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An adaptive hybrid algorithm based on particle swarm optimization and differential evolution for global optimization.

Yu X, Cao J, Shan H, Zhu L, Guo J - ScientificWorldJournal (2014)

Bottom Line: The HPSO-DE enjoys the advantages of PSO and DE and maintains diversity of the population.Compared with PSO, DE, and their variants, the performance of HPSO-DE is competitive.The balanced parameter sensitivity is discussed in detail.

View Article: PubMed Central - PubMed

Affiliation: China Institute of Manufacturing Development, Nanjing University of Information Science & Technology, Nanjing 210044, China ; Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China.

ABSTRACT
Particle swarm optimization (PSO) and differential evolution (DE) are both efficient and powerful population-based stochastic search techniques for solving optimization problems, which have been widely applied in many scientific and engineering fields. Unfortunately, both of them can easily fly into local optima and lack the ability of jumping out of local optima. A novel adaptive hybrid algorithm based on PSO and DE (HPSO-DE) is formulated by developing a balanced parameter between PSO and DE. Adaptive mutation is carried out on current population when the population clusters around local optima. The HPSO-DE enjoys the advantages of PSO and DE and maintains diversity of the population. Compared with PSO, DE, and their variants, the performance of HPSO-DE is competitive. The balanced parameter sensitivity is discussed in detail.

Show MeSH
HPSO-DE with different balance parameters and other six algorithms on f7, f8, f9, f10, f11, f12, f13, and f14.
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig5: HPSO-DE with different balance parameters and other six algorithms on f7, f8, f9, f10, f11, f12, f13, and f14.

Mentions: Figures 4, 5, and 6 show the box plots of minimal values that HPSO-DE obtains with four different balanced parameters. The box has lines at the lower quartile, median, and upper quartile values. The whiskers are lines extending from each end of the box to show the extent of the remaining data. Outliers are data with values beyond the ends of the whiskers.


An adaptive hybrid algorithm based on particle swarm optimization and differential evolution for global optimization.

Yu X, Cao J, Shan H, Zhu L, Guo J - ScientificWorldJournal (2014)

HPSO-DE with different balance parameters and other six algorithms on f7, f8, f9, f10, f11, f12, f13, and f14.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3934314&req=5

fig5: HPSO-DE with different balance parameters and other six algorithms on f7, f8, f9, f10, f11, f12, f13, and f14.
Mentions: Figures 4, 5, and 6 show the box plots of minimal values that HPSO-DE obtains with four different balanced parameters. The box has lines at the lower quartile, median, and upper quartile values. The whiskers are lines extending from each end of the box to show the extent of the remaining data. Outliers are data with values beyond the ends of the whiskers.

Bottom Line: The HPSO-DE enjoys the advantages of PSO and DE and maintains diversity of the population.Compared with PSO, DE, and their variants, the performance of HPSO-DE is competitive.The balanced parameter sensitivity is discussed in detail.

View Article: PubMed Central - PubMed

Affiliation: China Institute of Manufacturing Development, Nanjing University of Information Science & Technology, Nanjing 210044, China ; Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China.

ABSTRACT
Particle swarm optimization (PSO) and differential evolution (DE) are both efficient and powerful population-based stochastic search techniques for solving optimization problems, which have been widely applied in many scientific and engineering fields. Unfortunately, both of them can easily fly into local optima and lack the ability of jumping out of local optima. A novel adaptive hybrid algorithm based on PSO and DE (HPSO-DE) is formulated by developing a balanced parameter between PSO and DE. Adaptive mutation is carried out on current population when the population clusters around local optima. The HPSO-DE enjoys the advantages of PSO and DE and maintains diversity of the population. Compared with PSO, DE, and their variants, the performance of HPSO-DE is competitive. The balanced parameter sensitivity is discussed in detail.

Show MeSH