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Kosmos
Astronomia Astrofizyka
Inne

Kultura
Sztuka dawna i współczesna, muzea i kolekcje

Metoda
Metodologia nauk, Matematyka, Filozofia, Miary i wagi, Pomiary

Materia
Substancje, reakcje, energia
Fizyka, chemia i inżynieria materiałowa

Człowiek
Antropologia kulturowa Socjologia Psychologia Zdrowie i medycyna

Wizje
Przewidywania Kosmologia Religie Ideologia Polityka

Ziemia
Geologia, geofizyka, geochemia, środowisko przyrodnicze

Życie
Biologia, biologia molekularna i genetyka

Cyberprzestrzeń
Technologia cyberprzestrzeni, cyberkultura, media i komunikacja

Działalność
Wiadomości | Gospodarka, biznes, zarządzanie, ekonomia

Technologie
Budownictwo, energetyka, transport, wytwarzanie, technologie informacyjne

Particle Swarm Optimization-based Augmented Lagrangian Algorithm for Constrained Optimization Problems

This study proposes a Particle Swarm Optimization-based Augmented Lagrangian (PSOAL) algorithm which combines particle swarm optimization technique with a non-stationary penalty function method to solve constrained optimization and engineering design problems. A set of novel strategies are developed based on the particle feasibility to adaptively update critical parameters and a point-based local search procedure is embedded within the algorithm framework to improve the convergence property of the proposed algorithm. The 13 well-known constrained benchmark problems are solved and the obtained results are compared with other state-of-the-art algorithms. The results demonstrate that, the proposed PSOAL achieves higher accuracy compared to other considered algorithms. In addition, as an added benefit, PSOAL can also easily find out the Lagrange multipliers, which have great value for sensitivity analysis in practice but are almost not considered in most intelligent algorithms designed for constrained problems.

Journal of Software Engineering 2014/05/08 - 18:01 Czytaj