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

PurposeThe purpose of this paper is to propose an improved optimization method for image matching problem, which is based on multi-scale Gaussian mutation pigeon-inspired optimization (MGMPIO) algorithm, with the objective of accomplishing the complicated image matching quickly.Design/methodology/approachThe hybrid model of multi-scale Gaussian mutation (MGM) mechanism and pigeon-inspired optimization (PIO) algorithm is established for image matching problem. The MGM mechanism is a nonlinear model, which can adjust the position of pigeons by mutation operation. In addition, the variable parameter (VP) mechanism is exploited to adjust the map and compass factor of the original PIO. Low-cost quadrotor, a type of electric multiple rotorcraft, is used as a carrier of binocular camera to obtain the images.FindingsThis work improved the PIO algorithm by modifying the search strategy and adding some limits, so that it can have better performance when applied to the image matching problem. Experimental results show that the proposed method demonstrates satisfying performance in convergence speed, robustness and stability.Practical implicationsThe proposed MGMPIO algorithm can be easily applied to solve practical problems and accelerate convergence speed of the original PIO, and thus enhancing the speed of matching process, which will considerably increase the effectiveness of algorithm.Originality/valueA hybrid model of the MGM mechanism and PIO algorithm is proposed for image matching problem. The VP mechanism and low-cost quadrotor is also utilized in image matching problem.

Discrete pigeon-inspired optimization algorithm with Metropolis acceptance criterion for large-scale traveling salesman problem

Data collection is the basic purpose of deploying in heterogeneous WSNs for Internet of things, and the problem of data collection is the key problem that needs to be solved in heterogeneous WSNs. How to collect energy-efficient and reliable data is one of the key technologies of heterogeneous WSNs. Collecting the sensor node data by mobile sink is an effective measure to solve data collection efficiency. To this end, a data collection strategy of mobile sink for heterogeneous WSNs based on pigeon-inspired optimization by PSO algorithm is proposed. The proposed algorithm uses the improved pigeon-inspired optimization by particle swarm optimization algorithm to select the best dwell point and then regards the construction of the moving path based on the dwell point as a traveling salesman problem to optimize the moving path and solve the optimal moving path. The experimental analysis and simulation results show that, compared with other algorithms, the algorithm proposed in this paper can effectively prolong the lifetime of the network and reduce the delay of data collection, increasing the amount of data collection.

Trajectory Design of Re-entry Vehicles using combined Pigeon Inspired Optimization and Orthogonal Collocation method

There are numerous approaches being used to reduce power losses in the distribution system. In this work, the concept of network reconfiguration is used in an effort to provide new algorithms to decrease distribution system losses. The network reconfiguration issue must be solved with effective distribution load flow (DLF). Two key components are required for the Optimal Network Reconfiguration (ONR) strategy to reduce losses: first, promising radiality for the reconfigured network, and second, providing optimal losses for the final reconfigured network. The complex computational problem of network reconfiguration is addressed using five meta-heuristic techniques: Genetic Algorithm (GA), Shuffled Frog Leap Algorithm (SFLA), Particle Swarm Optimization (PSO), Pigeon Inspired Optimization (PIO), and Jaya Optimization algorithm. These techniques take into account equality and inequality constraints to find the best network reconfiguration with the minimum power losses in the system. The IEEE 33 bus distribution system is used as a test case, and the test system is subjected to network reconfiguration. Results of different algorithms were included. Over the other algorithms, the Pigeon Inspired Optimization (PIO) algorithm outperforms them.

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.

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, a hybrid model of Entropy Weight Method (EWM) and Gaussian Pigeon-Inspired Optimization (GPIO) is proposed to solve the problem of rectangular waveguide design. There are four parameters involved in the design: length, width, relative permittivity and electrical conductivity. Our goal is to optimize the combination of these parameters so that the waveguide achieves the best overall performance. EWM is employed in the building of the performance judging function. An improved Pigeon-Inspired Optimization adopting Gaussian searching strategy is utilized for minimizing the function's value. Comparative experiments with basic PIO and Particle Swarm Optimization (PSO) are conducted, and the results verify that better design can be obtained with our proposed Gaussian Entropy Weight Pigeon-Inspired Optimization (GEWPIO) algorithm because of its utilization of information theory and non-linear searching strategy.

The pigeon-inspired optimization algorithm is a category of a newly proposed swarm intelligence-based algorithm that belongs to the population-based solution technique. The MKP is a class of complex optimization problems that have many practical applications in the fields of engineering and sciences. Due to the practical applications of MKP, numerous algorithmic-based methods like local search and population-based search algorithms have been proposed to solve the MKP in the past few decades. This paper proposes a modified binary pigeon-inspired optimization algorithm named (Modified-BPIO) for the 0 - 1 multidimensional knapsack problem (MKP). The utilization of the binary pigeon-inspired optimization (BPIO) for solving the multidimensional knapsack problem came with huge success. However, it can be observed that the BPIO converges prematurely due to lost diversity during the search activities. Given the above, the crossover operator is integrated with the landmark component of the BPIO to improve the diversity of the solution space. The MKP benchmarks from the Operations Research (OR) library are utilized to test the performance of the proposed binary method. Experimentally, it is concluded that the proposed Modified-BPIO has a better performance when compared with the BPIO and existing state-of-the-arts that worked on the same MKP benchmarks.

In this paper, an improved air-to-air multiple Uninhabited Combat Aerial Vehicles (UCAVs) target assignment model is established and an integrated advantage function with three affecting factors, which are angle, velocity and combat capability, is proposed. Then, Bloch Quantum-Behaved Pigeon-Inspired Optimization (BQPIO) algorithm is designed and applied to optimize the integrated advantage function and solve the multiple UCAVs target assignment problem, which combines the quantum chromosome mechanism with the basic Pigeon-Inspired Optimization (PIO). The detailed procedure is also given. Comparative results with basic PIO and Particle Swarm Optimization (PSO) verified the feasibility and effectiveness of our proposed approach.

This study integrates supply-side structural reform and rural agricultural issues, utilizing Mixed-Integer Linear Programming (MILP) to establish an optimization model for crop selection, land type allocation, and sowing time optimization. To address the multi-constraint optimization problem, a genetic algorithm with elite retention is employed for global search, iteratively optimizing the planting strategy through crossover and mutation operators to ensure solution diversity and optimality. Additionally, considering fluctuations in crop sales, yield per acre, planting costs, and market prices, as well as potential planting risks, the study introduces range-based indicators to measure variability and incorporates soft constraints on overproduction to enhance model robustness. Furthermore, to solve the multi-objective optimization problem, a three-objective programming model is constructed, with a Pareto ranking method used to design the fitness function. The pigeon-inspired optimization (PIO) algorithm is then applied for global search to determine the optimal planting strategy for 2024–2030. This approach not only improves computational efficiency but also enhances the adaptability and stability of the optimization strategy.

For aerial manipulation, there are many challenging problems such as coupled force control and compensation of uncertain external disturbance. In this study, the backstepping controller utilizing a full coupled dynamic model is developed to control the Unmanned Aerial Vehicle (UAV) with a manipulator. In addition, the entire system is required to track the desired trajectory when the end-effector of the manipulator is disturbed by a lightweight object. Considering the precision requirement, the control problem is transformed into a controller parameter tuning problem and as well as makes full use of the heuristic optimization algorithm to search for a set of suitable parameters. Furthermore, in view of the difficulty of parameter tuning of the backstepping controller, a Gaussian Adaptive Mutation Pigeon-Inspired Optimization (GAMPIO) algorithm is proposed. Finally, a series of simulations, compared with other optimization algorithms, are conducted to demonstrate the GAMPIO’s superiority. The results verify that GAMPIO has faster convergence and better exploration capability to make end-effector performances better in trajectory tracking.KeywordsPigeon-Inspired Optimization (PIO)Aerial manipulationBackstepping controller tuningGaussian distributionStagnation mutation

The fuzzy production scheduling problem considering mould maintenance (FPSP-MM) is studied. The processing time and the maintenance time are represented by triangular fuzzy numbers. When tasks are executed based on the sequence provided by the fuzzy schedule, the real duration of each task needs to be known so the posteriori solution with deterministic processing times can be obtained. Therefore, the concept of the schedule robustness needs to be considered for the fuzzy problem. The robustness is considered as the optimization objective except for the fuzzy makespan in this research. To optimize these two objective functions, a multi-objective pigeon inspired optimization (MOPIO) algorithm is developed. To extend the pigeon inspired optimization (PIO) algorithm from the single-objective case to the multi-objective case, non-dominated solutions are used as candidates for the leader pigeon designation and a special crowding distance is used to ensure a good distribution of solutions in both the objective space and the corresponding decision space. Furthermore, an index-based ring topology is used to manage the convergence speed. Numerical experiments on a variety of simulated scenarios show the excellent efficiency and effectiveness of the proposed MOPIO algorithm by comparing it with other algorithms.

To establish a near-zero magnetic environment for accurate magnetocardiography (MCG) measurements, a magnetically shielded cylinder (MSC) is commonly used in combination with active magnetic compensation coils. However, the coupling effect between the field from internal coils and the magnetic shielding layer can distort the distribution of ideal magnetic field and degrade the quality of cardiac magnetic signals. To address this issue, this article proposes a design approach for self-shielding coils featuring curved structures that integrate the target-field method with the pigeon-inspired optimization algorithm. The results indicate that the optimized coils maintain nonuniformity error below 1% in the internal region with a side length of 0.2 m and attain an attenuation error below 5% in the external area. This means that the coils are not easily affected by ferromagnetic materials and are suitable for MSCs of different sizes. An optically pumped magnetometer-based magnetocardiography (OPM-MCG) system, using two sets of single-axis curved coils, is used to compensate for the background field interference, resulting in an improved signal-to-noise ratio (SNR) for cardiac magnetic signals. Within the frequency range of 1–40 Hz, the average sensitivity of the OPM-MCG compensation system improves from 810.59 to 97.63 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{fT/Hz}^{1/2}$ </tex-math></inline-formula>, and it achieves 9.19 dB of noise suppression. Based on the enhanced MCG system, the collected MCG signals reveal significant SNR gains of 6.44 and 5.89 dB in the R-wave and T-wave segments, which lays a foundation for further exploration of cardiac electrophysiological information and assisted cardiac treatment.

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