Efficient Stochastic Optimization using Chaos Collocation Method with modeFRONTIER

Pediroda, Valentino; Parussini, Lucia; Poloni, Carlo; Parashar, Sumeet; Fateh, Nader; Poian, Mauro

doi:10.4271/2008-01-1429

Cited by 6 publications

(1 citation statement)

References 9 publications

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“…In recent years, the scientific community has paid increasing attention to the chaotic systems and their applications in various areas of science and engineering. Such systems have been investigated in such fields as parameter identifications [14], optimizations [45], electronic circuits [46], electric motor drives [47,48], power electronics [49], communications [50], robotics [51], and many others. Feng et al [52] introduced two means of modifying the inertia weight of a PSO using chaos.…”

Section: Chaos-enhanced Particle Swarm Optimization With Adaptive Parmentioning

confidence: 99%

A Chaos-Enhanced Particle Swarm Optimization with Adaptive Parameters and Its Application in Maximum Power Point Tracking

Hong

Beltran

Paglinawan

2016

Mathematical Problems in Engineering

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This work proposes an enhanced particle swarm optimization scheme that improves upon the performance of the standard particle swarm optimization algorithm. The proposed algorithm is based on chaos search to solve the problems of stagnation, which is the problem of being trapped in a local optimum and with the risk of premature convergence. Type 1 constriction is incorporated to help strengthen the stability and quality of convergence, and adaptive learning coefficients are utilized to intensify the exploitation and exploration search characteristics of the algorithm. Several well known benchmark functions are operated to verify the effectiveness of the proposed method. The test performance of the proposed method is compared with those of other popular population-based algorithms in the literature. Simulation results clearly demonstrate that the proposed method exhibits faster convergence, escapes local minima, and avoids premature convergence and stagnation in a high-dimensional problem space. The validity of the proposed PSO algorithm is demonstrated using a fuzzy logic-based maximum power point tracking control model for a standalone solar photovoltaic system.

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Section: Chaos-enhanced Particle Swarm Optimization With Adaptive Parmentioning

confidence: 99%

A Chaos-Enhanced Particle Swarm Optimization with Adaptive Parameters and Its Application in Maximum Power Point Tracking

Hong

Beltran

Paglinawan

2016

Mathematical Problems in Engineering

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Different states of multi-block based forecast engine for price and load prediction

Gao

Darvishan

Toghani

et al. 2019

International Journal of Electrical Power & Energy Systems

222

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Reverse Multi-Objective Robust Design Optimization (R-MORDO) Using Chaos Collocation Based Robustness Quantification for Engine Calibration

Parashar¹,

Clarich²,

Geremia³

et al. 2010

13th AIAA/ISSMO Multidisciplinary Analysis Optimization Conference

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Efficient Stochastic Optimization using Chaos Collocation Method with modeFRONTIER

Cited by 6 publications

References 9 publications

A Chaos-Enhanced Particle Swarm Optimization with Adaptive Parameters and Its Application in Maximum Power Point Tracking

A Chaos-Enhanced Particle Swarm Optimization with Adaptive Parameters and Its Application in Maximum Power Point Tracking

Different states of multi-block based forecast engine for price and load prediction

Reverse Multi-Objective Robust Design Optimization (R-MORDO) Using Chaos Collocation Based Robustness Quantification for Engine Calibration

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