Adaptive multimodal continuous ant colony optimization

Qiang Yang, Wei Neng Chen, Zhengtao Yu, Tianlong Gu, Yun Li, Huaxiang Zhang, Jun Zhang

Research output: Contribution to journalArticle

61 Citations (Scopus)

Abstract

Seeking multiple optima simultaneously, which multimodal optimization aims at, has attracted increasing attention but remains challenging. Taking advantage of ant colony optimization (ACO) algorithms in preserving high diversity, this paper intends to extend ACO algorithms to deal with multimodal optimization. First, combined with current niching methods, an adaptive multimodal continuous ACO algorithm is introduced. In this algorithm, an adaptive parameter adjustment is developed, which takes the difference among niches into consideration. Second, to accelerate convergence, a differential evolution mutation operator is alternatively utilized to build base vectors for ants to construct new solutions. Then, to enhance the exploitation, a local search scheme based on Gaussian distribution is self-adaptively performed around the seeds of niches. Together, the proposed algorithm affords a good balance between exploration and exploitation. Extensive experiments on 20 widely used benchmark multimodal functions are conducted to investigate the influence of each algorithmic component and results are compared with several state-of-the-art multimodal algorithms and winners of competitions on multimodal optimization. These comparisons demonstrate the competitive efficiency and effectiveness of the proposed algorithm, especially in dealing with complex problems with high numbers of local optima.

LanguageEnglish
Article number7511696
Pages191-205
Number of pages15
JournalIEEE Transactions on Evolutionary Computation
Volume21
Issue number2
Early online date13 Jul 2016
DOIs
Publication statusPublished - 1 Apr 2017

Fingerprint

Continuous Optimization
Ant colony optimization
Multimodal Optimization
Optimization Algorithm
Niche
Exploitation
Niching
Multimodal Function
Differential Evolution
Local Search
Accelerate
Gaussian distribution
Adjustment
Mutation
Benchmark
Adaptive algorithms
Seed
Operator
Demonstrate
Experiment

Keywords

  • ant colony optimization (ACO)
  • multimodal optimization
  • multiple global optima
  • niching

Cite this

Yang, Q., Chen, W. N., Yu, Z., Gu, T., Li, Y., Zhang, H., & Zhang, J. (2017). Adaptive multimodal continuous ant colony optimization. IEEE Transactions on Evolutionary Computation, 21(2), 191-205. [7511696]. https://doi.org/10.1109/TEVC.2016.2591064
Yang, Qiang ; Chen, Wei Neng ; Yu, Zhengtao ; Gu, Tianlong ; Li, Yun ; Zhang, Huaxiang ; Zhang, Jun. / Adaptive multimodal continuous ant colony optimization. In: IEEE Transactions on Evolutionary Computation. 2017 ; Vol. 21, No. 2. pp. 191-205.
@article{af837e265e664508b83b81b3a3d2a13b,
title = "Adaptive multimodal continuous ant colony optimization",
abstract = "Seeking multiple optima simultaneously, which multimodal optimization aims at, has attracted increasing attention but remains challenging. Taking advantage of ant colony optimization (ACO) algorithms in preserving high diversity, this paper intends to extend ACO algorithms to deal with multimodal optimization. First, combined with current niching methods, an adaptive multimodal continuous ACO algorithm is introduced. In this algorithm, an adaptive parameter adjustment is developed, which takes the difference among niches into consideration. Second, to accelerate convergence, a differential evolution mutation operator is alternatively utilized to build base vectors for ants to construct new solutions. Then, to enhance the exploitation, a local search scheme based on Gaussian distribution is self-adaptively performed around the seeds of niches. Together, the proposed algorithm affords a good balance between exploration and exploitation. Extensive experiments on 20 widely used benchmark multimodal functions are conducted to investigate the influence of each algorithmic component and results are compared with several state-of-the-art multimodal algorithms and winners of competitions on multimodal optimization. These comparisons demonstrate the competitive efficiency and effectiveness of the proposed algorithm, especially in dealing with complex problems with high numbers of local optima.",
keywords = "ant colony optimization (ACO), multimodal optimization, multiple global optima, niching",
author = "Qiang Yang and Chen, {Wei Neng} and Zhengtao Yu and Tianlong Gu and Yun Li and Huaxiang Zhang and Jun Zhang",
year = "2017",
month = "4",
day = "1",
doi = "10.1109/TEVC.2016.2591064",
language = "English",
volume = "21",
pages = "191--205",
journal = "IEEE Transactions on Evolutionary Computation",
issn = "1089-778X",
number = "2",

}

Yang, Q, Chen, WN, Yu, Z, Gu, T, Li, Y, Zhang, H & Zhang, J 2017, 'Adaptive multimodal continuous ant colony optimization' IEEE Transactions on Evolutionary Computation, vol. 21, no. 2, 7511696, pp. 191-205. https://doi.org/10.1109/TEVC.2016.2591064

Adaptive multimodal continuous ant colony optimization. / Yang, Qiang; Chen, Wei Neng; Yu, Zhengtao; Gu, Tianlong; Li, Yun; Zhang, Huaxiang; Zhang, Jun.

In: IEEE Transactions on Evolutionary Computation, Vol. 21, No. 2, 7511696, 01.04.2017, p. 191-205.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Adaptive multimodal continuous ant colony optimization

AU - Yang, Qiang

AU - Chen, Wei Neng

AU - Yu, Zhengtao

AU - Gu, Tianlong

AU - Li, Yun

AU - Zhang, Huaxiang

AU - Zhang, Jun

PY - 2017/4/1

Y1 - 2017/4/1

N2 - Seeking multiple optima simultaneously, which multimodal optimization aims at, has attracted increasing attention but remains challenging. Taking advantage of ant colony optimization (ACO) algorithms in preserving high diversity, this paper intends to extend ACO algorithms to deal with multimodal optimization. First, combined with current niching methods, an adaptive multimodal continuous ACO algorithm is introduced. In this algorithm, an adaptive parameter adjustment is developed, which takes the difference among niches into consideration. Second, to accelerate convergence, a differential evolution mutation operator is alternatively utilized to build base vectors for ants to construct new solutions. Then, to enhance the exploitation, a local search scheme based on Gaussian distribution is self-adaptively performed around the seeds of niches. Together, the proposed algorithm affords a good balance between exploration and exploitation. Extensive experiments on 20 widely used benchmark multimodal functions are conducted to investigate the influence of each algorithmic component and results are compared with several state-of-the-art multimodal algorithms and winners of competitions on multimodal optimization. These comparisons demonstrate the competitive efficiency and effectiveness of the proposed algorithm, especially in dealing with complex problems with high numbers of local optima.

AB - Seeking multiple optima simultaneously, which multimodal optimization aims at, has attracted increasing attention but remains challenging. Taking advantage of ant colony optimization (ACO) algorithms in preserving high diversity, this paper intends to extend ACO algorithms to deal with multimodal optimization. First, combined with current niching methods, an adaptive multimodal continuous ACO algorithm is introduced. In this algorithm, an adaptive parameter adjustment is developed, which takes the difference among niches into consideration. Second, to accelerate convergence, a differential evolution mutation operator is alternatively utilized to build base vectors for ants to construct new solutions. Then, to enhance the exploitation, a local search scheme based on Gaussian distribution is self-adaptively performed around the seeds of niches. Together, the proposed algorithm affords a good balance between exploration and exploitation. Extensive experiments on 20 widely used benchmark multimodal functions are conducted to investigate the influence of each algorithmic component and results are compared with several state-of-the-art multimodal algorithms and winners of competitions on multimodal optimization. These comparisons demonstrate the competitive efficiency and effectiveness of the proposed algorithm, especially in dealing with complex problems with high numbers of local optima.

KW - ant colony optimization (ACO)

KW - multimodal optimization

KW - multiple global optima

KW - niching

UR - http://www.scopus.com/inward/record.url?scp=85018526850&partnerID=8YFLogxK

U2 - 10.1109/TEVC.2016.2591064

DO - 10.1109/TEVC.2016.2591064

M3 - Article

VL - 21

SP - 191

EP - 205

JO - IEEE Transactions on Evolutionary Computation

T2 - IEEE Transactions on Evolutionary Computation

JF - IEEE Transactions on Evolutionary Computation

SN - 1089-778X

IS - 2

M1 - 7511696

ER -