Orthogonal Particle Swarm Optimization Algorithm and Its Application in Circuit Design

Yan, Xuesong; Liu, Hammin

doi:10.11591/telkomnika.v11i6.2135

Cited by 8 publications

(2 citation statements)

References 14 publications

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“…One of the meta-heuristic methods that can be employed to overcame the deficiency of those methods is Particle Swarm Optimization (PSO). PSO has shown a promising optimization method to solve a complex problem such as power system [8], electronic industry, wireless sensor network, feature selection [9], circuit design [10], multi-objective optimization [11], and determining neuron weights in fuzzy neural networks [12].…”

Section: Introductionmentioning

confidence: 99%

Good Parameters for PSO in Optimizing Laying Hen Diet

Alfarisy¹,

Mahmudy²,

Natsir³

2018

IJECE

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Manual formulation of poultry diet by taking into account the fulfillment of all nutrients requirement with least cost is a difficult task. Particle Swarm Optimization (PSO) shows promising technique to solve this problem. However, there is a lack of studying a good parameter for PSO to solve feed formulation problem since PSO is sensitive to control parameter which depends on the problem. Therefore, this study investigates good swarm size, total iterations, acceleration coefficients, and inertia weight to produce a better formula. PSO with proposed good parameters is compared with other parameters. The obtained result shows that PSO with good parameters choice produces the highest fitness. Furthermore, good parameters of PSO can be used as a reference for a software developer and for further research to optimize poultry diet using PSO.

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Section: Introductionmentioning

confidence: 99%

Good Parameters for PSO in Optimizing Laying Hen Diet

Alfarisy¹,

Mahmudy²,

Natsir³

2018

IJECE

View full text Add to dashboard Cite

show abstract

“…Following their work, Yan et al have put forward a new method for the design of logic circuits using a group of logic gates (Yan, Wu, & Liu, 2012). Their results show that they have been able to optimize the number of gates required in the circuit.…”

Section: Introductionmentioning

confidence: 99%