scholarly journals Task allocation and route planning of multiple UAVs in a marine environment based on an improved particle swarm optimization algorithm

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Ming Yan ◽  
Huimin Yuan ◽  
Jie Xu ◽  
Ying Yu ◽  
Libiao Jin
Keyword(s):  
Particle Swarm Optimization ◽  
Marine Environment ◽  
Task Allocation ◽  
Particle Swarm ◽  
Route Planning ◽  
Pso Algorithm ◽  
Offshore Platforms ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Multiple Uavs

AbstractUnmanned aerial vehicles (UAVs) are considered a promising example of an automatic emergency task in a dynamic marine environment. However, the maritime communication performance between UAVs and offshore platforms has become a severe challenge. Due to the complex marine environment, the task allocation and route planning efficiency of multiple UAVs in an intelligent ocean are not satisfactory. To address these challenges, this paper proposes an intelligent marine task allocation and route planning scheme for multiple UAVs based on improved particle swarm optimization combined with a genetic algorithm (GA-PSO). Based on the simulation of an intelligent marine control system, the traditional particle swarm optimization (PSO) algorithm is improved by introducing partial matching crossover and secondary transposition mutation. The improved GA-PSO is used to solve the random task allocation problem of multiple UAVs and the two-dimensional route planning of a single UAV. The simulation results show that compared with the traditional scheme, the proposed scheme can significantly improve the task allocation efficiency, and the navigation path planned by the proposed scheme is also optimal.

scholarly journals Particle swarm optimization algorithm for the optimization of rescue task allocation with uncertain time constraints

2021 ◽  
Author(s):  
Na Geng ◽  
Zhiting Chen ◽  
Quang A. Nguyen ◽  
Dunwei Gong
Keyword(s):  
Particle Swarm Optimization ◽  
Task Allocation ◽  
Optimal Algorithm ◽  
Particle Swarm ◽  
Evolutionary Process ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Fitness Evaluation ◽  
Modified Particle Swarm Optimization ◽  
Uncertain Time

AbstractThis paper focuses on the problem of robot rescue task allocation, in which multiple robots and a global optimal algorithm are employed to plan the rescue task allocation. Accordingly, a modified particle swarm optimization (PSO) algorithm, referred to as task allocation PSO (TAPSO), is proposed. Candidate assignment solutions are represented as particles and evolved using an evolutionary process. The proposed TAPSO method is characterized by a flexible assignment decoding scheme to avoid the generation of unfeasible assignments. The maximum number of successful tasks (survivors) is considered as the fitness evaluation criterion under a scenario where the survivors’ survival time is uncertain. To improve the solution, a global best solution update strategy, which updates the global best solution depends on different phases so as to balance the exploration and exploitation, is proposed. TAPSO is tested on different scenarios and compared with other counterpart algorithms to verify its efficiency.

An Improved Particle Swarm Optimization Algorithm

2013 ◽  
Vol 394 ◽  
pp. 505-508 ◽  
Author(s):  
Guan Yu Zhang ◽  
Xiao Ming Wang ◽  
Rui Guo ◽  
Guo Qiang Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Genetic Mutation ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Optimum Path ◽  
Improved Pso

This paper presents an improved particle swarm optimization (PSO) algorithm based on genetic algorithm (GA) and Tabu algorithm. The improved PSO algorithm adds the characteristics of genetic, mutation, and tabu search into the standard PSO to help it overcome the weaknesses of falling into the local optimum and avoids the repeat of the optimum path. By contrasting the improved and standard PSO algorithms through testing classic functions, the improved PSO is found to have better global search characteristics.

CDMA Multiuser Detection Based on Improved Particle Swarm Optimization Algorithm

2011 ◽  
Vol 50-51 ◽  
pp. 3-7 ◽  
Author(s):  
Nan Ping Liu ◽  
Fei Zheng ◽  
Ke Wen Xia
Keyword(s):  
Particle Swarm Optimization ◽  
Multiuser Detection ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Discrete Pso ◽  
Case Simulation ◽  
Multi Access

CDMA multiuser detection (MUD) is a crucial technique to mobile communication. We adopt improved particle swarm optimization (PSO) algorithm in MUD which incorporates factor and utilizes function to discrete PSO. Comparison of BER and near-far effect has verified its effectiveness on multi-access interference (MAI). The algorithm accelerates the convergent speed meanwhile it also displays feasibility and superiority in case simulation.

scholarly journals An Improved Particle Swarm Optimization Algorithm Based on Centroid and Exponential Inertia Weight

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Shouwen Chen ◽  
Zhuoming Xu ◽  
Yan Tang ◽  
Shun Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Local Optima ◽  
Improved Particle Swarm Optimization ◽  
Inertia Weight ◽  
Ipso Algorithm

Particle swarm optimization algorithm (PSO) is a global stochastic tool, which has ability to search the global optima. However, PSO algorithm is easily trapped into local optima with low accuracy in convergence. In this paper, in order to overcome the shortcoming of PSO algorithm, an improved particle swarm optimization algorithm (IPSO), based on two forms of exponential inertia weight and two types of centroids, is proposed. By means of comparing the optimization ability of IPSO algorithm with BPSO, EPSO, CPSO, and ACL-PSO algorithms, experimental results show that the proposed IPSO algorithm is more efficient; it also outperforms other four baseline PSO algorithms in accuracy.

scholarly journals Multicriteria Ship Route Planning Method Based on Improved Particle Swarm Optimization–Genetic Algorithm

2021 ◽  
Vol 9 (4) ◽  
pp. 357
Author(s):  
Wei Zhao ◽  
Yan Wang ◽  
Zhanshuo Zhang ◽  
Hongbo Wang
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Fuel Consumption ◽  
Particle Swarm ◽  
Route Planning ◽  
Planning Method ◽  
Pareto Optimal ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Ship Route

With the continuous prosperity and development of the shipping industry, it is necessary and meaningful to plan a safe, green, and efficient route for ships sailing far away. In this study, a hybrid multicriteria ship route planning method based on improved particle swarm optimization–genetic algorithm is presented, which aims to optimize the meteorological risk, fuel consumption, and navigation time associated with a ship. The proposed algorithm not only has the fast convergence of the particle swarm algorithm but also improves the diversity of solutions by applying the crossover operation, selection operation, and multigroup elite selection operation of the genetic algorithm and improving the Pareto optimal frontier distribution. Based on the Pareto optimal solution set obtained by the algorithm, the minimum-navigation-time route, the minimum-fuel-consumption route, the minimum-navigation-risk route, and the recommended route can be obtained. Herein, a simulation experiment is conducted with respect to a container ship, and the optimization route is compared and analyzed. Experimental results show that the proposed algorithm can plan a series of feasible ship routes to ensure safety, greenness, and economy and that it provides route selection references for captains and shipping companies.

scholarly journals An Airborne Multi-Sensor Task Allocation Method Based on Improved Particle Swarm Optimization Algorithm

2018 ◽  
Vol 36 (4) ◽  
pp. 722-727
Author(s):  
Guoqing Shi ◽  
Fan Wu ◽  
Lin Zhang ◽  
Shuyang Zhang ◽  
Cao Guo
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Task Allocation ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Allocation Model ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Allocation Method

The characteristics of airborne multi-sensor task allocation problem are analyzed, and an airborne multi-sensor task allocation model is established. In order to solve the problems of local convergence and slow convergence of the traditional Particle Swarm Optimization (PSO) algorithm, the structure and parameters of the existing Particle Swarm Optimization algorithm are adjusted, and the direction coefficient and far away factor are introduced to control the velocity and direction of the particle far away from the worst solution, so that the particle moves away from the worst solution while moving to the optimal solution. Based on the improved Particle Swarm Optimization algorithm, an airborne multi-sensor task allocation method is proposed using maximum detection probability as objective function, and the algorithm is simulated. The simulation results show that this algorithm can effectively allocate tasks and improve allocation effects.

Route planning algorithm for autonomous underwater vehicles based on the hybrid of particle swarm optimization algorithm and radial basis function

2018 ◽  
Vol 41 (4) ◽  
pp. 942-953 ◽  
Author(s):  
Weidong Zhou ◽  
Zejing Xing ◽  
Bai Wenbin ◽  
Deng Chengchen ◽  
Yaen Xie ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Radial Basis Function ◽  
Basis Function ◽  
Autonomous Underwater Vehicles ◽  
Particle Swarm ◽  
Route Planning ◽  
Pso Algorithm ◽  
Underwater Vehicles ◽  
Local Optimum ◽  
Swarm Optimization

The mission route plays an essential role for the mission security and reliability of an unmanned system. This paper gives a route planning method for autonomous underwater vehicles (AUVs) based on the hybrid of particle swarm optimization (PSO) algorithm and radial basis function (RBF). In the improved PSO algorithm, metropolis criterion is used to prevent the improved PSO algorithm from falling into local optimum and RBF is used to smooth the path planned by PSO algorithm. Compared with classic PSO algorithm, the hybrid algorithm of PSO and RBF can avoid falling into the local optimum effectively and plan an anti-collision route. Moreover, based on the simulation results, it can be seen that the approach presented here is more efficient in convergence performance, and the planned route requires lower performance of AUVs.

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