Optimizacija problema raspoređivanja primjenom genetičkog algoritma s nišama

Abrashi, Arijan (2011) Optimizacija problema raspoređivanja primjenom genetičkog algoritma s nišama. = Doctoral thesis , Sveučilište u Zagrebu, Fakultet strojarstva i brodogradnje, UNSPECIFIED. Mentor: Štefanić, Nedeljko.

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19_07_2011_doktorat_abrashi.pdf Jezik dokumenta:Croatian

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Abstract (Croatian)

U doktorskom su radu dana osnovna svojstva genetičkoga algoritma te njegove prednosti i nedostaci. Posebno su razmatrane metode selekcije, operatori križanja i mutacije te direktan i indirektan način prezentacije kromosoma. Također je analizirana razlika između haploidnoga i diploidnoga kromosoma te je dana analiza utjecaja diploidnoga kromosoma na očuvanje genetske raznolikosti populacije. Predložen je i ispitan genetički algoritam s nišama (GA) koji se za usporedbu jedinki u populaciji koristi takozvanom Hamiltonovom sličnošću. Prednost Hamiltonove sličnosti je u tome da ne postoji potreba za poznavanjem problema koji se rješava da bi se uspješno usporedila dva člana populacije (u konkretnom slučaju dva rasporeda). Algoritam je ispitan na dvije grupe poznatih benchmark problema, mt4 i la5. Dobiveni rezultati pokazuju manju standardnu devijaciju predloženog algoritma u odnosu prema jednostavnom genetičkom algoritmu (SGA), što jasno upozorava na njegovu superiornost. Osim Hamiltonove sličnosti, predloženo je i vremenski zavisno skaliranje funkcije cilja koje u suradnji s nišama znatno umanjuje mogućnost zapinjanja genetičkog algoritma u jednom od manje poželjnih lokalnih optimuma. Na kraju su date smjernice za daljnja istraživanja na području genetičkih algoritama s nišama.

Abstract

In this PhD thesis basic characteristics of genetic algorithm have been presented along with its advantages and disadvantages. Selection methods, crossover and mutation operators, and direct and indirect chromosome presentation had especially been considered. The difference between haploid and diploid chromosomes was also analyzed and the influence of a diploid chromosome on the genetic diversity of a population presented. A niching genetic algorithm (GA), which uses so called Hamilton similarity for comparison of individuals in a population, was proposed and tested. The advantage of the Hamilton similarity lies in the fact that there is no need for context sensitive information in order to successfully compare two population members. Furthermore, the algorithm was tested on two famous benchmark problems (JSSP) - mt and la. The statistical results of the test were given. The significantly smaller standard deviation of the proposed GA compared to Simple GA clearly demonstrates its superiority. In addition to the Hamilton similarity, time dependent fitness scaling was also proposed, which in conjunction with niching significantly reduces the probability of the genetic algorithm getting trapped in one of the less desirable local optima. Finally, suggestions for future research are given.

Item Type: Thesis (Doctoral thesis)
Uncontrolled Keywords: genetički algoritam; niše; Hamiltonova sličnost; vremenski zavisno skaliranje funkcije cilja; raspoređivanje operacija po strojevima
Keywords (Croatian): genetic algorithm; niching; Hamilton similarity; time dependent fitness scaling; Job shop scheduling problem (JSSP)
Date Deposited: 22 Sep 2014 18:00
Last Modified: 16 Oct 2015 13:01
URI: http://repozitorij.fsb.hr/id/eprint/1483

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