Development of golden section search driven Particle Swarm Optimization and its application

Sehoon Oh; Yoichi Hori

doi:10.1109/SICE.2006.314857

Development of golden section search driven Particle Swarm Optimization and its application

Sehoon Oh, Yoichi Hori

Department of Robotics and Mechatronics Engineering

The University of Tokyo

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

The Particle Swarm Optimization (PSO), although it has been widely used in various fields, has a step-size problem, which deteriorates optimization performance. This problem is resolved using the Golden Section Search (GSS) and the Steepest descent method. We also design a filter that will improve optimization performance of the proposed algorithm. The effectiveness of the proposed algorithm, including for which type of problems the proposed algorithm is adequate, is verified using some benchmark problems. Moreover, a hardware-in-the-loop system which consists of a NC system with two motors and a computer that optimizes control parameters of the NC controller is introduced as an industrial application.

Original language	English
Title of host publication	2006 SICE-ICASE International Joint Conference
Pages	2868-2873
Number of pages	6
DOIs	https://doi.org/10.1109/SICE.2006.314857
State	Published - 2006
Event	2006 SICE-ICASE International Joint Conference - Busan, Korea, Republic of Duration: 18 Oct 2006 → 21 Oct 2006

Publication series

Name	2006 SICE-ICASE International Joint Conference

Conference

Conference	2006 SICE-ICASE International Joint Conference
Country/Territory	Korea, Republic of
City	Busan
Period	18/10/06 → 21/10/06

Keywords

Golden section search
Hardware-in-the-loop
Parameter tuning
Particle swarm optimization
Precision motion control
Steepest descent method

Access to Document

10.1109/SICE.2006.314857

Cite this

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title = "Development of golden section search driven Particle Swarm Optimization and its application",

abstract = "The Particle Swarm Optimization (PSO), although it has been widely used in various fields, has a step-size problem, which deteriorates optimization performance. This problem is resolved using the Golden Section Search (GSS) and the Steepest descent method. We also design a filter that will improve optimization performance of the proposed algorithm. The effectiveness of the proposed algorithm, including for which type of problems the proposed algorithm is adequate, is verified using some benchmark problems. Moreover, a hardware-in-the-loop system which consists of a NC system with two motors and a computer that optimizes control parameters of the NC controller is introduced as an industrial application.",

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AU - Hori, Yoichi

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AB - The Particle Swarm Optimization (PSO), although it has been widely used in various fields, has a step-size problem, which deteriorates optimization performance. This problem is resolved using the Golden Section Search (GSS) and the Steepest descent method. We also design a filter that will improve optimization performance of the proposed algorithm. The effectiveness of the proposed algorithm, including for which type of problems the proposed algorithm is adequate, is verified using some benchmark problems. Moreover, a hardware-in-the-loop system which consists of a NC system with two motors and a computer that optimizes control parameters of the NC controller is introduced as an industrial application.

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KW - Parameter tuning

KW - Particle swarm optimization

KW - Precision motion control

KW - Steepest descent method

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ER -

Development of golden section search driven Particle Swarm Optimization and its application

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Keywords

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