An improved genetic algorithm based on a novel selection strategy for nonlinear programming problems

Genetic algorithm is a heuristic population-based search method that incorporates three primary operators: crossover, mutation and selection. Selection operator plays a crucial role in finding optimal solution for constrained optimization problems. In this paper, an improved genetic algorithm (IGA)...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Computers & chemical engineering Ročník 35; číslo 4; s. 615 - 621
Hlavní autoři: Tang, Ke-Zong, Sun, Ting-Kai, Yang, Jing-Yu
Médium: Journal Article
Jazyk:angličtina
Vydáno: Kidlington Elsevier Ltd 07.04.2011
Elsevier
Témata:
Applied sciences
Constraint-handling
Decision theory. Utility theory
Exact sciences and technology
Genetic algorithms
Mathematical programming
Non-dominated solution
Nonlinear programming problems
Operational research and scientific management
Operational research. Management science
Optimization
Nonlinear programming problems
Non-dominated solution
Optimization
Constraint-handling
Genetic algorithms
Constraint handling
Local search
Pareto optimum
Process selection
Partial ordering
Constraint satisfaction
Non linear programming
Search algorithm
Constrained optimization
Selection problem
Value function
Genetic algorithm
Optimal solution
Heuristic method
Robustness
Objective function
Mathematical programming
ISSN:0098-1354, 1873-4375
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Genetic algorithm is a heuristic population-based search method that incorporates three primary operators: crossover, mutation and selection. Selection operator plays a crucial role in finding optimal solution for constrained optimization problems. In this paper, an improved genetic algorithm (IGA) based on a novel selection strategy is presented to handle nonlinear programming problems. Each individual in selection process is represented as a three-dimensional feature vector composed of objective function value, the degree of constraints violations and the number of constraints violations. We can distinguish excellent individuals through two indices according to Pareto partial order. Additionally, IGA incorporates a local search ( LS) process into selection operation so as to find feasible solutions located in neighboring areas of some infeasible solutions. Experimental results over a set of benchmark problems demonstrate that proposed IGA has better robustness, effectiveness and stableness than other algorithm reported in literature.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0098-1354
1873-4375
DOI:10.1016/j.compchemeng.2010.06.014