A Hybrid Multi-Objective Teaching Learning-Based Optimization Using Reference Points and R2 Indicator

Farajollah Tahernezhadjavazm; Debbie Rankin; Damien Coyle

doi:10.1145/3533050.3533053

A Hybrid Multi-Objective Teaching Learning-Based Optimization Using Reference Points and R2 Indicator

Farajollah Tahernezhadjavazm
, Debbie Rankin
, Damien Coyle

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

162 Downloads (Pure)

Abstract

Hybrid multi-objective evolutionary algorithms have recently become a hot topic in the domain of metaheuristics. Introducing new algorithms that inherit other algorithms' operators and structures can improve the performance of the algorithm. Here, we proposed a hybrid multi-objective algorithm based on the operators of the genetic algorithm (GA) and teaching learning-based optimization (TLBO) and the structures of reference point-based (from NSGA-III) and R2 indicators methods. The new algorithm (R2-HMTLBO) improves diversity and convergence by using NSGA-III and R2-based TLBO, respectively. Also, to enhance the algorithm performance, an elite archive is proposed. The proposed multi-objective algorithm is evaluated on 19 benchmark test problems and compared to four state-of-the-art algorithms. IGD metric is applied for comparison, and the results reveal that the proposed R2-HMTLBO outperforms MOEA/D, MOMBI-II, and MOEA/IGD-NS significantly in 16/19 tests, 14/19 tests and 13/19 tests, respectively. Furthermore, R2-HMTLBO obtained considerably better results compared to all other algorithms in 4 test problems, although it does not outperform NSGA-III on a number of tests.

Original language	English
Title of host publication	ISMSI 2022 - 2022 6th International Conference on Intelligent Systems, Metaheuristics and Swarm Intelligence
Publisher	Association for Computing Machinery
Pages	19-23
Number of pages	5
ISBN (Electronic)	9781450396288
ISBN (Print)	9781450396288
DOIs	https://doi.org/10.1145/3533050.3533053
Publication status	Published (in print/issue) - 24 Jun 2022
Event	International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence - Republic South Korea, Seoul, Korea, Republic of Duration: 23 Apr 2022 → 24 Apr 2022 http://www.ismsi.org/

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence
Abbreviated title	ISMSI2022
Country/Territory	Korea, Republic of
City	Seoul
Period	23/04/22 → 24/04/22
Internet address	http://www.ismsi.org/

Bibliographical note

Publisher Copyright:
© 2022 Owner/Author.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

Evolutionary algorithm
Multi-objective optimization
Genetic Algorithm
Teaching Learning-based optimization
R2 indicator
Reference directions
Multi-objective evolutionary algorithm (MOEA)
Optimization algorithm
Reference point-based method
NSGA-III
Teaching learning-based optimization (TLBO)

Access to Document

10.1145/3533050.3533053

3533050.3533053Final published version, 537 KBLicence: CC BY

Cite this

Tahernezhadjavazm, F., Rankin, D., & Coyle, D. (2022). A Hybrid Multi-Objective Teaching Learning-Based Optimization Using Reference Points and R2 Indicator. In ISMSI 2022 - 2022 6th International Conference on Intelligent Systems, Metaheuristics and Swarm Intelligence (pp. 19-23). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3533050.3533053

Tahernezhadjavazm, Farajollah ; Rankin, Debbie ; Coyle, Damien. / A Hybrid Multi-Objective Teaching Learning-Based Optimization Using Reference Points and R2 Indicator. ISMSI 2022 - 2022 6th International Conference on Intelligent Systems, Metaheuristics and Swarm Intelligence. Association for Computing Machinery, 2022. pp. 19-23 (ACM International Conference Proceeding Series).

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abstract = "Hybrid multi-objective evolutionary algorithms have recently become a hot topic in the domain of metaheuristics. Introducing new algorithms that inherit other algorithms' operators and structures can improve the performance of the algorithm. Here, we proposed a hybrid multi-objective algorithm based on the operators of the genetic algorithm (GA) and teaching learning-based optimization (TLBO) and the structures of reference point-based (from NSGA-III) and R2 indicators methods. The new algorithm (R2-HMTLBO) improves diversity and convergence by using NSGA-III and R2-based TLBO, respectively. Also, to enhance the algorithm performance, an elite archive is proposed. The proposed multi-objective algorithm is evaluated on 19 benchmark test problems and compared to four state-of-the-art algorithms. IGD metric is applied for comparison, and the results reveal that the proposed R2-HMTLBO outperforms MOEA/D, MOMBI-II, and MOEA/IGD-NS significantly in 16/19 tests, 14/19 tests and 13/19 tests, respectively. Furthermore, R2-HMTLBO obtained considerably better results compared to all other algorithms in 4 test problems, although it does not outperform NSGA-III on a number of tests.",

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author = "Farajollah Tahernezhadjavazm and Debbie Rankin and Damien Coyle",

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year = "2022",

month = jun,

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Tahernezhadjavazm, F, Rankin, D & Coyle, D 2022, A Hybrid Multi-Objective Teaching Learning-Based Optimization Using Reference Points and R2 Indicator. in ISMSI 2022 - 2022 6th International Conference on Intelligent Systems, Metaheuristics and Swarm Intelligence. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 19-23, International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence, Seoul, Korea, Republic of, 23/04/22. https://doi.org/10.1145/3533050.3533053

A Hybrid Multi-Objective Teaching Learning-Based Optimization Using Reference Points and R2 Indicator. / Tahernezhadjavazm, Farajollah; Rankin, Debbie ; Coyle, Damien.
ISMSI 2022 - 2022 6th International Conference on Intelligent Systems, Metaheuristics and Swarm Intelligence. Association for Computing Machinery, 2022. p. 19-23 (ACM International Conference Proceeding Series).

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

TY - GEN

T1 - A Hybrid Multi-Objective Teaching Learning-Based Optimization Using Reference Points and R2 Indicator

AU - Tahernezhadjavazm, Farajollah

AU - Rankin, Debbie

AU - Coyle, Damien

PY - 2022/6/24

Y1 - 2022/6/24

N2 - Hybrid multi-objective evolutionary algorithms have recently become a hot topic in the domain of metaheuristics. Introducing new algorithms that inherit other algorithms' operators and structures can improve the performance of the algorithm. Here, we proposed a hybrid multi-objective algorithm based on the operators of the genetic algorithm (GA) and teaching learning-based optimization (TLBO) and the structures of reference point-based (from NSGA-III) and R2 indicators methods. The new algorithm (R2-HMTLBO) improves diversity and convergence by using NSGA-III and R2-based TLBO, respectively. Also, to enhance the algorithm performance, an elite archive is proposed. The proposed multi-objective algorithm is evaluated on 19 benchmark test problems and compared to four state-of-the-art algorithms. IGD metric is applied for comparison, and the results reveal that the proposed R2-HMTLBO outperforms MOEA/D, MOMBI-II, and MOEA/IGD-NS significantly in 16/19 tests, 14/19 tests and 13/19 tests, respectively. Furthermore, R2-HMTLBO obtained considerably better results compared to all other algorithms in 4 test problems, although it does not outperform NSGA-III on a number of tests.

AB - Hybrid multi-objective evolutionary algorithms have recently become a hot topic in the domain of metaheuristics. Introducing new algorithms that inherit other algorithms' operators and structures can improve the performance of the algorithm. Here, we proposed a hybrid multi-objective algorithm based on the operators of the genetic algorithm (GA) and teaching learning-based optimization (TLBO) and the structures of reference point-based (from NSGA-III) and R2 indicators methods. The new algorithm (R2-HMTLBO) improves diversity and convergence by using NSGA-III and R2-based TLBO, respectively. Also, to enhance the algorithm performance, an elite archive is proposed. The proposed multi-objective algorithm is evaluated on 19 benchmark test problems and compared to four state-of-the-art algorithms. IGD metric is applied for comparison, and the results reveal that the proposed R2-HMTLBO outperforms MOEA/D, MOMBI-II, and MOEA/IGD-NS significantly in 16/19 tests, 14/19 tests and 13/19 tests, respectively. Furthermore, R2-HMTLBO obtained considerably better results compared to all other algorithms in 4 test problems, although it does not outperform NSGA-III on a number of tests.

KW - Evolutionary algorithm

KW - Multi-objective optimization

KW - Genetic Algorithm

KW - Teaching Learning-based optimization

KW - R2 indicator

KW - Reference directions

KW - Multi-objective evolutionary algorithm (MOEA)

KW - Optimization algorithm

KW - Reference point-based method

KW - NSGA-III

KW - Teaching learning-based optimization (TLBO)

UR - https://www.scopus.com/pages/publications/85133483235

UR - https://pure.ulster.ac.uk/en/publications/97f6e5dc-fbf6-45ac-a2f9-1f1bca47217d

U2 - 10.1145/3533050.3533053

DO - 10.1145/3533050.3533053

M3 - Conference contribution

SN - 9781450396288

T3 - ACM International Conference Proceeding Series

SP - 19

EP - 23

BT - ISMSI 2022 - 2022 6th International Conference on Intelligent Systems, Metaheuristics and Swarm Intelligence

PB - Association for Computing Machinery

T2 - International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence

Y2 - 23 April 2022 through 24 April 2022

ER -

Tahernezhadjavazm F, Rankin D , Coyle D. A Hybrid Multi-Objective Teaching Learning-Based Optimization Using Reference Points and R2 Indicator. In ISMSI 2022 - 2022 6th International Conference on Intelligent Systems, Metaheuristics and Swarm Intelligence. Association for Computing Machinery. 2022. p. 19-23. (ACM International Conference Proceeding Series). doi: 10.1145/3533050.3533053

A Hybrid Multi-Objective Teaching Learning-Based Optimization Using Reference Points and R2 Indicator

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Integrating hybrid structures and reinforcement learning into multi-objective evolutionary algorithms: enhancing efficiency and adaptability

Cite this

A Hybrid Multi-Objective Teaching Learning-Based Optimization Using Reference Points and R2 Indicator

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Student theses

Integrating hybrid structures and reinforcement learning into multi-objective evolutionary algorithms: enhancing efficiency and adaptability

Cite this