The Parallel Single Front Genetic Algorithm (PSFGA) in Dynamic Multi-objective Optimization

This paper analyzes the use of the, previously proposed, Parallel Single Front Genetic Algorithm (PSFGA) in applications in which the objective functions, the restrictions, and hence also solutions can change over the time. These dynamic optimization problems appear in quite different real applications with relevant socio-economic impacts. PSFGA uses a master process that distributes the population among the processors in the system (that evolve their corresponding solutions according to an island model), and collects and adjusts the set of local Pareto fronts found by each processor (this way, the master also allows an implicit communication among islands). The procedure exclusively uses non-dominated individuals for the selection and variation, and maintains the diversity of the approximation to the Pareto front by using a strategy based on a crowding distance.

[1]  Juan Julián Merelo Guervós,et al.  Parallel Problem Solving from Nature — PPSN VII , 2002, Lecture Notes in Computer Science.

[2]  Enrique Alba,et al.  Parallel evolutionary algorithms can achieve super-linear performance , 2002, Inf. Process. Lett..

[3]  Ben Paechter,et al.  PSFGA : Parallel processing and evolutionary computation for multiobjective optimisation , 2004 .

[4]  Gary B. Lamont,et al.  Considerations in engineering parallel multiobjective evolutionary algorithms , 2003, IEEE Trans. Evol. Comput..

[5]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[6]  Julio Ortega Lopera,et al.  Evolutionary algorithms for multiobjective and multimodal optimization of diagnostic schemes , 2006, IEEE Transactions on Biomedical Engineering.

[7]  Karsten Weicker,et al.  Performance Measures for Dynamic Environments , 2002, PPSN.

[8]  Carlos A. Coello Coello,et al.  An updated survey of GA-based multiobjective optimization techniques , 2000, CSUR.

[9]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[10]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation) , 2006 .

[11]  Jürgen Branke *,et al.  Anticipation and flexibility in dynamic scheduling , 2005 .

[12]  Jürgen Branke,et al.  Evolutionary optimization in uncertain environments-a survey , 2005, IEEE Transactions on Evolutionary Computation.

[13]  Kalyanmoy Deb,et al.  Dynamic multiobjective optimization problems: test cases, approximations, and applications , 2004, IEEE Transactions on Evolutionary Computation.

[14]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.