Adaptive particle swarm optimization

Yasuda, Keiichiro; Ide, Azuma; Iwasaki, Noriko

doi:10.1109/icsmc.2003.1244633

Cited by 67 publications

(40 citation statements)

References 3 publications

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“…This disagreement was informally explained by stochastic probability. Some other similar results were proposed by Yasuda (Yasuda et al, 2003) and Trelea (Trelea, 2003). Although those results provide insights into how particle swarm system works, all those analysis discard the randomness in the PSO algorithm, and are all based on a simplified deterministic algorithm.…”

Section: Related Workmentioning

confidence: 60%

Particle Swarm Optimization - Stochastic Trajectory Analysis and Parameter Selection

Jiang¹,

Luo²,

Yang³

2007

Swarm Intelligence, Focus on Ant and Particle Swarm Optimization

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Section: Related Workmentioning

confidence: 60%

Particle Swarm Optimization - Stochastic Trajectory Analysis and Parameter Selection

Jiang¹,

Luo²,

Yang³

2007

Swarm Intelligence, Focus on Ant and Particle Swarm Optimization

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“…5.2.1, and N 2 is number of parameter sets that obtain top 10% performance and whose a and b exist in S 1 . As shown in Table VIII, the ratios of f 7 , f 11 -f 13 , f 16 , f 18 , f 25 are under 0.5. The ratios of f 11 -f 13 , f 16 , f 18 accord with the appearance of Fig.…”

Section: Performance Analysismentioning

confidence: 86%

“…The ranges of parameters that would guarantee the convergence of the searching points have been investigated in many researches [13][14][15][16][17][18][19][20][21][22][23]. PSO can obtain better searching ability by the two important behavior, however, the performance of PSO can not be explained only by the diversity and the convergence.…”

Section: Introductionmentioning

confidence: 99%

Analysis of an optimizer based on piecewise-rotational chaotic system

Tsubone

2013

NOLTA

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An optimization method based on piecewise-rotational chaotic system (OPRC) is proposed. OPRC is a kind of multi-point searching methods in order to find an optimal solution, and these searching points are updated by piecewise-rotational chaotic dynamics. OPRC is a simple optimizer because these searching points are governed by simple dynamics which contains no stochastic terms. OPRC has significantly better performance than particle swarm optimization and our previous method based on another chaotic system. The relationship between the performance of OPRC and the time-series of the proposed chaotic system is analyzed. Then we clarify that OPRC obtains better solutions when the autocorrelation of the time-series takes negative values with damped oscillation.

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“…Therefore, in additon to the equilibrium points, the trajectories of PSO and PPSO are also different since trajectories of PPSO and PSO are characterised by a first-order and secondorder difference equations [Trelea, 2003, Yasuda et al, 2003, van den Bergh, 2003], respectively. These are the distinctive features of PSO and PPSO.…”

Section: Equilibrium Pointmentioning

confidence: 99%