Biogeography-based optimization for constrained optimization problems

Biogeography-based optimization (BBO) has been recently proposed as a viable stochastic optimization algorithm and it has so far been successfully applied in a variety of fields, especially for unconstrained optimization problems. The present paper shows how BBO can be applied for constrained optimi...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Computers & operations research Ročník 39; číslo 12; s. 3293 - 3304
Hlavní autoři: Boussaïd, Ilhem, Chatterjee, Amitava, Siarry, Patrick, Ahmed-Nacer, Mohamed
Médium: Journal Article
Jazyk:angličtina
Vydáno: Kidlington Elsevier Ltd 01.12.2012
Elsevier
Pergamon Press Inc
Témata:
Algorithmics. Computability. Computer arithmetics
Algorithms
Applied sciences
Biogeography
Biogeography-based optimization
Computer Science
Computer science; control theory; systems
Constrained optimization
Constraints
Exact sciences and technology
Inequalities
Mathematical analysis
Mathematical models
Mathematical problems
Mathematical programming
Operational research and scientific management
Operational research. Management science
Operations Research
Optimization
Optimization algorithms
Preserves
Selection procedure
Stochastic models
Stochasticity
Strategy
Studies
Theoretical computing
Selection procedure
Biogeography-based optimization
Constrained optimization
Unconstrained optimization
Evolutionary algorithm
Expert system
Inequality constraint
Equality constraint
Biogeography
Feasibility
Objective function
Stochastic programming
ISSN:0305-0548, 1873-765X, 0305-0548
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Biogeography-based optimization (BBO) has been recently proposed as a viable stochastic optimization algorithm and it has so far been successfully applied in a variety of fields, especially for unconstrained optimization problems. The present paper shows how BBO can be applied for constrained optimization problems, where the objective is to find a solution for a given objective function, subject to both inequality and equality constraints. To solve such problems, the present work proposes three new variations of BBO. Each new version uses different update strategies, and each is tested on several benchmark functions. A successful implementation of an additional selection procedure is also proposed in this work which is based on the feasibility-based rule to preserve fitter individuals for subsequent generations. Our extensive experimentations successfully demonstrate the usefulness of all these modifications proposed for the BBO algorithm that can be suitably applied for solving different types of constrained optimization problems.
Bibliografie:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-2
content type line 23
ObjectType-Article-1
ObjectType-Feature-2
ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2012.04.012