On Some Properties of Quantum Particles in Multi-Swarms for Dynamic Optimization

• Autorzy: Trojanowski K.

Warianty tytułu

PL

O własnościach cząsteczek kwantowych stosowanych w wielorojach do optymalizacji dynamicznej

• Języki publikacji: EN

Abstrakty

EN

This paper studies properties of a multi-swarm system based on a concept of physical quantum particles (mQSO). Quantum particles differ from the classic ones in the way they move. As opposed to the classic view on a particle movement, where motion is controlled by linear kinematic laws, quantum particles change their location according to random distributions. The procedure of generating a new location for the quantum particle is similar to mutation operator widely used in evolutionary computation with real-valued representation. In this paper we study a set of new distributions of candidates for the quantum particle location, and we show different features of these distributions. The distributions considered in this paper are divided into two classes: those with a limited range of the new location coordinates and those without such limitations. They are tested on different types of dynamic optimization problems. Experimental verification has been based on a number of testing environments and two main versions of the algorithm: with and without mechanisms protecting against stagnation caused by convergence of sub-swarms during the search process. The experimental results show the advantages of the distribution class, in which the candidates are spread out in the entire search space, and indicate the positive and negative aspects of application of anti-convergence mechanisms.

PL

W niniejszym raporcie studiowane są właściwości systemów wielorojowych opartych na idei cząsteczek kwantowych (mQSO). W przeciwieństwie do klasycznego podejścia do ruchu cząsteczki, w którym przemieszczanie kontrolowane jest przez liniowe prawa kinematyki, kwantowe cząsteczki zmieniają swoje położenie wykorzystując rozkłady losowe. Tutaj badamy pewien zbiór nowych rozkładów kandydatów na nowe położenie cząsteczki kwantowej i demonstrujemy ich różne właściwości. Rozkłady rozpatrywane poniżej można podzielić na dwie klasy: z ograniczonym obszarem możliwych nowych położeń cząsteczki, oraz pozostałe z nieograniczonym obszarem tych położeń. Wszystkie zostały testowane na różnych typach zadań dynamicznych. Eksperymentalna weryfikacja została oparta na pewnej liczbie zadań testowych, a także na dwóch głównych wersjach algorytmu: uzwględniającego i nieuwzględniającego mechanizmy chroniące przeciwko stagnacji powodowanej przez zbieganie zbioru rozwiązań algorytmu do niewielkich obszarów dziedziny w trakcie poszukiwań. Wyniki eksperymentów wskazują przewagę tej klasy rozkładów, w której kandydaci na nowe położenie cząsteczki mogą należeć do całego zbioru poszukiwań. Wyniki pokazują też pozytywne i negatywne aspekty stosowania mechanizmów anty-zbieżnosci.

Słowa kluczowe

EN

optimization in dynamic environments; particle swarm optimization (PSO); tracking optima in dynamic environments; multi-swarm; quantum particles

PL

optymalizacja rojem cząsteczek; wielorój; optymalizacja dynamiczna; cząsteczki kwantowe

• Wydawca: Instytut Podstaw Informatyki PAN
• Czasopismo: Prace Instytutu Podstaw Informatyki Polskiej Akademii Nauk
• Rocznik: 2009
• Tom: Nr 1016
• Strony: 1--60
• Opis fizyczny: Bibliogr. 51 poz., rys.

Twórcy

autor

Trojanowski K.

• Instytut Podstaw Informatyki PAN 01-237 Warszawa, Ordona 21 Polska

Bibliografia

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