Wolf Pack Algorithm Research Articles

A hybrid algorithm on the vessel routing optimization for marine debris collection

More and more marine debris not only damages the environment, but also endangers human health and marine life. Since the location of marine debris is not fixed, we first apply the GNOME software to predict the trajectory of marine debris and determine its location. Then, the logistics network is used to optimize the collection of marine debris. A mixed integer linear programming model for debris vessel routing is proposed. The objective function is to minimize the total cost, which includes the fuel cost consisting of is a piecewise linear function, labor cost, and fixed cost consisting of rent and insurance relative to the vessels used, taking into account the vessel weight capacity, fuel tank capacity, and time windows constraint at each debris location. We propose a hybrid algorithm of adaptive large-scale neighborhood search, combined with a wolf pack algorithm. This paper takes Yangtze River Estuary to the East China Sea as an example to verify the proposed model and algorithm. The results show that, compared with the worst-case, the most appropriate collection time can save up to 6.38% of the cost, which is about $75,590 per calendar year. The sensitivity analysis of debris weight and vessel capacity is also performed.

Construction of a Supply Chain Financial Logistics Supervision System Based on Internet of Things Technology

In recent years, with the rapid development of the global economy and the development trend of more and more stable, well-developed network communications, online shopping has become an increasingly common way; as a result, the logistics industry has emerged from many industries and has become one of the most popular industries. However, due to the extensive involvement of the logistics industry, the overly complex technology, and the huge amount of data and information, the security of logistics has become one of the hot topics of special concern. Based on the background of an intelligent environment, this paper constructs a supply chain financial logistics supervision system based on Internet of Things technology. This article refers to the research experience of previous scholars, briefly introduces the theoretical knowledge of the Internet of Things technology, smart environment, and supply chain finance, and makes a certain analysis of the logistics supervision system. We collect and calculate logistics data through the wolf group hunting and siege formula in the wolf group algorithm and analyze the application performance of the logistics supervision system in reality. Then, we briefly designed the system architecture diagram of the logistics supervision system and compared the freight situation of the logistics supervision system before and after and statistics on the deployment of the logistics supervision system in customs, docks, airports, stations, and other places from 2015 to 2019. Finally, a comparative analysis of the performance of wolf pack algorithm and other algorithms was performed under different path planning. The final result shows that the logistics supervision system has important practical value in the logistics industry; in addition, the deployment of logistics supervision systems in customs, terminals, and other places has increased year by year from 2015 to 2019.

A novel topology optimization of coverage-oriented strategy for wireless sensor networks

Aiming at the key optimization problems of wireless sensor networks in complex industrial application environments, such as the optimum coverage and the reliability of the network, a novel topology optimization of coverage-oriented strategy for wireless sensor networks based on the wolf pack algorithm is proposed. Combining the characteristics of topology structure of wireless sensor networks and the optimization idea of the wolf pack algorithm redefines the group’s wandering and surprise behavior. A novel head wolf mutation strategy is proposed, which increases the neighborhood search range of the optimal solution, enhances the uniformity of wolf pack distribution and the ergodicity ability of the wolf pack search, and greatly improves the calculation speed and the accuracy of the wolf pack algorithm. With the same probability, the cluster heads are randomly selected periodically, and the overall energy consumption of wireless sensor networks is evenly distributed to the sensor node to realize the balanced distribution of the data of the member nodes in the cluster and complete the design of the topology optimization of wireless sensor networks. Through algorithm simulation and result analysis, compared with the particle swarm optimization algorithm and artificial fish swarm algorithm, the wolf swarm algorithm shows its advantages in terms of the residual energy of the sensor node, the average transmission delay, the average packet delivery rate, and the coverage of the network. Among them, compared with the particle swarm optimization algorithm and artificial fish swarm algorithm, the remaining energy of nodes has increased by 9.5% and 15.5% and the average coverage of the network has increased by 10.5% and 5.6%, respectively.

Weapon-target assignment in UAV cluster based on pheromone heuristic wolf pack algorithm

Weapon-target assignment in UAV cluster based on pheromone heuristic wolf pack algorithm

A Chaotic Disturbance Wolf Pack Algorithm for Solving Ultrahigh‐Dimensional Complex Functions

The optimization of high‐dimensional functions is an important problem in both science and engineering. Wolf pack algorithm is a technique often used for computing the global optimum of a multivariable function. In this paper, we develop a new wolf pack algorithm that can accurately compute the optimal value of a high‐dimensional function. First, chaotic opposite initialization is designed to improve the quality of initial solution. Second, the disturbance factor is added in the scouting process to enhance the searching ability of wolves, and an adaptive step length is designed to enhance the global searching ability to prevent wolves from falling into the local optimum effectively. A set of standard test functions are selected to test the performance of the proposed algorithm, and the test results are compared with other algorithms. The high‐dimensional and ultrahigh‐dimensional functions (500 and 1000) are tested. The experimental results show that the proposed algorithm features in good global convergence, high accuracy calculation, strong robustness, and excellent performance in high‐dimensional functions.

Parameter estimation of attributed scattering centers in SAR images based on wolf pack algorithm

Parameter estimation of attributed scattering centers in SAR images based on wolf pack algorithm

Fleet Maintenance Strategy Planning with Time Windows Integrated with Multi-Agent and Wolf Pack Reinforcement Learning

Selective maintenance is a widely used strategy to identify and perform the maintenance actions necessary for fleet mission success. Aiming at the problem of maintenance strategy planning with time windows (MSPTW) which is common in short-term operation plan, a fleet maintenance strategy planning approach based on multi-agent and reinforcement learning is studied in this paper. Based on the four kinds of foraging behaviors including migration, summon and attack in traditional Wolf Pack Algorithm (WPA), the intelligent behavior is redefined, and a new wolf pack algorithm for solving the MSPTW is designed. In order to seek the best path planning, a mathematical model with the aim of minimizing the total cost (fixed cost, transportation cost, waiting cost and penalty cost) is constructed utilizing the fitness and penalty function.

Self-adaptive wolf pack algorithm based on dynamic population updating for continuous optimisation problems

Self-adaptive wolf pack algorithm based on dynamic population updating for continuous optimisation problems

The Improved Least Square Support Vector Machine Based on Wolf Pack Algorithm and Data Inconsistency Rate for Cost Prediction of Substation Projects

Accurate and stable cost forecasting of substation projects is of great significance to ensure the economic construction and sustainable operation of power engineering projects. In this paper, a forecasting model based on the improved least squares support vector machine (ILSSVM) optimized by wolf pack algorithm (WPA) is proposed to improve the accuracy and stability of the cost forecasting of substation projects. Firstly, the optimal features are selected through the data inconsistency rate (DIR), which helps reduce redundant input vectors. Secondly, the wolf pack algorithm is used to optimize the parameters of the improved least square support vector machine. Lastly, the cost forecasting method of WPA-DIR-ILSSVM is established. In this paper, 88 substation projects in different regions from 2015 to 2017 are chosen to conduct the training tests to verify the validity of the model. The results indicate that the new hybrid WPA-DIR-ILSSVM model presents better accuracy, robustness, and generality in cost forecasting of substation projects.

Design of Highly Uniform Magnetic Field Coils With Wolf Pack Algorithm

Uniform magnetic fields are widely used in atomic magnetometers, MRI and other fields. The traditional method of solving closed-form equations is usually used to design highly uniform magnetic field coils. In this paper, a new intelligent algorithm, wolf pack algorithm (WPA), is proposed to design the coil structure of a highly uniform magnetic field. This method can replace the traditional method to obtain the coil parameters. We additionally use a discrete optimization method to solve the rounding error. According to the investigation, compared with particle swarm optimization (PSO) algorithm, WPA has stronger global optimization ability, faster convergence speed and multiple optimization strategies. We set appropriate coil constraints (structural and process constraints) and simplify the multi-dimensional solutions to one-dimensional solutions to reduce the difficulty of operation. The distribution of magnetic field intensity of three axes of coils is analyzed by the finite element analysis method, and the 1%, 0.1%, and 0.01% uniform regions are studied. Under the same conditions, the uniform magnetic field regions of the coils designed by the PSO and WPA, as well as a single pair of axial and radial coils are compared. The performance results of multiple pairs of coils are superior to those of a single pair of coils. In general, the uniformity of the axial and radial magnetic field coils designed by the WPA is better than that designed by the PSO. Finally, the experimental value of the magnetic field uniformity is nearly consistent with the theoretical value.

Task Assignment of UAV Swarm Based on Wolf Pack Algorithm

To perform air missions with an unmanned aerial vehicle (UAV) swarm is a significant trend in warfare. The task assignment among the UAV swarm is one of the key issues in such missions. This paper proposes PSO-GA-DWPA (discrete wolf pack algorithm with the principles of particle swarm optimization and genetic algorithm) to solve the task assignment of a UAV swarm with fast convergence speed. The PSO-GA-DWPA is confirmed with three different ground-attack scenarios by experiments. The comparative results show that the improved algorithm not only converges faster than the original WPA and PSO, but it also exhibits excellent search quality in high-dimensional space.

Image Segmentation Method with Maximum Entropy Optimized by Wolf Pack Algorithm

In order to improve the accuracy and efficiency of image segmentation, the wolf pack algorithm is introduced into the maximum entropy image segmentation method. The wolf pack algorithm is applied to the calculation of the maximum entropy threshold, and the image segmentation is realized by finding the optimal threshold. Simulation results show that this method can quickly and accurately find the segmentation threshold, obtain better segmentation accuracy, and improve the image segmentation effect to a certain extent.

Improved Wolf Pack Algorithm for Optimum Design of Truss Structures

In order to find a more effective method in structural optimization, an improved wolf pack optimization algorithm was proposed. In the traditional wolf pack algorithm, the problem of falling into local optimum and low precision often occurs. Therefore, the adaptive step size search and Levy's flight strategy theory were employed to overcome the premature flaw of the basic wolf pack algorithm. Firstly, the reasonable change of the adaptive step size improved the fineness of the search and effectively accelerated the convergence speed. Secondly, the search strategy of Levy's flight was adopted to expand the search scope and improved the global search ability of the algorithm. At last, to verify the performance of improved wolf pack algorithm, it was tested through simulation experiments and actual cases, and compared with other algorithms. Experiments show that the improved wolf pack algorithm has better global optimization ability. This study provides a more effective solution to structural optimization problems.

Economic Capacity Allocation of Grid-connected Microgrid Based on Improved Hybrid Genetic Algorithm

For the wind and solar diesel storage grid-connected microgrid, in order to meet the minimum investment cost of the microgrid at the initial stage and have certain economic benefits. The objective function of this paper is the annual average economic cost, the number of each micro-power source is used as the optimization variable, the constraints of micro-grid operation are considered, the capacity optimization configuration model of grid-connected micro-grid is established, and then the improved genetic algorithm can be used to solve this problem. Finally, the annual average economic cost, the utilization rate of new energy and the redundancy rate are used as evaluation indicators. By comparing the configuration results of genetic algorithm, wolf pack algorithm and mixed wolf pack genetic algorithm, the capacity allocation scheme of microgrid under economic indicators is obtained.

A Novel Fuzzy Time Series Forecasting Model Based on the Hybrid Wolf Pack Algorithm and Ordered Weighted Averaging Aggregation Operator

The fuzzy time series has received extensive attention since it was proposed and it has been widely used in various practical applications. This study proposes a new fuzzy time series forecasting model which considers a hybrid wolf pack algorithm (HWPA) and an ordered weighted averaging (OWA) aggregation operator for fuzzy time series. The HWPA is adopted to obtain a suitable partition of the universe of discourse to promote the forecasting performance. Furthermore, the improved OWA aggregation method is applied to make the aggregation of historical information more practical. To overcome the deficiency of slow convergence speed and easy to entrap into the local extremum of the wolf pack algorithm (WPA), the chemotactic behavior and elimination–dispersal behavior of bacterial foraging optimization (BFO) are employed to optimize the scouting behavior of WPA. The actual enrollments data of the University of Alabama and Taiwan Futures Exchange (TAIFEX) are utilized as the benchmark data and the computational results of both training and testing phases all indicate that the new forecasting model outperforms other existing models. The robustness of the proposed model is also tested and the robust results can be obtained when the historical data are inaccurate.

Self-adaptive Wolf Pack Algorithm based on Dynamic Population Updating for Continuous Optimization Problems

Wolf pack algorithm (WPA) is a relatively new swarm intelligence-based algorithm for solving complex continuous optimisation problems as well as real-world optimisation problems. The basic WPA and ...

Hybrid Wolf Pack and Particle Swarm Optimization Algorithm for Multihop Routing Protocol in WSN

Hybrid Wolf Pack and Particle Swarm Optimization Algorithm for Multihop Routing Protocol in WSN

Uncertain bilevel knapsack problem based on an improved binary wolf pack algorithm

To address indeterminism in the bilevel knapsack problem, an uncertain bilevel knapsack problem (UBKP) model is proposed. Then, an uncertain solution for UBKP is proposed by defining the $${\mathcal{P}_E}$$ Nash equilibrium and $${\mathcal{P}_E}$$ Stackelberg-Nash equilibrium. To improve the computational efficiency of the uncertain solution, an evolutionary algorithm, the improved binary wolf pack algorithm, is constructed with one rule (wolf leader regulation), two operators (invert operator and move operator), and three intelligent behaviors (scouting behavior, intelligent hunting behavior, and upgrading). The UBKP model and the $${\mathcal{P}_E}$$ uncertain solution are applied to an armament transportation problem as a case study.

An Adaptive Shrinking Grid Search Chaotic Wolf Optimization Algorithm Using Standard Deviation Updating Amount.

To improve the optimization quality, stability, and speed of convergence of wolf pack algorithm, an adaptive shrinking grid search chaotic wolf optimization algorithm using standard deviation updating amount (ASGS-CWOA) was proposed. First of all, a strategy of adaptive shrinking grid search (ASGS) was designed for wolf pack algorithm to enhance its searching capability through which all wolves in the pack are allowed to compete as the leader wolf in order to improve the probability of finding the global optimization. Furthermore, opposite-middle raid method (OMR) is used in the wolf pack algorithm to accelerate its convergence rate. Finally, “Standard Deviation Updating Amount” (SDUA) is adopted for the process of population regeneration, aimed at enhancing biodiversity of the population. The experimental results indicate that compared with traditional genetic algorithm (GA), particle swarm optimization (PSO), leading wolf pack algorithm (LWPS), and chaos wolf optimization algorithm (CWOA), ASGS-CWOA has a faster convergence speed, better global search accuracy, and high robustness under the same conditions.

Hybrid integer-coded Wolf Pack Algorithm for multiple-type flatcars loading problem

This paper presents an efficient hybrid integer-coded Wolf Pack Algorithm (HICWPA) for multiple-type flatcars loading problem. The multiple-type flatcars loading problem aims at loading a given set of multi-size vehicles on the multiple-type railway flatcars so that the length utilization ratio of flatcars is maximized. The algorithm introduced in this paper is based on three important operations. First, an integer coding method of individual sequence is designed to express the usage order of flatcars. The integer-coded Wolf Pack Algorithm (ICWPA) is responsible for searching the optimal individual coding sequence wildly to improve the probability of achieving the approximated optimal solution. Second, the Best Fit Decreasing (BFD) algorithm is used to decode the individual coding sequences and load the vehicles on flatcars to generate initial loading plan. Third, a moving operator is designed to repair the infeasible loading sequences by reassigning the individual coding bits which can improve the convergence speed of the proposed algorithm. The effectiveness and advancement of HICWPA are verified by a practical instance of multiple-type flatcars loading problem. The computational results indicate that the flatcars length utilization ratio of our algorithm outperforms current excellent algorithms for the considered problem.