimization Algorithm (SHOA) to Solve the Hybrid Flow Shop Scheduling Problems to Minimize Carbon Footprint

In today’s world, a situational awareness of sustainability is becoming increasingly important. Leaving a better world for future generations is becoming the main interest of many studies. It also puts pressure on managers to change production methods in most industries. Reducing carbon emissions in industry today is crucial to saving our planet. Theoretical research and practical industry requirements diverge, even though numerous researchers have tackled various strategies to handle carbon emission problems. Therefore, this work considers the carbon emission problem of the furniture manufacturing industry in Hosur, Tamilnadu, India. The case study company has a manufacturing system that resembles a hybrid flow shop (HFS) environment. As the HFS scheduling problems are NP-hard in nature, exact solution techniques could not be used to solve the problems. Hence, a sequential hybrid optimization algorithm (SHOA) has been developed in this paper to minimize the carbon footprint. In the SHOA, the pigeon-inspired optimization algorithm (PIOA) is hybridized sequentially with the firefly algorithm (FA). A computational experimental design is proposed to analyze the efficiency of the introduced strategy, and the solutions indicate that the developed approach could reduce the carbon footprint by up to 9.82%. The results motivate us to implement the proposed algorithm in the manufacturing industry to reduce the carbon footprint.

Editorial

Dear Readers,  At the end of this year, it gives me great pleasure to announce the eleventh regular issue of 2023. In this issue, various topical aspects of computer science are covered in 6 articles by 18 authors from 7 countries (Argentina, Brazil, China, France, Germany, Spain, and Turkey). I would like to thank all the authors for their sound research and the editorial board for the highly valuable review effort and comments for improvement. These contributions, together with the generous support of the consortium members, sustain the quality of our journal. I am looking forward to continuing my work as Managing Editor-in-Chief with the J.UCS community.  In an ongoing effort to further strengthen our journal, I would like to expand the editorial board: If you are a tenured associate professor or above with a strong publication record, you are welcome to apply to join our editorial board. We are also interested in high-quality proposals for special issues on new topics and trends. Please consider yourself and encourage your colleagues to submit high-quality articles or special issue proposals for our journal. Finally, we are looking for further financial support from potential consortium members to cover the costs of publishing the journal next year.  In this regular issue, I am very pleased to introduce the following 6 accepted articles: F. Kebire Bardak, M. Nuri Seyman, and Feyzullah Temurtaş from Turkey present in their research a hybrid algorithm for emotion classification based on electroencephalogram signals, which is composed of a radial basis function neural network and a probabilistic neural network. In a collaborative research effort between Spain and France, Anita Herrera, Ángel Arroyo, Alfredo Jiménez and Álvaro Herrero conduct a comprehensive review of the different techniques and models with regard to Artificial Intelligence when applied to the tourism industry. In a collaboration between researchers from Brazil and Germany, Ana Cristina Alves de Oliveira, Marco Aurélio Spohn, Christof Fetzer, Le Quoc Do, and André Martin aim to address the cost problem of DaaS by developing a model that optimizes the cost of querying distributed data sources over virtual machines spread across multisite data centers. Herminia Beatriz Parra and Marcela Vegetti from Argentina present in their article OntoFoCE, Ontology for Electronic Mail Forensics, which is a specific ontology for the forensic analysis of emails to help the computer expert in validating an email presented as judicial evidence. Valdicélio Santos and Michel S. Soares from Brazil propose a framework, and then the design and further evaluation of a web-based application to support software architects in using the activities and tasks of the architecture conceptualization clause based on the ISO/IEC/IEEE 42020 framework. Last but not least, Yunwu Xu and Yan Li from China contribute to the improvement of an existing wireless sensor network coverage optimization method which is based on the pigeon-inspired optimization algorithm.  Season greetings to all of you, relaxing holidays and ‘Enjoy Reading’!  Cordially,  Christian Gütl, Managing Editor  Graz University of Technology, Graz, Austria

Wireless Sensor Network Coverage Optimization for Internet of Things

The objective of this work is to improve the existing Wireless Sensor Network coverage optimization method. The pigeon-inspired optimization algorithm was first evaluated, and its shortcomings were noted. The pigeon-inspired optimization method was then enhanced with the good point set, Yin-Yang optimization algorithm, and opposition-based learning. To test the improved algorithm, five representative standard functions were chosen: sphere function (f1), Rosenbrock function (f2), Levy function (f3), Schwefel function (f4), and Levy function N.13 (f5). The algorithm's speed of convergence may be determined by the first two functions, which are unimodal. The final three functions, which are multimodal, can extract several local optimal values from the local optimum. In comparison with other known algorithms, the improved Yin-Yang PIO algorithm showed the highest optimization accuracy and stability. Three sets of experiments were performed to optimize the WSN coverage with different parameters. The first series of experiments suggest that Yin–Yang PIO has the best optimization effect, with a coverage rate of 99.51% (10.22% higher with PIO and 6.41% higher compared with PSO). The second and third series of experiments show that Yin-Yang PIO significantly increased the WSN coverage ratio, up to 99.9%. The algorithm can be applied to optimize WSN coverage in various environments. Future research can extend the research scope to include other optimization problems in IoT. 

Hierarchical Decision-Making Framework for Multi-UAV Task Assignment via Enhanced Pigeon-Inspired Optimization

Effective task assignment decisions are paramount for ensuring reliable task execution in multi-UAV systems. However, in the development of feasible plans, challenges stemming from extensive and prolonged task requirements are encountered. This paper establishes a decision-making framework for multiple unmanned aerial vehicles (multi-UAV) based on the well-known pigeon-inspired optimization (PIO) algorithm. By addressing the problem from a hierarchical structural perspective, the initial stage involves minimizing the global objective of the flight distance cost after obtaining the entire task distribution and task requirements, utilizing the global optimization capability of the classical PIO algorithm to allocate feasible task spaces for each UAV. In the second stage, building upon the decisions made in the preceding stage, each UAV is abstracted as an agent maximizing its own task execution benefits. An improved version of the PIO algorithm modified with a sine-cosine search mechanism is proposed, enabling the acquisition of the optimal task execution sequence. Simulation experiments involving two different scales of UAVs validate the effectiveness of the proposed methodology. Moreover, dynamic events such as UAV damage and task changes are considered in the simulation to validate the efficacy of the two-stage framework.

Novel sustainable urban management framework based on solar energy and digital twin

This paper proposes a sustainable energy management method for microgrids (MGs) in the urban area while taking into account energy storage systems (ESSs) and renewable energy (RE). This study integrates RE, such as solar and wind energy production, into the power grid, and formulates the optimum dispatch scheme of hybrid energy production, based on ESS charge and discharge planning using their digital models. Electricity is exchanged among the MG systems and the utilities on a daily basis. MG systems' cost-effective dispatch per day is solved by an improved pigeon-inspired optimization algorithm (IPIOA) considering the time-of-use (TOU) and other technical limitations. IPIOA can be modified in various ways for finding possible spaces with greater efficiency. This study uses IPIOA to manage power costs economically for Islanding and non-Islanding cases and compares the outcomes with other methods. Additionally, it uses micro-turbines as well as REs, ESSs, and Islanding and non-Islanding cases to schedule MGs optimally. In comparison to a number of existing algorithms, the new approach has proven to be more robust, reliable, and efficient.

Determination of residual levels of procymidone in rapeseed oil using near-infrared spectroscopy combined with multivariate analysis

The issue of pesticide residues has always been a hot topic at home and abroad. A method for the quantitative detection of procymidone residues in grain and oil products using near-infrared (NIR) spectroscopy has been proposed. First, a NIR spectrometer was used to collect spectral data from rapeseed oil samples with different concentrations of procymidone residues. Based on full-spectrum data, the wavelength points selected by bootstrapping soft shrinkage (BOSS) algorithm, competitive adaptive reweighted sampling (CARS) algorithm, and variable combination population analysis (VCPA) algorithm then were compared and were quantified using support vector regression (SVR) model. Simultaneously, the prediction results of the SVR model optimized by dung beetle optimizer (DBO) algorithm and pigeon-inspired optimization (PIO) algorithm were compared using the full-spectrum data. Finally, the wavelength selection algorithms and parameter optimization algorithms with the best prediction results were selected for comparison and combination. In light of the outcomes, the three spectral characteristic wavelength selection algorithms and the two optimization algorithms can improve the coefficient of determination (RP2) and reduce the root mean square error of prediction (RMSEP). The SVR model that utilizing CARS and PIO algorithm demonstrates the best generalization performance among all models evaluated, and the RP2 is 0.9939 with a RMSEP of 2.3435 mg·kg−1. The results indicate that the high-precision and rapid detection of procymidone in edible oil can be achieved using the SVR model optimized by input feature and parameter based on NIR spectral data. This has great significance in ensuring the safety of grain and oil food.

Daily prediction method of dust accumulation on photovoltaic (PV) panels using echo state network with delay output

Dust accumulation over time can be one of the main causes of uncertainty in the output of photovoltaic (PV) systems. In order to better understand these losses, this paper established a daily dust accumulation prediction model for PV panels based on the delay output echo state network (DESN). A pigeon-inspired optimization (PIO) algorithm with adaptive Cauchy (AC) mutation strategy was proposed, which can optimize the reservoir parameters (such as leakage rate, spectral radius, and input scaling) of DESN, shorten the solution time, and improve search speed. Based on typical meteorological and air quality data, as well as daily accumulated dust weight recorded from the experimental platform, model training and testing were carried out. According to the Pearson correlation coefficient, the relationship between the environment parameters (humidity, wind speed, wind direction, PM2.5, PM10, and rainfall) and the dust accumulation was obtained. The results show that the prediction accuracy of AC-PIO-DESN is better than other methods for meteorological and air quality data. The mean absolute percentage error (MAPE) for humidity, irradiance, PM2.5 and PM10 were 4.0743%, 4.4958%, 10.6231% and 12.8402%, respectively. In addition, the proposed daily dust prediction model has good robustness for 10-day samples, with an average relative error ranging from 0.65% to 54%. This method can provide data support for grid scheduling and PV panel cleaning strategy of PV power plants.

A novel method on the optimization problem of energy conservation in public buildings

ABSTRACT In recent years, the problem of energy consumption has received considerable attention. Among them, the proportion of energy consumption in public buildings accounts for a substantial part, and there is a large space for energy saving in this field. Practically, the major limiting factor for energy consumption optimization is indoor environment comfort degree. As such, a trade-off problem between energy conservation and indoor environment must be resolved. In this paper, an office in a public building is chosen as the experimental object, with 2520 real observations collected for two months, a series of intelligence based techniques are combined and applied to deal with this optimization problem. First, Partial Rank Correlation Coefficient (PRCC) was employed to perform correlation analysis for a set of candidate variables, such that the computational time of subsequent model training is reduced by 8.32 seconds as compared to the case where no PRCC is in use. Following this, Genetic Algorithm (GA)-based Levenberg-Marquardt algorithm (LM) are applied collectively to optimize the conventional Back Propagation (BP) neural network, and results suggest that there was a marked improvement in neural model performance, on one hand the epoch number for convergence drops from 72 to 13, on the other hand the mean squared error for test set is reduced by 7.46e-4 (from 9.93e-4 to 2.47e-4). Finally, pigeon-inspired Optimization (PIO) algorithm is employed to address the formulated optimization problem and the optimal value for Predicted Mean Vote (PMV) and Indoor Air Quality (IAQ) are −0.4775 and 0.007826, respectively. To verify the validity of our results that derived by the proposed approach, corresponding parameters are applied in a practical scenario. The experimental result indicates that the energy efficiency is improved by around 18.6% compared to that yielded before optimization is performed, which demonstrates the applicability and effectiveness of the proposed method.

Group Coevolution and Immigration Pigeon-Inspired Optimized Dual-layer Controller for Aerial Manipulator Trajectory Tracking

The aerial manipulator expands the scope of unmanned aerial vehicle (UAV)’s application as well as increases the difficulties in the design of the controller. To better control the aerial manipulator for different trajectories tracking under different conditions, a new dual-layer controller is designed in this paper. The integral backstepping sliding mode controller (IBSMC) is applied to the outer-loop controller and backstepping controller (BC) is applied to the inner-loop controller. To improve the performance of the system, an improved pigeon-inspired optimization (PIO) algorithm called group coevolution and immigration pigeon-inspired optimization (GCIPIO) algorithm is proposed to optimize the controller parameters of IBSMC. GCIPIO algorithm utilizes the group coevolution and immigration mechanisms. A series of simulations are conducted to show the advantage of the proposed method. The results illustrate that the proposed method ensures the closed-loop system has less end-effector tracking error.