A faster path planner using accelerated particle swarm optimization

Mohamed, Abdullah Zawawi; Lee, Sang‐Heon; Hsu, Hung-Yao; Nath, Namrata

doi:10.1007/s10015-012-0051-3

Cited by 24 publications

(9 citation statements)

References 10 publications

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“…Karimi using the time scale to determine when a robot moves along the given reference trajectory [11]. In order to solve the problem of robot location and movement, Mohamed et al proposed accelerated PSO to plan a robot's path [12]. Besides, in [13], a new environment model called the danger degree map is constructed.…”

Section: IImentioning

confidence: 99%

Robot path planning in an environment with many terrains based on interval multi-objective PSO

Geng

Gong

Zhang

2013

2013 IEEE Congress on Evolutionary Computation

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In order to solve the problem of path planning in an environment with many terrains, we propose a method based on interval multi-objective Particle Swarm Optimization (PSO). First, the environment is modeled by the line partition method, and then, according to the distribution of the polygonal lines which form the robot path and taking the velocity's disturbance into consideration, robot's passing time is formulated as an interval by combining Local Optimal Criterion (LOC), and the path's danger degree is estimated through the area ratio between the robot path and the danger source. In addition, the path length is also calculated as an optimization objective. As a result, the robot path planning problem is modeled as an optimization problem with three objectives. Finally, the interval multiobjective PSO is employed to solve the problem above. Simulation and experimental results verify the effectiveness of the proposed method.

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Section: IImentioning

confidence: 99%

Robot path planning in an environment with many terrains based on interval multi-objective PSO

Geng

Gong

Zhang

2013

2013 IEEE Congress on Evolutionary Computation

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“…[18] at Cambridge University in 2007 in order to accelerate the convergence of the algorithm is to use the global best only. PSO and APSO-based optimizations have already been studied by the researchers for optimal design of substation grounding grid [34], performance analysis of MIMO radar waveform [35], design of frame structures [36], dual channel speech enhancement [37] and a faster path planner [38] etc.…”

Section: A Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Optimal power allocation scheme for plug-in hybrid electric vehicles using swarm intelligence techniques

et al. 2016

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Green technologies gain popularity to reduce the pollution and give higher penetration of renewable energy source in the transportation. This research induce that the extensive involvement of plug-in hybrid electric vehicles (PHEVs) requires adequate charging allocation strategy using a combination of smart grid systems and smart charging infrastructures. It is also noticed that daytime charging station are necessary for daily usage of PHEVs due to the limited all-electric-range. Most of the research in the past have been stated that only proper charging control and infrastructure management can assure the larger participation of PHEVs.Therefore, researchers are trying to develop efficient control mechanism for charging infrastructure in order to facilitate upcoming PHEVs penetration in highway. Nevertheless, most of the past researcher already aware with the issue related to intelligent energy management. Yet, these studies could not fill the gap of the problem associated with intelligent energy management and require formulation of mathematical models with extensive use of computational intelligence-based optimization techniques to solve many technical problems. The outcome of this research study provides four optimization techniques that includes Hybrid method within swarm intelligence group for the State-of-Charge (SoC) optimization of PHEVs. The finding of this research simulation results obtained for maximizing the highly nonlinear objective function evaluate the comparative performance of all four techniques in terms of best fitness, convergence speed and computation time. Finally, the hybridization method (PSOGSA) presented in this dissertation uses the advantages of both PSO and GSA optimization and thus produce higher best fitness values. This study evaluates the performance of standard PSO, then Accelerated version of PSO (APSO), GSA algorithm and then Hybrid of PSO and GSA. The hybridization method (PSOGSA) uses the advantages of both PSO and GSA optimization and thus produce higher best fitness values. However, PSOGSA method takes much longer computational time than single methods because of incorporating two single methods in one algorithm. This research study suggests that PSOGSA method is a great promise for SoC optimization but it takes much longer computational time.

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“…The standard PSO method, originally proposed by Kennedy and Eberhart in [20] and later refined by Shi and Eberhart in [21], [22], has been applied in several scientific fields, such as in optimization analysis, computational intelligence, and scheduling applications. More than thirty PSO variants have been proposed so far to achieve accelerated results, just to mention [24], [25]. However, existing algorithms neglect the acceleration factor of the particles in the swarm, whereas they adopt the term "accelerated" to characterize their convergence rate.…”

Section: Introductionmentioning

confidence: 99%

Dynamic spectrum sensing through accelerated particle swarm optimization

Paschos

Kapinas

Ntouni

et al. 2017

2017 25th Telecommunication Forum (TELFOR)

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In this paper, a novel optimization algorithm, called the acceleration-aided particle swarm optimization (A-APSO), is proposed for reliable dynamic spectrum sensing in cognitive radio networks. In A-APSO, the acceleration variable of the particles in the swarm is also considered in the search space of the optimization problem. We show that the proposed A-APSO based spectrum sensing technique is more efficient in terms of performance than the corresponding one based on the standard particle swarm optimization algorithm.

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A faster path planner using accelerated particle swarm optimization

Cited by 24 publications

References 10 publications

Robot path planning in an environment with many terrains based on interval multi-objective PSO

Robot path planning in an environment with many terrains based on interval multi-objective PSO

Optimal power allocation scheme for plug-in hybrid electric vehicles using swarm intelligence techniques

Dynamic spectrum sensing through accelerated particle swarm optimization

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