Dynamic population artificial bee colony algorithm for multi-objective optimal power flow

Man Ding; Hanning Chen; Na Lin; Shikai Jing; Fang Liu; Xiaodan Liang; Wei Liu

doi:10.1016/j.sjbs.2017.01.045

Dynamic population artificial bee colony algorithm for multi-objective optimal power flow

Man Ding, Hanning Chen^*, Na Lin, Shikai Jing, Fang Liu, Xiaodan Liang, Wei Liu

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

33 Citations (Scopus)

Abstract

This paper proposes a novel artificial bee colony algorithm with dynamic population (ABC-DP), which synergizes the idea of extended life-cycle evolving model to balance the exploration and exploitation tradeoff. The proposed ABC-DP is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. ABC-DP is then used for solving the optimal power flow (OPF) problem in power systems that considers the cost, loss, and emission impacts as the objective functions. The 30-bus IEEE test system is presented to illustrate the application of the proposed algorithm. The simulation results, which are also compared to nondominated sorting genetic algorithm II (NSGAII) and multi-objective ABC (MOABC), are presented to illustrate the effectiveness and robustness of the proposed method.

Original language	English
Pages (from-to)	703-710
Number of pages	8
Journal	Saudi Journal of Biological Sciences
Volume	24
Issue number	3
DOIs	https://doi.org/10.1016/j.sjbs.2017.01.045
Publication status	Published - 1 Mar 2017
Externally published	Yes

Keywords

Artificial bee colony algorithm
Life-cycle evolving model
Multi-objective optimization
Optimal power flow

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10.1016/j.sjbs.2017.01.045

Cite this

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title = "Dynamic population artificial bee colony algorithm for multi-objective optimal power flow",

abstract = "This paper proposes a novel artificial bee colony algorithm with dynamic population (ABC-DP), which synergizes the idea of extended life-cycle evolving model to balance the exploration and exploitation tradeoff. The proposed ABC-DP is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. ABC-DP is then used for solving the optimal power flow (OPF) problem in power systems that considers the cost, loss, and emission impacts as the objective functions. The 30-bus IEEE test system is presented to illustrate the application of the proposed algorithm. The simulation results, which are also compared to nondominated sorting genetic algorithm II (NSGAII) and multi-objective ABC (MOABC), are presented to illustrate the effectiveness and robustness of the proposed method.",

keywords = "Artificial bee colony algorithm, Life-cycle evolving model, Multi-objective optimization, Optimal power flow",

author = "Man Ding and Hanning Chen and Na Lin and Shikai Jing and Fang Liu and Xiaodan Liang and Wei Liu",

note = "Publisher Copyright: {\textcopyright} 2017 The Authors",

year = "2017",

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doi = "10.1016/j.sjbs.2017.01.045",

language = "English",

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T1 - Dynamic population artificial bee colony algorithm for multi-objective optimal power flow

AU - Ding, Man

AU - Chen, Hanning

AU - Lin, Na

AU - Jing, Shikai

AU - Liu, Fang

AU - Liang, Xiaodan

AU - Liu, Wei

PY - 2017/3/1

Y1 - 2017/3/1

N2 - This paper proposes a novel artificial bee colony algorithm with dynamic population (ABC-DP), which synergizes the idea of extended life-cycle evolving model to balance the exploration and exploitation tradeoff. The proposed ABC-DP is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. ABC-DP is then used for solving the optimal power flow (OPF) problem in power systems that considers the cost, loss, and emission impacts as the objective functions. The 30-bus IEEE test system is presented to illustrate the application of the proposed algorithm. The simulation results, which are also compared to nondominated sorting genetic algorithm II (NSGAII) and multi-objective ABC (MOABC), are presented to illustrate the effectiveness and robustness of the proposed method.

AB - This paper proposes a novel artificial bee colony algorithm with dynamic population (ABC-DP), which synergizes the idea of extended life-cycle evolving model to balance the exploration and exploitation tradeoff. The proposed ABC-DP is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. ABC-DP is then used for solving the optimal power flow (OPF) problem in power systems that considers the cost, loss, and emission impacts as the objective functions. The 30-bus IEEE test system is presented to illustrate the application of the proposed algorithm. The simulation results, which are also compared to nondominated sorting genetic algorithm II (NSGAII) and multi-objective ABC (MOABC), are presented to illustrate the effectiveness and robustness of the proposed method.

KW - Artificial bee colony algorithm

KW - Life-cycle evolving model

KW - Multi-objective optimization

KW - Optimal power flow

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SP - 703

EP - 710

JO - Saudi Journal of Biological Sciences

JF - Saudi Journal of Biological Sciences

IS - 3

ER -

Dynamic population artificial bee colony algorithm for multi-objective optimal power flow

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Keywords

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