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Multi-swarm that learns

Trojanowski K.

Control and Cybernetics

2010

Vol. 39, no 2

359-375

This paper studies particle swarm optimization approach enriched by two versions of an extension aimed at gathering information during the optimization process. Application of these extensions, called memory mechanisms, increases computational cost, but it is spent to a benefit by incorporating the knowledge about the problem into the algorithm and this way improving its search abilities. The first mechanism is based on the idea of storing explicit solutions while the second one applies one-pass clustering algorithm to build clusters containing search experiences. The main disadvantage of the former mechanism is lack of good rules for identification of outdated solutions among the remembered ones and as a consequence unlimited growth of the memory structures as the optimization process goes. The latter mechanism uses other form of knowledge representation and thus allows us to control the amount of allocated resources more efficiently than the former one. Both mechanisms have been experimentally verified and their advantages and disadvantages in application for different types of optimized environments are discussed.

Multi-Swarm That Learn

Trojanowski K.

Prace Instytutu Podstaw Informatyki Polskiej Akademii Nauk

2009

Nr 1015

1-24

In this paper a dynamic optimization with particle swarm approach using two different memory mechanisms is studied. One of them is based on the idea of storing explicit solutions in memory structures while the other applies one-pass clustering algorithm to build clusters containing search experiences. Both mechanisms have been experimentally verified and their advantages and disadvantages in application for different types of testing environments have been discussed.

Artykuł zawiera wyniki badań dwóch mechanizmów pamięciowych stosowanych w roju cząsteczek do optymalizacji dynamicznej. Jeden z nich jest oparty na zasadzie gromadzenie gotowych rozwiązań w strukturach pamięci, natomiast drugi stosuje jednoprzejściowy algorytm do budowy klastrów, w których mogłyby być przechowywane doświadczenia zdobywane w trakcie procesu szukania. Obydwa mechanizmy zostały zweryfikowane w badaniach eksperymentalnych a ich wady i zalety objawiające się w zastosowaniach do różnych typów zadań zostały omówione.