Multithreaded Parallel Dual Population Genetic Algorithm (MPDPGA) for unconstrained function optimizations on multi-core system

Various problems viz. population diversity problem, premature convergence problem and curse of dimensionality problem, are associated with Genetic Algorithm (GA). Dual Population GA (DPGA) helps to provide additional population diversity to the main population by means of crossbreeding between the m...

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Bibliographic Details
Published in:Applied mathematics and computation 2014-09, Vol.243, p.936-949
Main Authors: Umbarkar, A.J., Joshi, M.S., Hong, Wei-Chiang
Format: Article
Language:English
Subjects:
Convergence
Diversity in GA
DPGA
Dual Population Genetic Algorithm
Function optimization
Genetic algorithms
Mathematical analysis
Mathematical models
Optimization
Parallel Dual-Population GA
Premature convergence
Reserves
Serials
Standard deviation
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Summary:Various problems viz. population diversity problem, premature convergence problem and curse of dimensionality problem, are associated with Genetic Algorithm (GA). Dual Population GA (DPGA) helps to provide additional population diversity to the main population by means of crossbreeding between the main population and reserve population. This helps to solve the problem of premature convergence and helps in early convergence of the algorithm. The binary encoded Multithreaded Parallel DPGA (MPDPGA) is proposed in this paper to solve the problems of population diversity and premature convergence. The experimental results show that, the performance (mean, standard deviation and standard error of mean), student t-test, mean function evaluation and success rate of MPDPGA is better than serial DPGA (SDPGA) and simple GA (SGA).
ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2014.06.033