Improved Particle Swarm Optimization Using Two Novel Parallel Inertia Weights

Liu, Huailiang; Su, Ruijuan; Gao, Ying; Xu, Ruoning

doi:10.1109/icicta.2009.53

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…Experiment 5 (comparison of LDIW-PSO with DLPSO2) . The results for LIDIW-PSO and DLPSO2 [ 11 ] in Table 8 are compared based on the best fitness, mean best fitness, convergence speed, as well as standard deviation (Std. Dev.)…”

Section: Resultsmentioning

confidence: 99%

“…In [ 11 ], two types of variants were proposed to solve the premature convergence problem of PSO. In this variant, two dynamic nonlinear methods and logistic chaotic map were used to adjust the inertia weight in parallel according to different fitness values.…”

Section: A Review Of Ldiw-pso and Some Of Its Competing Pso Varianmentioning

confidence: 99%

“…Experiment 5. This experiment compared LDIW-PSO with DLPSO2 [ 11 ]. All the test problems except f 4 were used in the experiment with two different problem dimensions of 2 (for f 3 and f 5 ) and 30 (for f 1 , f 2 , and f 6 ).…”

Section: Testing With Benchmark Problemsmentioning

confidence: 99%

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On the Performance of Linear Decreasing Inertia Weight Particle Swarm Optimization for Global Optimization

Arasomwan

Adediran

2013

The Scientific World Journal

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Linear decreasing inertia weight (LDIW) strategy was introduced to improve on the performance of the original particle swarm optimization (PSO). However, linear decreasing inertia weight PSO (LDIW-PSO) algorithm is known to have the shortcoming of premature convergence in solving complex (multipeak) optimization problems due to lack of enough momentum for particles to do exploitation as the algorithm approaches its terminal point. Researchers have tried to address this shortcoming by modifying LDIW-PSO or proposing new PSO variants. Some of these variants have been claimed to outperform LDIW-PSO. The major goal of this paper is to experimentally establish the fact that LDIW-PSO is very much efficient if its parameters are properly set. First, an experiment was conducted to acquire a percentage value of the search space limits to compute the particle velocity limits in LDIW-PSO based on commonly used benchmark global optimization problems. Second, using the experimentally obtained values, five well-known benchmark optimization problems were used to show the outstanding performance of LDIW-PSO over some of its competitors which have in the past claimed superiority over it. Two other recent PSO variants with different inertia weight strategies were also compared with LDIW-PSO with the latter outperforming both in the simulation experiments conducted.

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Section: Resultsmentioning

confidence: 99%

Section: A Review Of Ldiw-pso and Some Of Its Competing Pso Varianmentioning

confidence: 99%

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On the Performance of Linear Decreasing Inertia Weight Particle Swarm Optimization for Global Optimization

Arasomwan

Adediran

2013

The Scientific World Journal

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“…The Linearly Decreasing [12] [14], Linearly increasing [15] , Nonlinear [16], Sigmoid decreasing [17] , Adaptive [18], Random [19], Chaotic [20], Oscillating [21], Logarithmic decreasing [22] , Exponent Decreasing inertia weight strategies [23], [24], [25] and Fuzzy Adaptive Inertia Weight [26] were used in the literature. The Linearly Decreasing strategy improves the efficiency and performance of PSO.…”

Section:  Inertia Weightmentioning

confidence: 99%

Adaptive Random Link PSO with Link Change Variations and Confinement Handling

Kamalapur¹,

Patil²

2014

IJISA

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Abstract-Particle Swarm Optimization is swarm based optimization technique. Swarm consists of particles and the particles fly through the problem space in Particle Swarm Optimization (PSO). Confinement methods and parameters such as Inertia Weight, Neighborhood of the particle have major impact on PSO performance. The paper presents variations of the PSO with adaptive random link neighborhood. The work carried out considers linearly decreasing inertia weight and different confinement methods. The performance of adaptive random link PSO by geometrical updation of velocity with confinement methods is tested here.

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