Multi-objective single agent stochastic search in non-dominated sorting genetic algorithm
Algirdas Lančinskas
Vilnius University, Lithuania
Pilar Martinez Martinez Ortigosa
Universidad de Almería, Spain
Julius Žilinskas
Vilnius University, Lithuania
Published 2013-07-25


multi-objective optimization
Pareto set
non-dominated sorting genetic algorithm
single agent stochastic search

How to Cite

Lančinskas, A., Ortigosa, P.M.M. and Žilinskas, J. (trans.) (2013) “Multi-objective single agent stochastic search in non-dominated sorting genetic algorithm”, Nonlinear Analysis: Modelling and Control, 18(3), pp. 293–313. doi:10.15388/NA.18.3.14011.


A hybrid multi-objective optimization algorithm based on genetic algorithm and stochastic local search is developed and evaluated. The single agent stochastic search local optimization algorithm has been modified in order to be suitable for multi-objective optimization where the local optimization is performed towards non-dominated points. The presented algorithm has been experimentally investigated by solving a set of well known test problems, and evaluated according to several metrics for measuring the performance of algorithms for multi-objective optimization. Results of the experimental investigation are presented and discussed.