HC12: highly scalable optimisation algorithm In engineering as well as in non-engineering areas, numerous optimisation problems have to be solved using a wide range of optimisation methods. Soft-computing optimisation procedures are often applied to problems for which the classic mathematical optimisation approaches do not yield satisfactory results. In this paper we present a relatively new optimisation algorithm denoted as HC12 and demonstrate its possible parallel implementation. The paper aims to show that HC12 is highly scalable and can be implemented in a cluster of computers. As a practical consequence, the high scalability substantially reduces the computing time of optimisation problems.
Keywords for this software
References in zbMATH (referenced in 6 articles , 1 standard article )
Showing results 1 to 6 of 6.
- Kominkova Oplatkova, Zuzana; Senkerik, Roman; Zelinka, Ivan; Pluhacek, Michal: Analytic programming in the task of evolutionary synthesis of a controller for high order oscillations stabilization of discrete chaotic systems (2013)
- Matousek, Radomil; Minar, Petr: Stabilization of chaotic logistic equation using HC12 and grammatical evolution (2013)
- Oplatkova, Zuzana Kominkova; Senkerik, Roman: Evolutionary synthesis of complex structures -- pseudo neural networks for the task of iris dataset classification (2013)
- Skanderova, Lenka; Zelinka, Ivan; Šaloun, Petr: Chaos powered selected evolutionary algorithms (2013) ioport
- Zelinka, Ivan; Chadli, Mohammed; Davendra, Donald; Senkerik, Roman; Pluhacek, Michal; Lampinen, Jouni: Do evolutionary algorithms indeed require random numbers? Extended study (2013)
- Matousek, Radomil: HC12: highly scalable optimisation algorithm (2010)