Global migration strategy with moving colony for hierarchical distributed evolutionary algorithms

In the established hierarchical distributed evolutionary algorithms (HDEAs), object of global migration is individual. To obtain better solutions of concrete problems, global migration strategy with moving colony is proposed in this paper. In global migration based on the proposed strategy, migration object is subpopulation which moves between groups. Such global migration can increase the efficiency of thereafter local migration. Moreover, realizing it even needs no communication because it can be executed by regrouping subpopulations. In our experiments, the basement of parallelism is an EA for the Traveling Salesman Problem. Outcomes of HDEAs based on proposed scheme which have different global migration topology are compared with those of traditional ones on nine benchmark instances. The results show that a HDEA based on the proposed strategy having the ring global topology performs better than traditional HDEAs for high difficulty instances. However, the advantage of that having the random global topology is not so significant because of conflicting migrations arisen from this topology.

[1]  Erick Cantú-Paz,et al.  A Survey of Parallel Genetic Algorithms , 2000 .

[2]  Dana S. Richards,et al.  Punctuated Equilibria: A Parallel Genetic Algorithm , 1987, ICGA.

[3]  Kyriakos C. Giannakoglou,et al.  Grid enabled, hierarchical distributed metamodel-assisted evolutionary algorithms for aerodynamic shape optimization , 2008, Future Gener. Comput. Syst..

[4]  K. Srinivas,et al.  Single and multi–objective UAV aerofoil optimisation via hierarchical asynchronous parallel evolutionary algorithm , 2006, The Aeronautical Journal (1968).

[5]  Conor Ryan,et al.  Promoting diversity using migration strategies in distributed genetic algorithms , 2005, 2005 IEEE Congress on Evolutionary Computation.

[6]  Zbigniew Michalewicz,et al.  Inver-over Operator for the TSP , 1998, PPSN.

[7]  Leonardo Vanneschi,et al.  Studying the Influence of Communication Topology and Migration on Distributed Genetic Programming , 2001, EuroGP.

[8]  Zbigniew Skolicki,et al.  The influence of migration sizes and intervals on island models , 2005, GECCO '05.

[9]  Dirk Sudholt,et al.  Design and analysis of migration in parallel evolutionary algorithms , 2013, Soft Comput..

[10]  David E. Goldberg,et al.  AllelesLociand the Traveling Salesman Problem , 1985, ICGA.

[11]  Marios K. Karakasis,et al.  Hierarchical distributed metamodel‐assisted evolutionary algorithms in shape optimization , 2007 .

[12]  Yan Bai,et al.  Identifying Stochastic Nonlinear Dynamic Systems Using Multi-objective Hierarchical Fair Competition Parallel Genetic Programming , 2010, J. Multiple Valued Log. Soft Comput..

[13]  David Millán-Ruiz,et al.  Matching island topologies to problem structure in parallel evolutionary algorithms , 2013, Soft Computing.

[14]  Brian W. Kernighan,et al.  An Effective Heuristic Algorithm for the Traveling-Salesman Problem , 1973, Oper. Res..

[15]  Francisco Herrera,et al.  Hierarchical distributed genetic algorithms , 1999 .

[16]  Eugenio Oñate,et al.  Active transonic aerofoil design optimization usingrobust multiobjective evolutionary algorithms , 2011 .

[17]  Enrique Alba,et al.  Parallelism and evolutionary algorithms , 2002, IEEE Trans. Evol. Comput..

[18]  Cai Zhi,et al.  An Improved Evolutionary Algorithm for the Traveling Salesman Problem , 2005 .

[19]  Sung-Kwun Oh,et al.  Identification of fuzzy relation models using hierarchical fair competition-based parallel genetic algorithms and information granulation , 2009 .