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A novel multi-objective optimization algorithm based on artificial bee colony

Authors:

Wenping Zou,

Yunlong Zhu,

Hanning Chen,

Hai ShenAuthors Info & Claims

GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation

Pages 103 - 104

https://doi.org/10.1145/2001858.2001917

Published: 12 July 2011 Publication History

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Abstract

Multi-objective optimization has been a difficult problem and focus for research in fields of science and engineering. This paper presents a novel algorithm based on Artificial Bee Colony (ABC) to deal with multi-objective optimization problems. ABC is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. It uses less control parameters and it can be efficiently used for solving multimodal and multidimensional optimization problems. Our algorithm uses the concept of Pareto dominance to determine the flight direction of a bee and it maintains nondominated solution vectors which have been found in an external archive. The proposed algorithm is validated using the standard test problems ZDT1 to ZDT3 and ZDT6, and simulation results show that the proposed approach is highly competitive and that can be considered a viable alternative to solve multi-objective optimization problems.

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Alhakkak N(2020)Modeling Smart Cloud Computing Resource Allocation in E-Learning2020 International Conference on Computer Vision, Image and Deep Learning (CVIDL)10.1109/CVIDL51233.2020.00-87(274-277)Online publication date: Jul-2020
https://doi.org/10.1109/CVIDL51233.2020.00-87
Sudharman SBindiya T(2020)Design of Reconfigurable FRM Channelizer using Resource Shared Non-maximally Decimated Masking FiltersJournal of Signal Processing Systems10.1007/s11265-020-01615-1Online publication date: 26-Nov-2020
https://doi.org/10.1007/s11265-020-01615-1
Yang LZhu AShao JChi T(2018)A Knowledge-Informed and Pareto-Based Artificial Bee Colony Optimization Algorithm for Multi-Objective Land-Use AllocationISPRS International Journal of Geo-Information10.3390/ijgi70200637:2(63)Online publication date: 12-Feb-2018
https://doi.org/10.3390/ijgi7020063
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Index Terms

A novel multi-objective optimization algorithm based on artificial bee colony
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction

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Development and investigation of efficient artificial bee colony algorithm for numerical function optimization

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The figure shows the PF produced by each algorithm on the two-objective test functions ZDT. It can be observed that our proposed algorithm EMOABC can produce competitive results that not only have good convergence but also have appropriate distribution ...

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Published In

GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation

July 2011

1548 pages

ISBN:9781450306904

DOI:10.1145/2001858

Editor:
Natalio Krasnogor
University of Nottingham, UK
,
General Chair:
Pier Luca Lanzi
Politecnico di Milano, Italy

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2011

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SIGEVO

GECCO '11: Genetic and Evolutionary Computation Conference

July 12 - 16, 2011

Dublin, Ireland

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Alhakkak N(2020)Modeling Smart Cloud Computing Resource Allocation in E-Learning2020 International Conference on Computer Vision, Image and Deep Learning (CVIDL)10.1109/CVIDL51233.2020.00-87(274-277)Online publication date: Jul-2020
https://doi.org/10.1109/CVIDL51233.2020.00-87
Sudharman SBindiya T(2020)Design of Reconfigurable FRM Channelizer using Resource Shared Non-maximally Decimated Masking FiltersJournal of Signal Processing Systems10.1007/s11265-020-01615-1Online publication date: 26-Nov-2020
https://doi.org/10.1007/s11265-020-01615-1
Yang LZhu AShao JChi T(2018)A Knowledge-Informed and Pareto-Based Artificial Bee Colony Optimization Algorithm for Multi-Objective Land-Use AllocationISPRS International Journal of Geo-Information10.3390/ijgi70200637:2(63)Online publication date: 12-Feb-2018
https://doi.org/10.3390/ijgi7020063
Kumar AKumar DJarial S(2017)A Review on Artificial Bee Colony Algorithms and Their Applications to Data ClusteringCybernetics and Information Technologies10.1515/cait-2017-002717:3(3-28)Online publication date: 4-Oct-2017
https://doi.org/10.1515/cait-2017-0027
Patel VSavsani V(2016)A multi-objective improved teaching-learning based optimization algorithm (MO-ITLBO)Information Sciences: an International Journal10.1016/j.ins.2014.05.049357:C(182-200)Online publication date: 20-Aug-2016
https://dl.acm.org/doi/10.1016/j.ins.2014.05.049
Saif UGuan ZLiu WZhang CWang B(2014)Pareto based artificial bee colony algorithm for multi objective single model assembly line balancing with uncertain task timesComputers and Industrial Engineering10.1016/j.cie.2014.07.00976:C(1-15)Online publication date: 1-Oct-2014
https://dl.acm.org/doi/10.1016/j.cie.2014.07.009
Zhong YXiang YLiu H(2014)A multi-objective artificial bee colony algorithm based on division of the searching spaceApplied Intelligence10.1007/s10489-014-0555-841:4(987-1011)Online publication date: 1-Dec-2014
https://dl.acm.org/doi/10.1007/s10489-014-0555-8
Mishra BDehuri SWang G(2013)A State-of-the-Art Review of Artificial Bee Colony in the Optimization of Single and Multiple CriteriaInternational Journal of Applied Metaheuristic Computing10.4018/ijamc.20131001024:4(23-45)Online publication date: 1-Oct-2013
https://dl.acm.org/doi/10.4018/ijamc.2013100102
Qi XZhu YNan LMa L(2013)A Learnable Artificial Bee Colony Algorithm for Numerical Function OptimizationProceedings of 20th International Conference on Industrial Engineering and Engineering Management10.1007/978-3-642-40063-6_35(349-360)Online publication date: 20-Nov-2013
https://doi.org/10.1007/978-3-642-40063-6_35
Akbari RHedayatzadeh RZiarati KHassanizadeh B(2012)A multi-objective artificial bee colony algorithmSwarm and Evolutionary Computation10.1016/j.swevo.2011.08.0012(39-52)Online publication date: Feb-2012
https://doi.org/10.1016/j.swevo.2011.08.001

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Development and investigation of efficient artificial bee colony algorithm for numerical function optimization

Velocity based artificial bee colony algorithm for high dimensional continuous optimization problems

Elite-guided multi-objective artificial bee colony algorithm

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