scholarly journals A Bat Algorithm with Mutation for UCAV Path Planning

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Gaige Wang ◽  
Lihong Guo ◽  
Hong Duan ◽  
Luo Liu ◽  
Heqi Wang
Keyword(s):  
Path Planning ◽  
Optimization Problem ◽  
Bat Algorithm ◽  
Optimization Methods ◽  
Population Based ◽  
Planning Problem ◽  
New Approach ◽  
Air Vehicle ◽  
Flight Route ◽  
Path Planning Problem

Path planning for uninhabited combat air vehicle (UCAV) is a complicated high dimension optimization problem, which mainly centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. Original bat algorithm (BA) is used to solve the UCAV path planning problem. Furthermore, a new bat algorithm with mutation (BAM) is proposed to solve the UCAV path planning problem, and a modification is applied to mutate between bats during the process of the new solutions updating. Then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic BA. The realization procedure for original BA and this improved metaheuristic approach BAM is also presented. To prove the performance of this proposed metaheuristic method, BAM is compared with BA and other population-based optimization methods, such as ACO, BBO, DE, ES, GA, PBIL, PSO, and SGA. The experiment shows that the proposed approach is more effective and feasible in UCAV path planning than the other models.

scholarly journals A Hybrid Metaheuristic DE/CS Algorithm for UCAV Three-Dimension Path Planning

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Gaige Wang ◽  
Lihong Guo ◽  
Hong Duan ◽  
Heqi Wang ◽  
Luo Liu ◽  
...  
Keyword(s):  
Path Planning ◽  
Optimization Problem ◽  
Cuckoo Search ◽  
Planning Problem ◽  
Three Dimension ◽  
New Approach ◽  
Hybrid Metaheuristic ◽  
Air Vehicle ◽  
Flight Route ◽  
Path Planning Problem

Three-dimension path planning for uninhabited combat air vehicle (UCAV) is a complicated high-dimension optimization problem, which primarily centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. A new hybrid metaheuristic differential evolution (DE) and cuckoo search (CS) algorithm is proposed to solve the UCAV three-dimension path planning problem. DE is applied to optimize the process of selecting cuckoos of the improved CS model during the process of cuckoo updating in nest. The cuckoos can act as an agent in searching the optimal UCAV path. And then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic CS. The realization procedure for this hybrid metaheuristic approach DE/CS is also presented. In order to make the optimized UCAV path more feasible, the B-Spline curve is adopted for smoothing the path. To prove the performance of this proposed hybrid metaheuristic method, it is compared with basic CS algorithm. The experiment shows that the proposed approach is more effective and feasible in UCAV three-dimension path planning than the basic CS model.

Three Dimensional Robot Path Planning With Workspace Considerations

1992 ◽  
Author(s):  
C. Y. Liu ◽  
R. W. Mayne
Keyword(s):  
Path Planning ◽  
Three Dimensional ◽  
Optimization Methods ◽  
Planning Problem ◽  
Robot Path Planning ◽  
Recursive Quadratic Programming ◽  
Six Degree Of Freedom ◽  
Nonlinear Programming Method ◽  
Path Planning Problem ◽  
Robot Path

Abstract This paper considers the problem of robot path planning by optimization methods. It focuses on the use of recursive quadratic programming (RQP) for the optimization process and presents a formulation of the three dimensional path planning problem developed for compatibility with the RQP selling. An approach 10 distance-to-contact and interference calculations appropriate for RQP is described as well as a strategy for gradient computations which are critical to applying any efficient nonlinear programming method. Symbolic computation has been used for general six degree-of-freedom transformations of the robot links and to provide analytical derivative expressions. Example problems in path planning are presented for a simple 3-D robot. One example includes adjustments in geometry and introduces the concept of integrating 3-D path planning with geometric design.

A Glowworm Swarm Optimization Algorithm for Uninhabited Combat Air Vehicle Path Planning

2015 ◽  
Vol 24 (1) ◽  
pp. 69-83 ◽  
Author(s):  
Zhonghua Tang ◽  
Yongquan Zhou
Keyword(s):  
Path Planning ◽  
Heuristic Algorithms ◽  
Optimization Methods ◽  
Pso Algorithm ◽  
Population Based ◽  
Computational Accuracy ◽  
Swarm Optimization ◽  
Glowworm Swarm Optimization ◽  
Air Vehicle ◽  
The Individual

AbstractUninhabited combat air vehicle (UCAV) path planning is a complicated, high-dimension optimization problem. To solve this problem, we present in this article an improved glowworm swarm optimization (GSO) algorithm based on the particle swarm optimization (PSO) algorithm, which we call the PGSO algorithm. In PGSO, the mechanism of a glowworm individual was modified via the individual generation mechanism of PSO. Meanwhile, to improve the presented algorithm’s convergence rate and computational accuracy, we reference the idea of parallel hybrid mutation and local search near the global optimal location. To prove the performance of the proposed algorithm, PGSO was compared with 10 other population-based optimization methods. The experiment results show that the proposed approach is more effective in UCAV path planning than most of the other meta-heuristic algorithms.

Multi-Layer Distributed Constraint Satisfaction for Multi-criteria Optimization Problem

2020 ◽  
Vol 11 (2) ◽  
pp. 134-155 ◽  
Author(s):  
Mouna Gargouri Mnif ◽  
Sadok Bouamama
Keyword(s):  
Constraint Satisfaction ◽  
Optimization Problem ◽  
Transportation Network ◽  
Optimization Methods ◽  
Planning Problem ◽  
Scheduling Problems ◽  
Present Contribution ◽  
Multimodal Transportation ◽  
New Approach ◽  
Distributed Constraint Satisfaction

This article introduces a new approach to solve the multimodal transportation network planning problem (MTNP). In this problem, the commodities must be transported from an international network by at least two different transport modes. The main purpose is to identify the best multimodal transportation strategy. The present contribution focuses on efficient optimization methods to solve MTNP. This includes the assignment and the scheduling problems. The authors split the MTNP into layered. Each layer is presented by an agent. These agents interact, collaborate, and communicate together to solve the problem. This article defines MTNP as a distributed constraint satisfaction multi-criteria optimization problem (DCSMOP). This latter is a description of the constraint optimization problem (COP), where variables and constraints are distributed among a set of agents. Each agent can interact with other agents to share constraints and to distribute complementary tasks. Experimental results are the proof of this work efficiently.

An Improved Flower Pollination Algorithm for Optimal Unmanned Undersea Vehicle Path Planning Problem

2016 ◽  
Vol 30 (04) ◽  
pp. 1659010 ◽  
Author(s):  
Yongquan Zhou ◽  
Rui Wang
Keyword(s):  
Path Planning ◽  
Three Dimensional ◽  
Optimization Method ◽  
Population Based ◽  
Flower Pollination Algorithm ◽  
Planning Problem ◽  
Flower Pollination ◽  
3D Space ◽  
Undersea Vehicle ◽  
Path Planning Problem

Path planning of Unmanned Undersea Vehicle (UUV) is a rather complicated global optimum problem which is about seeking a superior sailing route considering the different kinds of constrains under complex combat field environment. Flower pollination algorithm (FPA) is a new optimization method motivated by flower pollination behavior. In this paper, a variant of FPA is proposed to solve the UUV path planning problem in two-dimensional (2D) and three-dimensional (3D) space. Optimization strategies of particle swarm optimization are applied to the local search process of IFPA to enhance its search ability. In the progress of iteration of this improved algorithm, a dimension by dimension based update and evaluation strategy on solutions is used. This new approach can accelerate the global convergence speed while preserving the strong robustness of standard FPA. The realization procedure for this improved flower pollination algorithm is also presented. To prove the performance of this proposed method, it is compared with nine population-based algorithms. The experiment result shows that the proposed approach is more effective and feasible in UUV path planning in 2D and 3D space.

Image Matching Using a Bat Algorithm with Mutation

2012 ◽  
Vol 203 ◽  
pp. 88-93 ◽  
Author(s):  
Jia Wei Zhang ◽  
Gai Ge Wang
Keyword(s):  
Image Matching ◽  
Heuristic Method ◽  
Bat Algorithm ◽  
Optimization Methods ◽  
Population Based ◽  
The Other ◽  
Matching Problem ◽  
New Approach ◽  
Strong Robustness ◽  
Traditional Image

Due to shortcoming of traditional image matching for computing the fitness for every pixel in the searching space, a new bat algorithm with mutation (BAM) is proposed to solve image matching problem, and a modification is applied to mutate between bats during the process of the new solutions updating. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic BA. The realization procedure for this improved meta-heuristic approach BAM is also presented. To prove the performance of this proposed meta-heuristic method, BAM is compared with BA and other population-based optimization methods, DE and SGA. The experiment shows that the proposed approach is more effective and feasible in image matching than the other model.

scholarly journals A Comparative Study of Swarm Intelligence Algorithms for UCAV Path-Planning Problems

Mathematics ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 171
Author(s):  
Haoran Zhu ◽  
Yunhe Wang ◽  
Zhiqiang Ma ◽  
Xiangtao Li
Keyword(s):  
Path Planning ◽  
Swarm Intelligence ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Planning Problem ◽  
Complex Optimization ◽  
Battlefield Environment ◽  
Planning Problems ◽  
Path Planning Problem ◽  
Open Issues

Path-planning for uninhabited combat air vehicles (UCAV) is a typically complicated global optimization problem. It seeks a superior flight path in a complex battlefield environment, taking into various constraints. Many swarm intelligence (SI) algorithms have recently gained remarkable attention due to their capability to address complex optimization problems. However, different SI algorithms present various performances for UCAV path-planning since each algorithm has its own strengths and weaknesses. Therefore, this study provides an overview of different SI algorithms for UCAV path-planning research. In the experiment, twelve algorithms that published in major journals and conference proceedings are surveyed and then applied to UCAV path-planning. Moreover, to demonstrate the performance of different algorithms in further, we design different scales of problem cases for those comparative algorithms. The experimental results show that UCAV can find the safe path to avoid the threats efficiently based on most SI algorithms. In particular, the Spider Monkey Optimization is more effective and robust than other algorithms in handling the UCAV path-planning problem. The analysis from different perspectives contributes to highlight trends and open issues in the field of UCAVs.

Sign in / Sign up

Export Citation Format

Share Document