A novel particle swarm optimization algorithm with local search for dynamic constrained multi-objective optimization problems

In the real world, many optimization problems are dynamic constrained multi-objective optimization problems. This requires an optimization algorithm not only to find the global optimal solutions under a specific environment but also to track the trajectory of the varying optima over dynamic environm...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:2013 IEEE Congress on Evolutionary Computation s. 2436 - 2443
Hlavní autori: Jingxuan Wei, Liping Jia
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.06.2013
Predmet:
Algorithm design and analysis
Convergence
Force
Heuristic algorithms
Pareto optimization
Vectors
ISBN:1479904538, 9781479904532
ISSN:1089-778X
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:In the real world, many optimization problems are dynamic constrained multi-objective optimization problems. This requires an optimization algorithm not only to find the global optimal solutions under a specific environment but also to track the trajectory of the varying optima over dynamic environments. To address this requirement, this paper proposes a novel particle swarm optimization algorithm for such problems. This algorithm employs a new points selection strategy to speed up evolutionary process, and a local search operator to search optimal solutions in a promising subregion. The new algorithm is examined and compared with two wellknown algorithms on a sequence of benchmark functions. The results show that the proposed algorithm can effectively track the varying Pareto fronts over time. The proposed developments are effective individually, but the combined effect is much better for the test functions.
ISBN:1479904538
9781479904532
ISSN:1089-778X
DOI:10.1109/CEC.2013.6557861