Unaprjeđenja algoritma diferencijalne evolucije podešavanjem parametara i izborom počrtne populacije

Unaprjeđenja algoritma diferencijalne evolucije podešavanjem parametara i izborom počrtne populacijeThe need or tendency to improve different systems or models can be encountered in numerous forms of engineering and science. For that purpose, frequently optimization problems must be tackled. Such problems usually posses different properties that make them hard to solve. Besides, it is not uncommon that they are like black boxes which only provide responses to given inputs. Dealing with numerical optimization problems, differential evolution (DE) as an representative evolutionary algorithm (EA) may be pointed out, which is also in the center of the thesis. Although relatively simple, its performance is acclaimed. Three enhancements of DE are proposed in the thesis. Due to sensitivity to parameter values and the problem of determining appropriate ones, a self-adaptive scheme for controlling the scale factor and crossover-rate is proposed. It features, for each population member, the maintenance of a number of previously successful parameter values that are used for generating new ones. Also, the initial population may play an important factor in the performance of DE, and an initialization method is proposed which is based on data clustering and Cauchy deviates. The key element and difference between DE and other common EAs is its mutation. A mutation that employs an adaptive k-tournament selection for determining the base vector is proposed. All proposed enhancements were extensively tested on a set of selected standard functions and benchmark functions prepared for the CEC 2014 competition on numerical optimization. The analysis of the obtained results led to the conclusion that the enhancements considerably improve the performance of the algorithm incorporating them, but also that they compare favorably against similar enhancements from the literature. Finally, the behavior and performance of the standard DE algorithm were investigated in designing radial basis function networks for classification. The design of such networks represents a complex global optimization problem primarily from the viewpoint of the number of parameters that need to be adjusted and expansive evaluations. Although the standard algorithm is a viable choice for tackling this problem, the performed testing and analysis showed that the proposed enhancements yield improved performance in this case as wel

CreatorBajer, Dražen (Search Europeana for this person)
CollectionJosip Juraj Strossmayer University of Osijek. Faculty of Electrical Engineering, Computer Science and Information Technology Osijek. Department of Software Engineering. Chair of Programming Languages and Systems.
Repository Pagehttps://www.europeana.eu/portal/record/9200133/https___dr_ns...
Web Pagehttps://urn.nsk.hr/urn:nbn:hr:200:645925...
Subject TermsDifferential evolution, population initialization, classification, mutation, numerical optimization, radial basis function networks, self-adaptation of parameters, TECHNICAL SCIENCES. Computing. Program Engineering., Electrical engineering
ProviderNational and University Library in Zagreb