Abstract
Target searching in unknown environment using multi-robot search systems has received increasing attention in recent years. Particle Swarm Optimization (PSO) has applied successfully on multi-robot target searching system. However, this algorithm suffer from premature convergence problem and cannot escape from the local optima. It is, therefore, important to have an efficient method to escape from the local optima and create and efficient balance between exploitation and exploration. In this study, we propose a new method based on PSO algorithm (ATREL-PSO) to find the target in unknown environment using multi-robot system within a limited time. This novel algorithm is demonstrated to escape from the local optima and create an efficient balance between exploration and exploitation to reach the target faster. The concept of attraction, repulsion and the combination of repulsion and attraction enhancing the search exploration, and when the robot get closer to the target it should forget the PSO concept and apply the local search method to reach the target faster. Experimental results obtained in a simulated environment show that biological and sociological inspiration could be useful to meet the challenges of robotic applications that can be described as optimization problems.
| Original language | English |
|---|---|
| Pages (from-to) | 4055-4065 |
| Number of pages | 11 |
| Journal | Journal of Theoretical and Applied Information Technology |
| Volume | 96 |
| Issue number | 13 |
| Publication status | Published - 15 Jul 2018 |
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Keywords
- Particle swarm optimization
- Premature convergence
- Swarm robots
- Target searching
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)
Cite this
Target searching in unknown environment of multi-robot system using a hybrid particle swarm optimization. / Nakisa, Bahareh; Rastgoo, Mohammad Naim; Ahmad Nazri, Mohd Zakree; Nordin, Md. Jan.
In: Journal of Theoretical and Applied Information Technology, Vol. 96, No. 13, 15.07.2018, p. 4055-4065.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Target searching in unknown environment of multi-robot system using a hybrid particle swarm optimization
AU - Nakisa, Bahareh
AU - Rastgoo, Mohammad Naim
AU - Ahmad Nazri, Mohd Zakree
AU - Nordin, Md. Jan
PY - 2018/7/15
Y1 - 2018/7/15
N2 - Target searching in unknown environment using multi-robot search systems has received increasing attention in recent years. Particle Swarm Optimization (PSO) has applied successfully on multi-robot target searching system. However, this algorithm suffer from premature convergence problem and cannot escape from the local optima. It is, therefore, important to have an efficient method to escape from the local optima and create and efficient balance between exploitation and exploration. In this study, we propose a new method based on PSO algorithm (ATREL-PSO) to find the target in unknown environment using multi-robot system within a limited time. This novel algorithm is demonstrated to escape from the local optima and create an efficient balance between exploration and exploitation to reach the target faster. The concept of attraction, repulsion and the combination of repulsion and attraction enhancing the search exploration, and when the robot get closer to the target it should forget the PSO concept and apply the local search method to reach the target faster. Experimental results obtained in a simulated environment show that biological and sociological inspiration could be useful to meet the challenges of robotic applications that can be described as optimization problems.
AB - Target searching in unknown environment using multi-robot search systems has received increasing attention in recent years. Particle Swarm Optimization (PSO) has applied successfully on multi-robot target searching system. However, this algorithm suffer from premature convergence problem and cannot escape from the local optima. It is, therefore, important to have an efficient method to escape from the local optima and create and efficient balance between exploitation and exploration. In this study, we propose a new method based on PSO algorithm (ATREL-PSO) to find the target in unknown environment using multi-robot system within a limited time. This novel algorithm is demonstrated to escape from the local optima and create an efficient balance between exploration and exploitation to reach the target faster. The concept of attraction, repulsion and the combination of repulsion and attraction enhancing the search exploration, and when the robot get closer to the target it should forget the PSO concept and apply the local search method to reach the target faster. Experimental results obtained in a simulated environment show that biological and sociological inspiration could be useful to meet the challenges of robotic applications that can be described as optimization problems.
KW - Particle swarm optimization
KW - Premature convergence
KW - Swarm robots
KW - Target searching
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M3 - Article
VL - 96
SP - 4055
EP - 4065
JO - Journal of Theoretical and Applied Information Technology
JF - Journal of Theoretical and Applied Information Technology
SN - 1992-8645
IS - 13
ER -