Path Planning for Uninhabited Combat Aerial Vehicle Using Hybrid Meta-Heuristic DE/BBO Algorithm

Wang, Gai-Ge; Guo, Lihong; Duan, Hongliang; Liu, Luo; Wang, Heqi; Shao, Mingzhen

doi:10.1166/asem.2012.1223

Cited by 55 publications

(22 citation statements)

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“…The actual cost function ( ) can be calculated by formula (15), and here we discuss the calculation of ℎ( ).…”

Section: Trajectory Cost Functionmentioning

confidence: 99%

“…In SAS algorithm, the calculation of route cost can be expressed as follows: where ( ) is the distance between and target node and ( ) is the actual cost value. The cost value generated by the distance and threat can be calculated by formula (15).…”

Section: Trajectory Cost Functionmentioning

confidence: 99%

“…Reference [5] adopted sparse algorithm, but angle heuristic function is not taken into consideration and the number of constraint conditions is small; the simulation result is not scientific. Reference [7,15] proposed the algorithms with large amount of calculation, making the algorithms unsuitable to seek optimization solution. To address these problems, an improved heuristic algorithm [16] is studied which takes angle information into account in a certain range, two methods of trajectory smooth straightening processing are adopted and compared, and the corresponding simulations are given in this paper.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Improved Heuristic Algorithm for UCAV Path Planning

Kun

Kong

et al. 2017

Journal of Optimization

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The study of unmanned combat aerial vehicle (UCAV) path planning is increasingly important in military and civil field. This paper presents a new mathematical model and an improved heuristic algorithm based on Sparse * Search (SAS) for UCAV path planning problem. In this paper, flight constrained conditions will be considered to meet the flight restrictions and task demands. With three simulations, the impacts of the model on the algorithms will be investigated, and the effectiveness and the advantages of the model and algorithm will be validated.

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“…The actual cost function ( ) can be calculated by formula (15), and here we discuss the calculation of ℎ( ).…”

Section: Trajectory Cost Functionmentioning

confidence: 99%

Section: Trajectory Cost Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Improved Heuristic Algorithm for UCAV Path Planning

Kun

Kong

et al. 2017

Journal of Optimization

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show abstract

“…These algorithms have been used to successfully address many complicated engineering problems, such as ordinal regression 27 , classification 28 , data encryption 29 , possession 30 , scheduling 31 , test-sheet composition 32 , target assessment [33][34] , path planning [35][36][37] , directing orbits of chaotic systems 38 , feature selection 39 , and fault diagnosis 40 .…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Cuckoo Search Algorithm with Global Harmony Search for 0–1 Knapsack Problems

Feng¹,

Wang²,

Gao³

2016

IJCIS

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Cuckoo search (CS) is a novel biologically inspired algorithm and has been widely applied to many fields. Although some binary-coded CS variants are developed to solve 0-1 knapsack problems, the search accuracy and the convergence speed are still needed to further improve. According to the analysis of the shortcomings of the standard CS and the advantage of the global harmony search (GHS), a novel hybrid meta-heuristic optimization approach, called cuckoo search Algorithm with global harmony search (CSGHS), is proposed in this paper to solve 0-1 knapsack problems (KP) more effectively. In CSGHS, it is the combination of the exploration of GHS and the exploitation of CS that makes the CSGHS efficient and effective. The experiments conducted demonstrate that the CSGHS generally outperformed the binary cuckoo search, the binary shuffled frog-leaping algorithm and the binary differential evolution in accordance with the search accuracy and convergence speed. Therefore, the proposed hybrid algorithm is effective to solve 0-1 knapsack problems.

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“…Among them, swarm-based metaheuristic search, so called swarm intelligence methods, are one of the most well-known paradigms in nature-inspired algorithms. Due to its remarkable performance, they have dealt with a variety of applications, such as reliability [42,43], knapsack problems [44], quantitative interpretation [45], scheduling [46], path planning [47], parameter estimation [48], global numerical optimization [49][50][51], neural network training [52,53] and feature selection [54]. The KH method that is inspired by the krill herding behavior of krill in sea was first proposed by Gandomi and Alavi in 2012 [55,56].…”

Section: Introductionmentioning

confidence: 99%

A New Swarm Intelligence Approach for Clustering Based on Krill Herd with Elitism Strategy

Wang

2015

Algorithms

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As one of the most popular and well-recognized clustering methods, fuzzy C-means (FCM) clustering algorithm is the basis of other fuzzy clustering analysis methods in theory and application respects. However, FCM algorithm is essentially a local search optimization algorithm. Therefore, sometimes, it may fail to find the global optimum. For the purpose of getting over the disadvantages of FCM algorithm, a new version of the krill herd (KH) algorithm with elitism strategy, called KHE, is proposed to solve the clustering problem. Elitism tragedy has a strong ability of preventing the krill population from degrading. In addition, the well-selected parameters are used in the KHE method instead of originating from nature. Through an array of simulation experiments, the results show that the KHE is indeed a good choice for solving general benchmark problems and fuzzy clustering analyses.

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Path Planning for Uninhabited Combat Aerial Vehicle Using Hybrid Meta-Heuristic DE/BBO Algorithm

Cited by 55 publications

References 0 publications

An Improved Heuristic Algorithm for UCAV Path Planning

An Improved Heuristic Algorithm for UCAV Path Planning

A Novel Hybrid Cuckoo Search Algorithm with Global Harmony Search for 0–1 Knapsack Problems

A New Swarm Intelligence Approach for Clustering Based on Krill Herd with Elitism Strategy

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