A novel hybrid algorithm based on PSO and FOA for target searching in unknown environments

In unknown environments, multiple-robot cooperation for target searching is a hot and difficult issue. Swarm intelligence algorithms, such as Particle Swarm Optimization (PSO) and Fruit Fly Optimization Algorithm (FOA), are widely used. To overcome local optima and enhance swarm diversity, this pape...

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Vydáno v:Applied intelligence (Dordrecht, Netherlands) Ročník 49; číslo 7; s. 2603 - 2622
Hlavní autoři: Tang, Hongwei, Sun, Wei, Yu, Hongshan, Lin, Anping, Xue, Min, Song, Yuxue
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.07.2019
Springer Nature B.V
Témata:
Algorithms
Artificial Intelligence
Computer Science
Falling
Machines
Manufacturing
Mechanical Engineering
Mutation
Obstacle avoidance
Particle swarm optimization
Processes
Robots
Searching
Swarm intelligence
Unknown environments
Multi-swarm
Fruit Fly optimization algorithm
Multi-scale cooperative mutation
Target searching
Particle swarm optimization
ISSN:0924-669X, 1573-7497
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Shrnutí:In unknown environments, multiple-robot cooperation for target searching is a hot and difficult issue. Swarm intelligence algorithms, such as Particle Swarm Optimization (PSO) and Fruit Fly Optimization Algorithm (FOA), are widely used. To overcome local optima and enhance swarm diversity, this paper presents a novel multi-swarm hybrid FOA-PSO (MFPSO) algorithm for robot target searching. The main contributions of the proposed method are as follows. (1) The improved FOA (IFOA) provides a better value for the improved PSO (IPSO) to find the next optimal robot position value. (2) Multi-swarm strategy is introduced to enhance the diversity and achieve an effective exploration to avoid premature convergence and falling into local optima. (3) An escape mechanism named MSCM (Multi-Scale Cooperative Mutation) is used to address the limitation of local optima and enhance the escape ability for obstacle avoidance. All of the aspects mentioned above lead robots to the target without falling into local optima and allow the search mission to be performed more quickly. Several experiments in four parts are performed to verify the better performance of MFPSO. The experimental results show that the performance of MFPSO is much more significant than that of other current approaches.
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ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-018-1390-0