GPGPU based Multi-hive ABC Algorithm for Constrained Global Optimization Problems

Sandeep Mane; Amol Adamuthe; Aprupa Pawar

Research Article

GPGPU based Multi-hive ABC Algorithm for Constrained Global Optimization Problems

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@ARTICLE{10.4108/eai.13-7-2018.163156,
    author={Sandeep U. Mane and Amol C. Adamuthe and Aprupa S. Pawar},
    title={GPGPU based Multi-hive ABC Algorithm for Constrained Global Optimization Problems},
    journal={EAI Endorsed Transactions on Energy Web},
    volume={7},
    number={28},
    publisher={EAI},
    journal_a={EW},
    year={2020},
    month={2},
    keywords={Multi-hive ABC algorithm, GPGPU, Coarse-grained model, Constrained benchmark functions, Optimization problems},
    doi={10.4108/eai.13-7-2018.163156}
}

Sandeep U. Mane
Amol C. Adamuthe
Aprupa S. Pawar
Year: 2020
GPGPU based Multi-hive ABC Algorithm for Constrained Global Optimization Problems
EW
EAI
DOI: 10.4108/eai.13-7-2018.163156

Sandeep U. Mane¹^,*, Amol C. Adamuthe², Aprupa S. Pawar³

1: Dept. of CSE, Rajarambapu Institute of Technology (affiliated to Shivaji University Kolhapur), Rajaramnagar, Sangli Dist., MH, India
2: Dept. of CS&IT, Rajarambapu Institute of Technology (affiliated to Shivaji University Kolhapur), Rajaramnagar, Sangli Dist., MH, India
3: Dept. of CSE, Walchand College of Engineering, Sangli, (affiliated to Shivaji University Kolhapur), MH, India

*Contact email: manesandip82@gmail.com

Abstract

INTRODUCTION: The artificial bee colony (ABC) algorithm is a nature-inspired technique used for solving different optimization problems. This paper presents a multi-hive ABC algorithm for solving constrained benchmark functions of CEC2006. The CEC2006 data set contains the global benchmark functions with different design variables, number and type of constraints.

OBJECTIVES: The objective of the proposed work is to design and apply the GPGPU based multi-hive ABC algorithm to solve constrained optimization problems.

METHODS: The proposed approach is a multi-population coarse-grained system implemented using General Purpose Graphics Processing Unit (GPGPU). The performance of the proposed approach is compared with the serial ABC algorithm for eleven benchmark functions and results in the literature. The multi-hive ABC algorithm has multiple hives, each running separate ABC algorithm on different cores of GPGPU.

RESULTS: The proposed approach provides global best solutions in significantly reduced time for all benchmark functions. The speed-up obtained is approximately 7X to 9X. The GPGPU device utilization is approximately 57% to 91%.

CONCLUSION: The GPGPU based multi-hive ABC algorithm is a found good with respect to best results, speed up factor and GPU utilization to solve constrained optimization problems.

Keywords: Multi-hive ABC algorithm, GPGPU, Coarse-grained model, Constrained benchmark functions, Optimization problems

Received: 2019-09-30
Accepted: 2020-01-31
Published: 2020-02-14
Publisher: EAI

: http://dx.doi.org/10.4108/eai.13-7-2018.163156

Copyright © 2020 Sandeep U. Mane et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.