A dynamic inertia weight particle swarm optimization algorithm

Jiao, Bin; Lian, Zhigang; Gu, Xingsheng

doi:10.1016/j.chaos.2006.09.063

Cited by 241 publications

(96 citation statements)

References 9 publications

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“…A larger inertia weight facilitates a global search, while a smaller inertia weight facilitates a local search [14]. A balance can be achieved between global and local exploration to speed up search results using a dynamically adjustable inertia weight formulation.…”

Section: Updating Velocitiesmentioning

confidence: 99%

“…There have been different strategies for determining the value dynamic inertia weight. Introducing a nonlinear decreasing inertia weight as a dynamic inertia weight into the original PSO significantly improves its performance through the parameter study of inertia weight [14]. This nonlinear distribution of inertia weight is expressed as follow:…”

Section: Updating Velocitiesmentioning

confidence: 99%

See 1 more Smart Citation

Evolution of Biped Walking Using Truncated Fourier Series and Particle Swarm Optimization

Shafii

Aslani

Nezami

et al. 2010

RoboCup 2009: Robot Soccer World Cup XIII

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Abstract. Controlling a biped robot with a high degree of freedom to achieve stable and straight movement patterns is a complex problem. With growing computational power of computer hardware, high resolution real time simulation of such robot models has become more and more applicable. This paper presents a novel approach to generate bipedal gait for humanoid locomotion. This approach is based on modified Truncated Fourier Series (TFS) for generating angular trajectories. It is also the first time that Particle Swarm Optimization (PSO) is used to find the best angular trajectory and optimize TFS. This method has been implemented on Simulated NAO robot in Robocup 3D soccer simulation environment (rcssserver3d). To overcome inherent noise of the simulator we applied a Resampling algorithm which could lead the robustness in nondeterministic environments. Experimental results show that PSO optimizes TFS faster and better than GA to generate straighter and faster humanoid locomotion.

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Section: Updating Velocitiesmentioning

confidence: 99%

Section: Updating Velocitiesmentioning

confidence: 99%

Evolution of Biped Walking Using Truncated Fourier Series and Particle Swarm Optimization

Shafii

Aslani

Nezami

et al. 2010

RoboCup 2009: Robot Soccer World Cup XIII

View full text Add to dashboard Cite

show abstract

“…Recently, Jiao et al (Jiao et al, 2008) proposed a dynamic inertia weight for the particle swarm optimization algorithm. They showed that it can improve the quality of the solutions obtained by the algorithm.…”

Section: Particle Swarm Optimization -Psomentioning

confidence: 99%

Multi-Ring Particle Swarm Optimization

Bastos-Filho¹,

Danilo²,

Marcel³

et al. 2009

Evolutionary Computation

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“…A particle swarm optimization algorithm with dynamic inertia weight is proposed by Jiao. In his algorithm, the greater the number of iterations, the smaller the inertia weight [5]. In the literature [6], the chaos algorithm is introduced into the particle swarm algorithm.…”

Section: Introductionmentioning

confidence: 99%