An Improved Primal-Dual Genetic Algorithm for Optimization in Dynamic Environments

An Improved Primal-Dual Genetic Algorithm for Optimization in Dynamic Environments

Inspired by the complementary and dominance mechanism in nature, the Primal-Dual Genetic Algorithm (PDGA) has been proved successful in dynamic environments. In this paper, an important operator in PDGA, primal-dual mapping, is discussed and a new statistics-based primal-dual mapping scheme is propo...

Ausführliche Beschreibung

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Bibliographische Detailangaben
Hauptverfasser: Wang, Hongfeng, Wang, Dingwei
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Connectionism. Neural networks
Distance Space
Dynamic Environment
Dynamic Optimization Problem
Exact sciences and technology
Fitness Landscape
Genetic Algorithm
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Beschreibung
Zusammenfassung:Inspired by the complementary and dominance mechanism in nature, the Primal-Dual Genetic Algorithm (PDGA) has been proved successful in dynamic environments. In this paper, an important operator in PDGA, primal-dual mapping, is discussed and a new statistics-based primal-dual mapping scheme is proposed. The experimental results on the dynamic optimization problems generated from a set of stationary benchmark problems show that the improved PDGA has stronger adaptability and robustness than the original for dynamic optimization problems.
ISSN:0302-9743
1611-3349
DOI:10.1007/11893295_92