A niched Pareto genetic algorithm for multiobjective optimization

Abstract

Many, if not most, optimization problems have multiple objectives. Historically, multiple objectives have been combined ad hoc to form a scalar objective function, usually through a linear combination (weighted sum) of the multiple attributes, or by turning objectives into constraints. The genetic algorithm (GA), however, is readily modified to deal with multiple objectives by incorporating the concept of Pareto domination in its selection operator, and applying a niching pressure to spread its population out along the Pareto optimal tradeoff surface. We introduce the Niched Pareto GA as an algorithm for finding the Pareto optimal set. We demonstrate its ability to find and maintain a diverse "Pareto optimal population" on two artificial problems and an open problem in hydrosystems.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>

Keywords

Pareto principleMathematical optimizationPopulationMulti-objective optimizationGenetic algorithmSelection (genetic algorithm)Computer sciencePareto optimalSet (abstract data type)MathematicsAlgorithmArtificial intelligence

Affiliated Institutions

University of Illinois Urbana-Champaign US

Related Publications

A fast and elitist multiobjective genetic algorithm: NSGA-II

Kalyanmoy Deb , Amrit Pratap , Sakshi Agarwal +1 more

Multi-objective evolutionary algorithms (MOEAs) that use non-dominated sorting and sharing have been criticized mainly for: (1) their O(MN/sup 3/) computational complexity (wher...

2002 IEEE Transactions on Evolutionary Com... 44965 citations

The Pareto archived evolution strategy: a new baseline algorithm for Pareto multiobjective optimisation

Joshua Knowles , David Corne

Most popular evolutionary algorithms for multiobjective optimisation maintain a population of solutions from which individuals are selected for reproduction. In this paper, we i...

2003 Proceedings of the 1999 Congress on E... 1267 citations

Applying the genetic approach to simulated annealing in solving some NP-hard problems

Feng‐Tse Lin , Cheng‐Yan Kao , Ching‐Chi Hsu

A stochastic approach called the annealing-genetic algorithm is presented for solving some well-known combinatorial optimization problems. This approach incorporates genetic alg...

1993 IEEE Transactions on Systems Man and ... 252 citations

A New Evolutionary Algorithm for Solving Many-Objective Optimization Problems

Oliver Cuate , Bilel Derbel , Arnaud Liefooghe +2 more

In this paper, we focus on the study of evolutionary algorithms for solving multiobjective optimization problems with a large number of objectives. First, a comparative study of...

2008 IEEE Transactions on Systems Man and ... 324 citations

Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art

David A. Van Veldhuizen , Gary B. Lamont

Solving optimization problems with multiple (often conflicting) objectives is, generally, a very difficult goal. Evolutionary algorithms (EAs) were initially extended and applie...

2000 Evolutionary Computation 1179 citations

Publication Info

Year: 2002
Type: article
Pages: 82-87
Citations: 2410
Access: Closed

External Links

View on DOI.org

Social Impact

Altmetric

A niched Pareto genetic algorithm for multiobjective optimization

PlumX Metrics

Social media, news, blog, policy document mentions

Citation Metrics

2410

OpenAlex

Cite This

APA Style

                            
                                    Jeffrey Horn, 
                                
                                    N. Nafpliotis, 
                                
                                    David E. Goldberg
                                
                            (2002). 
                            A niched Pareto genetic algorithm for multiobjective optimization. 
                            
                            , 82-87.
                            https://doi.org/10.1109/icec.1994.350037

Identifiers

DOI: 10.1109/icec.1994.350037