Pigeon-inspired Optimization Research Articles

Adaptive Cruise Predictive Control Based on Variable Compass Operator Pigeon-Inspired Optimization

A vehicle adaptive cruise system can control the speed and the safe distance between vehicles rapidly and effectively, which is an integral part of an intelligent driver assistance system. Adaptive cruise predictive control algorithms based on variable compass operator pigeon-inspired optimization (PIO) and PSO are proposed to improve the time response characteristics of multi-objective adaptive cruise system predictive control. Firstly, a longitudinal kinematic model of an adaptive cruise system was established and linearly discretized. Secondly, the multi-objective optimal cost function and parameter constraints were designed by integrating factors such as distance error, relative speed, acceleration and impact, and a mathematical model of the adaptive cruise predictive control optimization problem was constructed. Finally, PIO and PSO were used to solve the optimal control law for MPC and simulated by Matlab. The results show that the adaptive cruise system can reach a steady state quickly with the control laws of PIO or PSO. However, due to the global optimization and fast convergence characteristic, variable compass operator PIO has better time response characteristics.

Hybridization of Pigeon inspired with glowworm' swarm optimization based clustering technique in wireless sensor networks

In wireless sensor networks (WSN), clustering is treated as an energy efficient technique employed to achieve maximum network lifetime. But, the process of cluster head (CH) selection for stabilized network operation and prolonged network lifetime remains a challenging issue in WSN. To resolve this issue, this paper presents a new hybridization of pigeon inspired with glowworm swarm optimization (HPIGSO) algorithm based clustering technique in WSN. The proposed HPIGSO algorithm integrates the good characteristics of pigeon inspired optimization (PIO) algorithm and glowworm swarm optimization (GSO) algorithm. The proposed algorithm operates on three major stages namely initialization, CH selection and cluster construction. Once the nodes are deployed, initialization process takes place. Followed by, base station (BS) executes the HPIGSO algorithm and selects the CHs effectively. Subsequently, nearby nodes joins the CH and becomes cluster members (CMs), thereby cluster construction takes place. Finally, the CMs send the data to CHs which is then forwarded to BS via inter-cluster communication. The proficient performance of the HPIGSO method has been evaluated and the results portrayed that the HPIGSO algorithm prolonged the lifetime of WSN over the existing clustering techniques.

Maximum power point tracking and parameter estimation for multiple-photovoltaic arrays based on enhanced pigeon-inspired optimization with Taguchi method

The simulation, control and optimization of photovoltaic (PV) modules require the extraction of parameters from actual data and the construction of highly accurate PV cells. Multiple PV modules supplying power to a common load is the most common form of power distribution in PV systems. In these PV systems, providing separate maximum power point tracking (MPPT) technology for each PV module would increase the cost of the entire system. Determining how to accurately identify the internal parameter information of the PV modules and control the MPPT technology is the problem solved in this paper. we proposes an improved pigeon-inspired optimization (PIO) algorithm based on Taguchi method to solve the above problems. In this paper, we use the CEC2014 test library for testing and cross-sectional comparison. Experimental results show that the PIO algorithm based on Taguchi method is more competitive than other algorithms. The proposed algorithm uses measurement data to extract the unknown parameter in the PV modules and then uses this information to optimize the MPPT of all PV systems under partially shaded conditions (PSCs). Simulation results demonstrate the fitness value of the unknown parameters extracted by TPIO is 9.7525 × 10−4, which is better than the compared algorithms.

Active disturbance rejection controllers optimized via adaptive granularity learning distributed pigeon-inspired optimization for autonomous aerial refueling hose-drogue system

The autonomous aerial refueling (AAR) hose-drogue system (HDS) suffers the multi-wind disturbances leading to the low stability of drogue position, which is adverse for the successful probe-and-drogue AAR docking. This paper addresses the drogue position stability control problem in the presence of multi-wind disturbances. The finite-segment multi-body method is adopted to model the hose-drogue assembly as a link-connected system. A controllable drogue is equipped at the end of the hose to stabilize the drogue's relative position in the presence of tanker trailing vortex, receiver bow wave, atmospheric turbulence, and gust. Thus, the drogue active disturbance rejection controllers (ADRC) are designed to enhance the anti-disturbance ability and position stability of HDS. Besides, an improved pigeon-inspired optimization (PIO), adaptive granularity learning distributed PIO (AGLDPIO), is proposed to optimize the drogue lateral and vertical position controllers for realizing the optimal control effects and reducing the difficulties of parameter tuning. The simulation results show that the proposed optimized ADRC position controllers can effectively maintain the drogue swinging in a smaller range, which demonstrates the effectiveness and superiority of the proposed controllers.

A knee point-driven many-objective pigeon-inspired optimization algorithm

The number of solutions obtained is too large to provide a set of solutions with good performance in the nearby area of the true Pareto front when problem-specific preferences are unavailable. Therefore, this paper proposes a knee point-driven many-objective pigeon-inspired optimization algorithm (KnMAPIO). An environmental selection strategy based on knee-oriented dominance is proposed to improve selection pressure and population diversity. In addition, a new velocity updating equation with Gaussian distribution, Cauchy distribution and Levy distribution is proposed in this paper to provide new search directions and reduce the possibility of falling into local optima. Two types of experiments are carried out in this paper: one is to compare the proposed method with four other algorithms on the knee-oriented benchmark PMOPs to verify the algorithm’s performance in detecting the knee points and the knee region; another is to compare the proposed method with eight other state-of-the-art algorithms on the classic benchmark DTLZ and WFG. The results of both experiments verify the effectiveness of the proposed algorithm and the ability to approximate to the true Pareto front.

Hawk and pigeon’s intelligence for UAV swarm dynamic combat game via competitive learning pigeon-inspired optimization

Hawk and pigeon’s intelligence for UAV swarm dynamic combat game via competitive learning pigeon-inspired optimization

Path Tracking of an Underwater Snake Robot and Locomotion Efficiency Optimization Based on Improved Pigeon-Inspired Algorithm

This paper considers the tracking control of curved paths for an underwater snake robot, and investigates the methods used to improve energy efficiency. Combined with the path-planning method based on PCSI (parametric cubic-spline interpolation), an improved LOS (light of sight) method is proposed to design the controller and guide the robot to move along the desired path. The evaluation of the energy efficiency of robot locomotion is discussed. In particular, a pigeon-inspired optimization algorithm improved by quantum rules (QPIO) is proposed for dynamically selecting the gait parameters that maximize energy efficiency. Simulation results show that the proposed controller enables the robot to accurately follow the curved path and that the QPIO algorithm is effective in improving robot energy efficiency.

A Novel Metaheuristic Optimization based Clustering with Routing Scheme for IoT Mobile Edge Computing Platform

Largescale IoT applications with thousands of geo distributed IoT gadgets making huge volumes of data impose immense challenges to designing transmission mechanisms that offer data transfer has less latency and great scalability. In this work, an investigation of a hierarchical Edge-Cloud publishes or subscribe brokers method was performed with the help of an effective two-tier routing structure for alleviating such problems whenever sending event notices in large scale IoT mechanisms. In this technique, IoT gadgets use the benefits of nearby edge brokers deliberately positioned in edge network for data supplying services for minimizing latency. This manuscript introduces a Novel Metaheuristic Optimization based Clustering with Routing Scheme for IoT Mobile Edge Computing Platform, named MOCRS-IoTMEC model. The projected MOCRS-IoTMEC model is mainly concentrated on the identification of optimal routes in the IoT assisted MEC environment by the use of pigeon inspired optimization (PIO) algorithm. Also, the LEACH protocol is applied to initially cluster the IoT devices. The PIO algorithm is applied to determine the fitness function to choose optimal routes. To depict the enhanced performance of the MOCRS-IoTMEC model, a detailed comparison study is made. The experimental outcomes reported the enhanced execution of the MOCRS-IoTMEC method over other approaches.

VANET: Optimal Cluster Head Selection Using Opposition Based Learning

Traffic related accidents and route congestions remain to dwell significant issues in the globe. To overcome this, VANET was proposed to enhance the traffic management. However, there are several drawbacks in VANET such as collision of vehicles, data transmission in high probability of network fragmentation and data congestion. To overcome these issues, the Enhanced Pigeon Inspired Optimization (EPIO) and the Adaptive Neuro Fuzzy Inference System (ANFIS) based methods have been proposed. The Cluster Head (CH) has been selected optimally using the EPIO approach, and then the ANFIS has been used for updating and validating the CH and also for enhancing the data transmission procedures. The dijkstra’s algorithm has been used for identifying the shortest path for data transmission. The results showcases that the proposed technique has attained the maximum Packet Delivery Ratios (PDRs) as 73.23% at a sensor radius of 130 m and 70.42% at a velocity of 10 km/h. Moreover, the proposed method has outperformed the existing technique in terms of the CH formation delay, the end to end delay and the PDR.

Chaotic Pigeon Inspired Optimization Technique for Clustered Wireless Sensor Networks

Wireless Sensor Networks (WSN) interlink numerous Sensor Nodes (SN) to support Internet of Things (loT) services. But the data gathered from SNs can be divulged, tempered, and forged. Conventional WSN data processes manage the data in a centralized format at terminal gadgets. These devices are prone to attacks and the security of systems can get compromised. Blockchain is a distributed and decentralized technique that has the ability to handle security issues in WSN. The security issues include transactions that may be copied and spread across numerous nodes in a peer-peer network system. This breaches the mutual trust and allows data immutability which in turn permits the network to go on. At some instances, few nodes die or get compromised due to heavy power utilization. The current article develops an Energy Aware Chaotic Pigeon Inspired Optimization based Clustering scheme for Blockchain assisted WSN technique abbreviated as EACPIO-CB technique. The primary objective of the proposed EACPIO-CB model is to proficiently group the sensor nodes into clusters and exploit Blockchain (BC) for inter-cluster communication in the network. To select Cluster Heads (CHs) and organize the clusters, the presented EACPIO-CB model designs a fitness function that involves distinct input parameters. Further, BC technology enables the communication between one CH and the other and with the Base Station (BS) in the network. The authors conducted comprehensive set of simulations and the outcomes were investigated under different aspects. The simulation results confirmed the better performance of EACPIO-CB method over recent methodologies.

A multi strategy improved pigeon-inspired optimization algorithm

Pigeon-inspired optimization algorithm is easy to fall into local optimization and low convergence accuracy in solving nonlinear optimization problems. In this paper, an improved pigeon-inspired optimization algorithm called Gaussian mixture pigeon-inspired optimization algorithm (GPIO) is proposed. In GPIO, the cubic mapping of chaotic mapping method is used to initialize the pigeon population, which increases the diversity of the population. Gaussian mutation operator is introduced to change the shortage that pigeon swarm algorithm is easy to fall into local optimization, and improve the convergence efficiency of the algorithm. The experimental results of 19 benchmark functions show that the algorithm has better optimization ability than other swarm intelligence algorithms.

A continuous modeling method via improved pigeon-inspired optimization for wake vortices in UAVs close formation flight

This paper explores the modeling of wake vortices to estimate the wake vortex effect in unmanned aerial vehicles (UAVs) close formation flight. A continuous horseshoe vortex method is proposed based on the consideration of a compromise between estimation accuracy and computational efficiency. Using the proposed modeling method, the wake vortex model can easily adapt to UAVs with different wing shapes by adjusting model parameters. Furthermore, the interaction coefficient of filaments of the wake vortex is introduced to improve the estimation accuracy. However, the analytical value is solved difficultly by theoretical derivation. Therefore, the improved pigeon-inspired optimization is developed to search for the optimal value. On the other hand, the wake vortex effect is formulated by the statistical average strategy. The formulated wake vortex effect can be readily utilized to conduct a realistic dynamic model of the vortex-suffering UAV. Simulation results show that the estimation accuracy of the proposed modeling method increases by approximately 30% and 34%, compared with the single and multiple horseshoe vortex methods respectively. Furthermore, the computational efficiency is sufficient enough for the real-time estimation in UAVs close formation flight.

Novel PIO Algorithm with Multiple Selection Strategies for Many-Objective Optimization Problems

With the increase of problem dimensions, most solutions of existing many-objective optimization algorithms are non-dominant. Therefore, the selection of individuals and the retention of elite individuals are important. Existing algorithms cannot provide sufficient solution precision and guarantee the diversity and convergence of solution sets when solving practical many-objective industrial problems. Thus, this work proposes an improved many-objective pigeon-inspired optimization (ImMAPIO) algorithm with multiple selection strategies to solve many-objective optimization problems. Multiple selection strategies integrating hypervolume, knee point, and vector angles are utilized to increase selection pressure to the true Pareto Front. Thus, the accuracy, convergence, and diversity of solutions are improved. ImMAPIO is applied to the DTLZ and WFG test functions with four to fifteen objectives and compared against NSGA-III, GrEA, MOEA/D, RVEA, and many-objective Pigeon-inspired optimization algorithm. Experimental results indicate the superiority of ImMAPIO on these test functions.

Falcon Optimization Algorithm for Bayesian Networks Structure Learning

In machine-learning, one of the useful scientific models for producing the structure of knowledge is Bayesian network, which can draw probabilistic dependency relationships between variables. The score and search is a method used for learning the structure of a Bayesian network. The authors apply the Falcon Optimization Algorithm (FOA) as a new approach to learning the structure of Bayesian networks. This paper uses the Reversing, Deleting, Moving and Inserting operations to adopt the FOA for approaching the optimal solution of Bayesian network structure. Essentially, the falcon prey search strategy is used in the FOA algorithm. The result of the proposed technique is compared with Pigeon Inspired optimization, Greedy Search, and Simulated Annealing using the BDeu score function. The authors have also examined the performances of the confusion matrix of these techniques utilizing several benchmark data sets. As shown by the evaluations, the proposed method has more reliable performance than the other algorithms including producing better scores and accuracy values.

A bat-pigeon algorithm to crack detection-enabled autonomous vehicle navigation and mapping

• An image processing algorithm is developed to accurately locate the cracks with crack detection. • The proposed Bat-Pigeon algorithm effectively navigates the autonomous vehicle to decelerate around small crack areas while planning a collision-free path with the least travel time. • A local reactive navigator is developed that builds environmental maps locally while avoiding moving obstacles. • Simulation and comparative studies validate that the proposed algorithms effectively carry out the adjustable speed navigation and mapping of autonomous vehicles based on road conditions in various real-world scenarios. Autonomous vehicles have nowadays received widespread attention, and path planning is one of the most important components of its autonomous operation. However, due to the long-term use of roads and lack of maintenance, the roads that autonomous vehicles need to pass inevitably have cracks. In this case, while the autonomous vehicles pass through these cracked areas at high speed, it will increase the sense of bumps and even deviate from the originally planned route, which may potentially cause vehicle damage. Therefore, in this paper, we propose an adjustable speed navigation method in light of crack detection for autonomous vehicle path planning, which can automatically adjust the speed in the cracked areas of the road. Based on the obtained image of the road environment, an image processing algorithm to accurately locate the crack is developed. Then, in light of the obtained location of cracks and obstacles, a Bat-Pigeon algorithm (BPA) is proposed to conduct adjustable speed navigation of autonomous vehicles. Take advantage of the same individual speed update rule, we integrate the global search of the Pigeon-inspired optimization (PIO) algorithm and the local search by the Bat algorithm (BA), which can effectively improve the speed and performance of the convergence algorithm. The proposed Bat-Pigeon algorithm navigates the autonomous vehicle to decelerate around small crack areas while planning a collision-free path with the least travel time. In addition, a local reactive navigator is developed that builds environmental maps locally while avoiding dynamic and unknown obstacles. To verify the theoretical advantages of the developed algorithms, we perform comparative experiments under various scenarios. Simulation and comparative studies validate that the proposed algorithms effectively carry out the adjustable speed navigation and mapping of autonomous vehicles based on road conditions in various real-world scenarios.

An Improved Pigeon-Inspired Optimization for Multi-focus Noisy Image Fusion

An Improved Pigeon-Inspired Optimization for Multi-focus Noisy Image Fusion

Active disturbance rejection attitude control of unmanned quadrotor via paired coevolution pigeon-inspired optimization

PurposeThe purpose of this paper is to develop a novel active disturbance rejection attitude controller for quadrotors and propose a controller parameters identification approach to obtain better control results.Design/methodology/approachAiming at the problem that quadrotor is susceptible to disturbance in outdoor flight, the improved active disturbance rejection control (IADRC) is applied to design attitude controller. To overcome the difficulty that adjusting the parameters of IADRC controller manually is complex, paired coevolution pigeon-inspired optimization (PCPIO) algorithm is used to optimize the control parameters.FindingsThe IADRC, where nonlinear state error feedback control law is replaced by non-singular fast terminal sliding mode control law and a third-order tracking differentiator is designed for second derivative of the state, has higher control accuracy and better robustness than ADRC. The improved PIO algorithm based on evolutionary mechanism, named PCPIO, is proposed. The optimal parameters of ADRC controller are found by PCPIO with the optimization criterion of integral of time-weighted absolute value of the error. The effectiveness of the proposed method is verified by a series of simulation experiments.Practical implicationsIADRC can improve the accuracy of attitude control of quadrotor and resist external interference more effectively. The proposed PCPIO algorithm can be easily applied to practice and can help the design of the quadrotor control system.Originality/valueAn improved active disturbance rejection controller is designed for quadrotor attitude control, and a hybrid model of PIO and evolution mechanism is proposed for parameters identification of the controller.

Collaborative Target Search Algorithm for UAV Based on Chaotic Disturbance Pigeon-Inspired Optimization

Unmanned aerial vehicles (UAVs) have shown their superiority in military and civilian missions. In the face of complex tasks, many UAVs are usually needed to cooperate with each other. Therefore, multi-UAV cooperative target search has attracted more and more scholars’ attention. At present, there are many bionic algorithms for solving the cooperative search problem of multi-UAVs, including particle swarm optimization algorithm (PSO) and differential evolution (DE). Pigeon-inspired optimization (PIO) is a new swarm intelligence optimization algorithm proposed in recent years. It has great advantages over other algorithms in convergence, robustness, and accuracy, and has few parameters to be adjusted. Aiming at the shortcomings of the standard pigeon colony algorithm, such as poor population diversity, slow convergence speed, and the ease of falling into local optimum, we have proposed chaotic disturbance pigeon-inspired optimization (CDPIO) algorithm. The improved tent chaotic map was used to initialize the population and increase the diversity of the population. The disturbance factor is introduced in the iterative update stage of the algorithm to generate new individuals, replace the individuals with poor performance, and carry out disturbance to increase the optimization accuracy. Benchmark functions and UAV target search model were used to test the algorithm performance. The results show that the CDPIO had faster convergence speed, better optimization precision, better robustness, and better performance than PIO.

Detection of segmented uterine cancer images by Hotspot Detection method using deep learning models, Pigeon-Inspired Optimization, types-based dominant activation selection approaches

Uterine cancer consists of cells of a layer that forms the inside of the uterus. Sometimes, as a result of abnormal growth of normal cells, it can damage the surrounding tissues and cause the formation of cancerous cells. In the USA, according to the projections for 2021, approximately 66 thousand new cases of uterine cancer will be detected and approximately 13 thousand of these cancer patients are expected to die from uterine cancer. Early diagnosis of cancer is important. Recently, artificial intelligence-based technologies have been used in the diagnosis and treatment processes of various diseases. In this study, five categories of datasets including normal, abnormal, and benign cells were used. The dataset consists of cellular images and is publicly available. The proposed approach consists of three steps. In the first step, the Hotspot method was used to detect the tumor cells in the images. In the second step, tumor cells that were brought to the fore by segmentation were trained by deep learning models, and activation sets of five types from each deep learning model were created. In the last step, the best activation sets were selected among the activation sets obtained by deep learning models of each type (for five dataset types). Pigeon-Inspired Optimization was used for this selection. Thus, the activation sets with the best performance of the five types were classified by the Softmax method. The overall accuracy success achieved with the approach suggested as a result of the classification was 99.65%.

A multi-strategy pigeon-inspired optimization approach to active disturbance rejection control parameters tuning for vertical take-off and landing fixed-wing UAV

In this paper, Active Disturbance Rejection Control (ADRC) is utilized in the pitch control of a vertical take-off and landing fixed-wing Unmanned Aerial Vehicle (UAV) to address the problem of height fluctuation during the transition from hover to level flight. Considering the difficulty of parameter tuning of ADRC as well as the requirement of accuracy and rapidity of the controller, a Multi-Strategy Pigeon-Inspired Optimization (MSPIO) algorithm is employed. Particle Swarm Optimization (PSO), Genetic Algorithm (GA), the basic Pigeon-Inspired Optimization (PIO), and an improved PIO algorithm CMPIO are compared. In addition, the optimized ADRC control system is compared with the pure Proportional-Integral-Derivative (PID) control system and the non-optimized ADRC control system. The effectiveness of the designed control strategy for forward transition is verified and the faster convergence speed and better exploitation ability of the proposed MSPIO algorithm are confirmed by simulation results.