D-MAENS2: A Self-adaptive D-MAENS Algorithm with Better Decision Diversity

Qingquan ZHANG; Feng WU; Yang TAO; Jiyuan PEI; Jialin LIU; Xin YAO

doi:10.1109/SSCI47803.2020.9308250

D-MAENS2: A Self-adaptive D-MAENS Algorithm with Better Decision Diversity

Qingquan ZHANG, Feng WU, Yang TAO, Jiyuan PEI, Jialin LIU, Xin YAO

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

2 Citations (Scopus)

Abstract

The capacitated arc routing problem is a challenging combinatorial optimization problem with numerous real-world applications. In recent years, several multi-objective optimization algorithms have been applied to minimize both the total cost and makespan for capacitated arc routing problems, among which the decomposition-based memetic algorithm with extended neighborhood search has shown promising results. In this paper, we propose an improved decomposition-based memetic algorithm with extended neighborhood search, called D-MAENS2, which uses a novel method to construct a gene pool to measure and improve the diversity of solutions in decision variable space. Additionally, D-MAENS2 is capable of adapting online its hyper-parameters to various problem instances. Experimental studies show that our novel D-MAENS2 significantly outperforms D-MAENS on 81 benchmark instances and shows outstanding performance on instances of large size.

Original language	English
Title of host publication	2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2754-2761
Number of pages	8
ISBN (Electronic)	9781728125473
ISBN (Print)	9781728125473
DOIs	https://doi.org/10.1109/SSCI47803.2020.9308250
Publication status	Published - 1 Dec 2020
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

Capacitated arc routing problem
local search
memetic algorithms
meta-heuristics
multiobjective optimization

Access to Document

10.1109/SSCI47803.2020.9308250

Cite this

@inproceedings{d0611716de9d409f957a2f56fa500c72,

title = "D-MAENS2: A Self-adaptive D-MAENS Algorithm with Better Decision Diversity",

abstract = "The capacitated arc routing problem is a challenging combinatorial optimization problem with numerous real-world applications. In recent years, several multi-objective optimization algorithms have been applied to minimize both the total cost and makespan for capacitated arc routing problems, among which the decomposition-based memetic algorithm with extended neighborhood search has shown promising results. In this paper, we propose an improved decomposition-based memetic algorithm with extended neighborhood search, called D-MAENS2, which uses a novel method to construct a gene pool to measure and improve the diversity of solutions in decision variable space. Additionally, D-MAENS2 is capable of adapting online its hyper-parameters to various problem instances. Experimental studies show that our novel D-MAENS2 significantly outperforms D-MAENS on 81 benchmark instances and shows outstanding performance on instances of large size.",

keywords = "Capacitated arc routing problem, local search, memetic algorithms, meta-heuristics, multiobjective optimization",

author = "Qingquan ZHANG and Feng WU and Yang TAO and Jiyuan PEI and Jialin LIU and Xin YAO",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.",

year = "2020",

month = dec,

day = "1",

doi = "10.1109/SSCI47803.2020.9308250",

language = "English",

isbn = "9781728125473",

pages = "2754--2761",

booktitle = "2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

}

D-MAENS2: A Self-adaptive D-MAENS Algorithm with Better Decision Diversity. / ZHANG, Qingquan; WU, Feng; TAO, Yang et al.
2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 2754-2761 9308250.

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

TY - GEN

T1 - D-MAENS2: A Self-adaptive D-MAENS Algorithm with Better Decision Diversity

AU - ZHANG, Qingquan

AU - WU, Feng

AU - TAO, Yang

AU - PEI, Jiyuan

AU - LIU, Jialin

AU - YAO, Xin

PY - 2020/12/1

Y1 - 2020/12/1

N2 - The capacitated arc routing problem is a challenging combinatorial optimization problem with numerous real-world applications. In recent years, several multi-objective optimization algorithms have been applied to minimize both the total cost and makespan for capacitated arc routing problems, among which the decomposition-based memetic algorithm with extended neighborhood search has shown promising results. In this paper, we propose an improved decomposition-based memetic algorithm with extended neighborhood search, called D-MAENS2, which uses a novel method to construct a gene pool to measure and improve the diversity of solutions in decision variable space. Additionally, D-MAENS2 is capable of adapting online its hyper-parameters to various problem instances. Experimental studies show that our novel D-MAENS2 significantly outperforms D-MAENS on 81 benchmark instances and shows outstanding performance on instances of large size.

AB - The capacitated arc routing problem is a challenging combinatorial optimization problem with numerous real-world applications. In recent years, several multi-objective optimization algorithms have been applied to minimize both the total cost and makespan for capacitated arc routing problems, among which the decomposition-based memetic algorithm with extended neighborhood search has shown promising results. In this paper, we propose an improved decomposition-based memetic algorithm with extended neighborhood search, called D-MAENS2, which uses a novel method to construct a gene pool to measure and improve the diversity of solutions in decision variable space. Additionally, D-MAENS2 is capable of adapting online its hyper-parameters to various problem instances. Experimental studies show that our novel D-MAENS2 significantly outperforms D-MAENS on 81 benchmark instances and shows outstanding performance on instances of large size.

KW - Capacitated arc routing problem

KW - local search

KW - memetic algorithms

KW - meta-heuristics

KW - multiobjective optimization

UR - https://www.scopus.com/pages/publications/85099704521

U2 - 10.1109/SSCI47803.2020.9308250

DO - 10.1109/SSCI47803.2020.9308250

M3 - Conference paper (refereed)

SN - 9781728125473

SP - 2754

EP - 2761

BT - 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

D-MAENS2: A Self-adaptive D-MAENS Algorithm with Better Decision Diversity

Abstract

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this