OSA: Orthogonal Simulated Annealing Algorithm and Its Application to Designing Mixed&amp;lt;tex&amp;gt;$rm H_2 /rm H_infty$&amp;lt;/tex&amp;gt;Optimal Controllers

Ho, Shinn-Hang; Ho, Shinn‐Ying; Shu, Li-Sun

doi:10.1109/tsmca.2004.832834

Cited by 56 publications

(45 citation statements)

References 23 publications

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“…Studies in [44] used OED further with a factor analysis that predicts the potentially best combinations. The OED has also been introduced to other optimization algorithms such as simulated annealing [45], [46], ant colony optimization [47], and PSO [48]- [50].…”

Section: B Motivations Of the Ol Strategymentioning

confidence: 99%

Orthogonal Learning Particle Swarm Optimization

Zhan

Zhang

et al. 2011

IEEE Trans. Evol. Computat.

650

145

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Abstract-Particle swarm optimization (PSO) relies on its learning strategy to guide its search direction. Traditionally, each particle utilizes its historical best experience and its neighborhood's best experience through linear summation. Such a learning strategy is easy to use, but is inefficient when searching in complex problem spaces. Hence, designing learning strategies that can utilize previous search information (experience) more efficiently has become one of the most salient and active PSO research topics. In this paper, we proposes an orthogonal learning (OL) strategy for PSO to discover more useful information that lies in the above two experiences via orthogonal experimental design. We name this PSO as orthogonal learning particle swarm optimization (OLPSO). The OL strategy can guide particles to fly in better directions by constructing a much promising and efficient exemplar. The OL strategy can be applied to PSO with any topological structure. In this paper, it is applied to both global and local versions of PSO, yielding the OLPSO-G and OLPSO-L algorithms, respectively. This new learning strategy and the new algorithms are tested on a set of 16 benchmark functions, and are compared with other PSO algorithms and some state of the art evolutionary algorithms. The experimental results illustrate the effectiveness and efficiency of the proposed learning strategy and algorithms. The comparisons show that OLPSO significantly improves the performance of PSO, offering faster global convergence, higher solution quality, and stronger robustness.Index Terms-Global optimization, orthogonal experimental design (OED), orthogonal learning particle swarm optimization (OLPSO), particle swarm optimization (PSO), swarm intelligence.

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Section: B Motivations Of the Ol Strategymentioning

confidence: 99%

Orthogonal Learning Particle Swarm Optimization

Zhan

Zhang

et al. 2011

IEEE Trans. Evol. Computat.

650

145

View full text Add to dashboard Cite

show abstract

“…Example 2: To facilitate comparison with the GAbased, Improved GA-based and OSA-based methods proposed in [13,15,28], the same MIMO optimal control design problem is treated using the proposed algorithm. , are transformed to three linearly independent inputs.…”

Section: Illustrative Examplementioning

confidence: 99%

“…5(d). Outputs of the systems using the GA-based [15], Improved GA-based [28] and OSAbased [13] for the same system are also as shown in Figs GA-based PI controller [15]. (b) Improved GAbased PI controller [28].…”

Section: Illustrative Examplementioning

confidence: 99%

“…Beside the mentioned analytical methods, there are several numerical methods [13][14][15][16] which design a H ∞ PID controller according to minimization of an H 2 criterion such as integral of squared-error (ISE) for both SISO and MIMO systems. These methods not only satisfy the H ∞ -norm constraint to achieve the robust performance but also improve the step response of the system with minimizing the H 2 criterion.…”

Section: Introductionmentioning

confidence: 99%

“…We use Particle swarm optimization (PSO) algorithm [17,18] to minimize the cost function as a powerful optimization method with high efficiency in comparison to other methods such as Genetic Algorithm (GA) [19] and Simulated Annealing (SA) [20] which have been used in [13][14][15][16]. Especially, PSO is very powerful than GA in applications with highly epistatic objective function (i.e., where the parameters being optimized are highly correlated) [21].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Design of an H ∞ PID controller using particle swarm optimization

Zamani

Sadati

Karimi-Ghartemani

2009

Int. J. Control Autom. Syst.

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This paper proposes a novel method to designing an H ∞ PID controller with robust stability and disturbance attenuation. This method uses particle swarm optimization algorithm to minimize a cost function subject to H ∞ -norm to design robust performance PID controller. We propose two cost functions to design of a multiple-input, multiple-output (MIMO) and single-input, single-output (SISO) robust performance PID controller. We apply this method to a SISO flexible-link manipulator and a MIMO super maneuverable F18/HARV fighter aircraft system as two challenging examples to illustrate the design procedure and to verify performance of the proposed PID controller design methodology. It is shown with the MIMO super maneuverable F18/HARV fighter system that PSO performs well for parametric optimization functions and performance of the PSO-based method without prior domain knowledge is superior to those of existing GA-based and OSA-based methods for designing H ∞ PID controllers.

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Intelligent Particle Swarm Optimization in Multi-objective Problems

Jou

et al. 2006

Advances in Knowledge Discovery and Data Mining

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OSA: Orthogonal Simulated Annealing Algorithm and Its Application to Designing Mixed<tex>$rm H_2 /rm H_infty$</tex>Optimal Controllers

Cited by 56 publications

References 23 publications

Orthogonal Learning Particle Swarm Optimization

Orthogonal Learning Particle Swarm Optimization

Design of an H ∞ PID controller using particle swarm optimization

Intelligent Particle Swarm Optimization in Multi-objective Problems

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