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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.

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Performance of the algorithms for  f9, f10  f11, f12, f13, f14, f15, and f16.
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Related In: Results  -  Collection


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fig2: Performance of the algorithms for  f9, f10  f11, f12, f13, f14, f15, and f16.

Mentions: The mean and standard deviation (Std) of the solutions in 25 independent runs are listed in Table 3. The best result among these algorithms is indicated by boldface in the table. Figures 1, 2, and 3 show the comparisons in terms of convergence, mean solutions, and evolution processes in solving 16 benchmark functions.


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)

Performance of the algorithms for  f9, f10  f11, f12, f13, f14, f15, and f16.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig2: Performance of the algorithms for  f9, f10  f11, f12, f13, f14, f15, and f16.
Mentions: The mean and standard deviation (Std) of the solutions in 25 independent runs are listed in Table 3. The best result among these algorithms is indicated by boldface in the table. Figures 1, 2, and 3 show the comparisons in terms of convergence, mean solutions, and evolution processes in solving 16 benchmark functions.

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