Self-adapting approach in parameter tuning for differential evolution

Wang, Shir Li; Ng, Theam Foo; Jamil, Nurul Aini; Samuri, Suzani Mohamad; Mailok, Ramlah; Rahmatullah, Bahbibi

doi:10.1109/taai.2015.7407109

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…The major advantage of DE algorithm over other evolutionary algorithms is that the diverse nature of control parameters and mutation strategies of DE algorithm increases the probability of finding optima for function optimization problems and other optimization problems (Wang et al, 2014;Yildiz, 2013;Ali et al, 2005;Engelbrecht, 2007;Storn and Price 1995;1997;Zamee et al, 2016). The varying nature of DE algorithm parameters enables it to escape from local optima problem (Pant et al, 2009;Wang et al, 2015;Li and Yin, 2016). The single objective as well as multi-objective versions of DE algorithm is successfully applied to many real life problems (Adeyemo et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

OPSODE: Opposition based particle swarm optimization instilled with differential evolution

Аббас

2017

Int. j. adv. appl. sci.

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Particle Swarm Optimization (PSO) is a very powerful global optimization technique. Differential Evolution (DE) is another fast and emerging algorithm of evolutionary computing. PSODE is hybrid of PSO and DE that incorporates diversity in the PSO algorithm. In this research a new opposition based version of PSODE (OPSODE) is proposed that incorporates some more diversity by employing the opposition based learning in the PSODE algorithm. Some standard benchmark functions are used to access the performance of the OPOSDE algorithm. The proposed version is then compared with the PSO, OPSO, and PSODE algorithm. The research result shows that the new version OPSODE has significance performance.

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