Paper Info
Title
An archived-based stochastic ranking evolutionary algorithm (asrea) for multi-objective optimization
Abstract
In this paper, we propose a new multi-objective optimization algorithm called Archived-based Stochastic Ranking Evolutionary Algorithm (ASREA) that ranks the population by comparing individuals with members of an archive. The stochastic comparison breaks the usual O(mn2) complexity into O(man) (m being the number of objectives, a the size of the archive and n the population size), whereas updating the archive with distinct and well-spread non-dominated solutions and developed selection strategy retain the quality of state of the art deterministic multi-objective evolutionary algorithms (MOEAs). Comparison on ZDT and 3-objective DTLZ functions shows that ASREA converges on the Pareto-optimal front at least as well as NSGA-II and SPEA2 while reaching it much faster, and being cheaper on ranking comparisons.
Year
DOI
Venue
2010
10.1145/1830483.1830572
GECCO
Keywords
Field
DocType
3-objective dtlz function,usual o,stochastic comparison,ranking comparison,art deterministic multi-objective,asrea converges,stochastic ranking evolutionary algorithm,new multi-objective optimization algorithm,population size,pareto-optimal front,evolutionary algorithm,multi objective optimization,multiobjective optimization,evolutionary algorithms
Population,Mathematical optimization,Ranking,Evolutionary algorithm,Computer science,Evolutionary computation,Multi-objective optimization,Population size,Artificial intelligence,Optimization algorithm,Evolutionary programming,Machine learning
Conference
Citations 
PageRank 
References 
3
0.45
15
Authors
2
Name
Order
Citations
PageRank
Deepak Sharma130.45
Pierre Collet26211.26