A multi-objective PSO algorithm with transposon and elitist seeding approaches

Zhenlun Yang; Angus Wu; Huaqing Min

doi:10.1109/ICACI.2013.6748475

A multi-objective PSO algorithm with transposon and elitist seeding approaches

Zhenlun Yang
, Angus Wu
, Huaqing Min

Department of Electrical Engineering

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

4 Citations (Scopus)

Abstract

In this paper, we propose a new Particle Swarm Optimization (PSO) algorithm called Elitist Seeding Multi-Objective Particle Swarm Optimization with Transposon (ESMOPSO-T) to multi-objective optimization. ESMOPSO-T improves both the exploitation and exploration ability of MOPSO based on the combination of the transposon and elitist seeding approaches. ESMOPSO-T is compared against three state-of-the-art Metaheuristic algorithms, including a PSO-based approach and two evolutionary algorithms. Results indicate that the ESMOPSO-T is highly competitive in both approximating the Pareto-optimal front and maintaining the diversity of the solutions on the front. © 2013 IEEE.

Original language	English
Title of host publication	2013 6th International Conference on Advanced Computational Intelligence, ICACI 2013 - Proceedings
Publisher	IEEE Computer Society
Pages	64-69
ISBN (Print)	9781467363433
DOIs	https://doi.org/10.1109/ICACI.2013.6748475
Publication status	Published - 2013
Event	2013 6th International Conference on Advanced Computational Intelligence, ICACI 2013 - Hangzhou, Zhejiang, China Duration: 19 Oct 2013 → 21 Oct 2013

Conference

Conference	2013 6th International Conference on Advanced Computational Intelligence, ICACI 2013
Place	China
City	Hangzhou, Zhejiang
Period	19/10/13 → 21/10/13

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10.1109/ICACI.2013.6748475

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@inproceedings{82694f25dd364bbc9ea8cd33c77b9a91,

title = "A multi-objective PSO algorithm with transposon and elitist seeding approaches",

abstract = "In this paper, we propose a new Particle Swarm Optimization (PSO) algorithm called Elitist Seeding Multi-Objective Particle Swarm Optimization with Transposon (ESMOPSO-T) to multi-objective optimization. ESMOPSO-T improves both the exploitation and exploration ability of MOPSO based on the combination of the transposon and elitist seeding approaches. ESMOPSO-T is compared against three state-of-the-art Metaheuristic algorithms, including a PSO-based approach and two evolutionary algorithms. Results indicate that the ESMOPSO-T is highly competitive in both approximating the Pareto-optimal front and maintaining the diversity of the solutions on the front. {\textcopyright} 2013 IEEE.",

author = "Zhenlun Yang and Angus Wu and Huaqing Min",

year = "2013",

doi = "10.1109/ICACI.2013.6748475",

language = "English",

isbn = "9781467363433",

pages = "64--69",

booktitle = "2013 6th International Conference on Advanced Computational Intelligence, ICACI 2013 - Proceedings",

publisher = "IEEE Computer Society",

address = "United States",

note = "2013 6th International Conference on Advanced Computational Intelligence, ICACI 2013 ; Conference date: 19-10-2013 Through 21-10-2013",

}

Yang, Z, Wu, A & Min, H 2013, A multi-objective PSO algorithm with transposon and elitist seeding approaches. in 2013 6th International Conference on Advanced Computational Intelligence, ICACI 2013 - Proceedings., 6748475, IEEE Computer Society, pp. 64-69, 2013 6th International Conference on Advanced Computational Intelligence, ICACI 2013, Hangzhou, Zhejiang, China, 19/10/13. https://doi.org/10.1109/ICACI.2013.6748475

A multi-objective PSO algorithm with transposon and elitist seeding approaches. / Yang, Zhenlun; Wu, Angus; Min, Huaqing.
2013 6th International Conference on Advanced Computational Intelligence, ICACI 2013 - Proceedings. IEEE Computer Society, 2013. p. 64-69 6748475.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - A multi-objective PSO algorithm with transposon and elitist seeding approaches

AU - Yang, Zhenlun

AU - Wu, Angus

AU - Min, Huaqing

PY - 2013

Y1 - 2013

N2 - In this paper, we propose a new Particle Swarm Optimization (PSO) algorithm called Elitist Seeding Multi-Objective Particle Swarm Optimization with Transposon (ESMOPSO-T) to multi-objective optimization. ESMOPSO-T improves both the exploitation and exploration ability of MOPSO based on the combination of the transposon and elitist seeding approaches. ESMOPSO-T is compared against three state-of-the-art Metaheuristic algorithms, including a PSO-based approach and two evolutionary algorithms. Results indicate that the ESMOPSO-T is highly competitive in both approximating the Pareto-optimal front and maintaining the diversity of the solutions on the front. © 2013 IEEE.

AB - In this paper, we propose a new Particle Swarm Optimization (PSO) algorithm called Elitist Seeding Multi-Objective Particle Swarm Optimization with Transposon (ESMOPSO-T) to multi-objective optimization. ESMOPSO-T improves both the exploitation and exploration ability of MOPSO based on the combination of the transposon and elitist seeding approaches. ESMOPSO-T is compared against three state-of-the-art Metaheuristic algorithms, including a PSO-based approach and two evolutionary algorithms. Results indicate that the ESMOPSO-T is highly competitive in both approximating the Pareto-optimal front and maintaining the diversity of the solutions on the front. © 2013 IEEE.

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DO - 10.1109/ICACI.2013.6748475

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781467363433

SP - 64

EP - 69

BT - 2013 6th International Conference on Advanced Computational Intelligence, ICACI 2013 - Proceedings

PB - IEEE Computer Society

T2 - 2013 6th International Conference on Advanced Computational Intelligence, ICACI 2013

Y2 - 19 October 2013 through 21 October 2013

ER -

A multi-objective PSO algorithm with transposon and elitist seeding approaches

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