Biogeography-based Optimization in Noisy Environments

Haiping Ma; Minrui Fei; Daniel J. Simon; Zixiang Chen

doi:10.1177/0142331214537015

Biogeography-based Optimization in Noisy Environments

Haiping Ma
, Minrui Fei
, Daniel J. Simon
, Zixiang Chen

Shaoxing University
Shanghai University

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

Abstract

Biogeography-based optimization (BBO) is a new evolutionary optimization algorithm that is based on the science of biogeography. In this paper, BBO is applied to the optimization of problems in which the fitness function is corrupted by random noise. Noise interferes with the BBO immigration rate and emigration rate, and adversely affects optimization performance. We analyse the effect of noise on BBO using a Markov model. We also incorporate re-sampling in BBO, which samples the fitness of each candidate solution several times and calculates the average to alleviate the effects of noise. BBO performance on noisy benchmark functions is compared with particle swarm optimization (PSO), differential evolution (DE), self-adaptive DE (SaDE) and PSO with constriction (CPSO). The results show that SaDE performs best and BBO performs second best. In addition, BBO with re-sampling is compared with Kalman filter-based BBO (KBBO). The results show that BBO with re-sampling achieves almost the same performance as KBBO but consumes less computational time

Original language	American English
Journal	Transactions of the Institute of Measurement and Control
Volume	37
DOIs	https://doi.org/10.1177/0142331214537015
State	Published - Feb 1 2015

Keywords

Biogeography-based optimization; evolutionary algorithm; Kalman filter; noisy optimization; re-sampling

Disciplines

Electrical and Computer Engineering

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10.1177/0142331214537015License: CC BY

Biogeography-based Optimization in Noisy EnvironmentsFinal published version, 904 KBLicense: CC BY

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title = "Biogeography-based Optimization in Noisy Environments",

abstract = " Biogeography-based optimization (BBO) is a new evolutionary optimization algorithm that is based on the science of biogeography. In this paper, BBO is applied to the optimization of problems in which the fitness function is corrupted by random noise. Noise interferes with the BBO immigration rate and emigration rate, and adversely affects optimization performance. We analyse the effect of noise on BBO using a Markov model. We also incorporate re-sampling in BBO, which samples the fitness of each candidate solution several times and calculates the average to alleviate the effects of noise. BBO performance on noisy benchmark functions is compared with particle swarm optimization (PSO), differential evolution (DE), self-adaptive DE (SaDE) and PSO with constriction (CPSO). The results show that SaDE performs best and BBO performs second best. In addition, BBO with re-sampling is compared with Kalman filter-based BBO (KBBO). The results show that BBO with re-sampling achieves almost the same performance as KBBO but consumes less computational time",

keywords = "Biogeography-based optimization; evolutionary algorithm; Kalman filter; noisy optimization; re-sampling",

author = "Haiping Ma and Minrui Fei and Simon, \{Daniel J.\} and Zixiang Chen",

note = "H. Ma, M. Fei, D. Simon and Z. Chen, {"}Biogeography-based optimization in noisy environments,{"} Transactions of the Institute of Measurement and Control, vol. 37, pp. 190-204, 2015.",

year = "2015",

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day = "1",

doi = "10.1177/0142331214537015",

language = "American English",

volume = "37",

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T1 - Biogeography-based Optimization in Noisy Environments

AU - Ma, Haiping

AU - Fei, Minrui

AU - Simon, Daniel J.

AU - Chen, Zixiang

N1 - H. Ma, M. Fei, D. Simon and Z. Chen, "Biogeography-based optimization in noisy environments," Transactions of the Institute of Measurement and Control, vol. 37, pp. 190-204, 2015.

PY - 2015/2/1

Y1 - 2015/2/1

N2 - Biogeography-based optimization (BBO) is a new evolutionary optimization algorithm that is based on the science of biogeography. In this paper, BBO is applied to the optimization of problems in which the fitness function is corrupted by random noise. Noise interferes with the BBO immigration rate and emigration rate, and adversely affects optimization performance. We analyse the effect of noise on BBO using a Markov model. We also incorporate re-sampling in BBO, which samples the fitness of each candidate solution several times and calculates the average to alleviate the effects of noise. BBO performance on noisy benchmark functions is compared with particle swarm optimization (PSO), differential evolution (DE), self-adaptive DE (SaDE) and PSO with constriction (CPSO). The results show that SaDE performs best and BBO performs second best. In addition, BBO with re-sampling is compared with Kalman filter-based BBO (KBBO). The results show that BBO with re-sampling achieves almost the same performance as KBBO but consumes less computational time

AB - Biogeography-based optimization (BBO) is a new evolutionary optimization algorithm that is based on the science of biogeography. In this paper, BBO is applied to the optimization of problems in which the fitness function is corrupted by random noise. Noise interferes with the BBO immigration rate and emigration rate, and adversely affects optimization performance. We analyse the effect of noise on BBO using a Markov model. We also incorporate re-sampling in BBO, which samples the fitness of each candidate solution several times and calculates the average to alleviate the effects of noise. BBO performance on noisy benchmark functions is compared with particle swarm optimization (PSO), differential evolution (DE), self-adaptive DE (SaDE) and PSO with constriction (CPSO). The results show that SaDE performs best and BBO performs second best. In addition, BBO with re-sampling is compared with Kalman filter-based BBO (KBBO). The results show that BBO with re-sampling achieves almost the same performance as KBBO but consumes less computational time

KW - Biogeography-based optimization; evolutionary algorithm; Kalman filter; noisy optimization; re-sampling

UR - https://engagedscholarship.csuohio.edu/enece_facpub/328

U2 - 10.1177/0142331214537015

DO - 10.1177/0142331214537015

M3 - Article

VL - 37

JO - Transactions of the Institute of Measurement and Control

JF - Transactions of the Institute of Measurement and Control

ER -

Biogeography-based Optimization in Noisy Environments

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