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dc.contributor.authorNeri, Ferranteen
dc.contributor.authorIacca, Giovannien
dc.contributor.authorMininno, Ernestoen
dc.date.accessioned2012-04-04T10:28:59Z
dc.date.available2012-04-04T10:28:59Z
dc.date.issued2011
dc.identifier.citationNeri, F., Iacca, G. and Mininno, E. (2011) Disturbed Exploitation Compact Differential Evolution for Limited Memory Optimization Problems. Information Sciences, 181 (12), pp 2469-2487en
dc.identifier.issn0020-0255
dc.identifier.urihttp://hdl.handle.net/2086/5881
dc.description.abstractThis paper proposes a novel and unconventional Memetic Computing approach for solving continuous optimization problems characterized by memory limitations. The proposed algorithm, unlike employing an explorative evolutionary framework and a set of local search algorithms, employs multiple exploitative search within the main framework and performs a multiple step global search by means of a randomized perturbation of the virtual population corresponding to a periodical randomization of the search for the exploitative operators. The proposed Memetic Computing approach is based on a populationless (compact) evolutionary framework which, instead of processing a population of solutions, handles its statistical model. This evolutionary framework is based on a Differential Evolution which cooperatively employs two exploitative search operators: the first is based on a standard Differential Evolution mutation and exponential crossover, and the second is the trigonometric mutation. These two search operators have an exploitative action on the algorithmic framework and thus contribute to the rapid convergence of the virtual population towards promising candidate solutions. The action of these search operators is counterbalanced by a periodical stochastic perturbation of the virtual population, which has the role of ‘‘disturbing’’ the excessively exploitative action of the framework and thus inhibits its premature convergence. The proposed algorithm, namely Disturbed Exploitation compact Differential Evolution, is a simple and memory-wise cheap structure that makes use of the Memetic Computing paradigm in order to solve complex optimization problems. The proposed approach has been tested on a set of various test problems and compared with state-of-the-art compact algorithms and with some modern population based meta-heuristics. Numerical results show that Disturbed Exploitation compact Differential Evolution significantly outperforms all the other compact algorithms present in literature and reaches a competitive performance with respect to modern population algorithms, including some memetic approaches and complex modern Differential Evolution based algorithms. In order to show the potential of the proposed approach in real-world applications, Disturbed Exploitation compact Differential Evolution has been implemented for performing the control of a space robot by simulating the implementation within the robot micro-controller. Numerical results show the superiority of the proposed algorithm with respect to other modern compact algorithms present in literature.en
dc.language.isoenen
dc.publisherElsevieren
dc.subjectdifferential evolutionen
dc.subjectevolutionary algorithmsen
dc.subjectcompact algorithmsen
dc.subjectmemetic computingen
dc.titleDisturbed exploitation compact differential evolution for limited memory optimization problemsen
dc.typeArticleen
dc.identifier.doihttp://dx.doi.org/10.1016/j.ins.2011.02.004
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.ref2014.selected1367395509_1210680252775_11_3
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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