On the impact of the migration topology on the Island Model

Parallel Global Optimization Algorithms (PGOA) provide an efficient way of dealing with hard optimization problems. One method of parallelization of GOAs that is frequently applied and commonly found in the contemporary literature is the so-called Island Model (IM). In this paper, we analyze the impact of the migration topology on the performance of a PGOA which uses the Island Model. In particular we consider parallel Differential Evolution and Simulated Annealing with Adaptive Neighborhood and draw first conclusions that emerge from the conducted experiments.

[1]  L. Darrell Whitley,et al.  Serial and Parallel Genetic Algorithms as Function Optimizers , 1993, ICGA.

[2]  W. Kruskal.  Ordinal Measures of Association , 1958 .

[3]  Christopher M. Brown,et al.  Parallel genetic algorithms on distributed-memory architectures , 1993 .

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

[5]  Theodore C. Belding,et al.  The Distributed Genetic Algorithm Revisited , 1995, ICGA.

[6]  Erick Cantú-Paz,et al.  A Summary of Research on Parallel Genetic Algorithms , 1995 .

[7]  David E. Goldberg,et al.  Efficient Parallel Genetic Algorithms: Theory and Practice , 2000 .

[8]  H. Homayounfar.  An Advanced Island Based GA For Optimization Problems , 2003 .

[9]  John J. Grefenstette,et al.  Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application , 1987 .

[10]  Jens Gottlieb,et al.  Evolutionary Computation in Combinatorial Optimization , 2006, Lecture Notes in Computer Science.

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

[12]  Krzysztof Bandurski,et al.  A Parallel Differential Evolution Algorithm A Parallel Differential Evolution Algorithm , 2006, PARELEC.

[13]  C. Ribeiro,et al.  Essays and Surveys in Metaheuristics , 2002, Operations Research/Computer Science Interfaces Series.

[14]  Welch Bl.  THE GENERALIZATION OF ‘STUDENT'S’ PROBLEM WHEN SEVERAL DIFFERENT POPULATION VARLANCES ARE INVOLVED , 1947 .

[15]  Dimitris K. Tasoulis,et al.  Parallel differential evolution , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[16]  Enrique Alba,et al.  Parallel Metaheuristics: A New Class of Algorithms , 2005 .

[17]  Reiko Tanese,et al.  Distributed Genetic Algorithms , 1989, ICGA.

[18]  Leonardo Vanneschi,et al.  An Empirical Study of Multipopulation Genetic Programming , 2003, Genetic Programming and Evolvable Machines.

[19]  Enrique Alba,et al.  A survey of parallel distributed genetic algorithms , 1999, Complex..

[20]  Dana S. Richards,et al.  Genetic Algorithms and Punctuated Equilibria in VLSI , 1990, PPSN.

[21]  Marco Tomassini,et al.  Effects of Scale-Free and Small-World Topologies on Binary Coded Self-adaptive CEA , 2006, EvoCOP.

[22]  Atsushi Sasaki.  A time-optimal distributed sorting algorithm on a line network , 2002, Inf. Process. Lett..

[23]  Chu Shik Jhon,et al.  Torus Ring: improving performance of interconnection network by modifying hierarchical ring , 2007, Parallel Comput..

[24]  Zdenek Konfrst,et al.  Parallel Genetic Algorithms: Advances, Computing Trends, Applications and Perspectives , 2004, IPDPS.

[25]  Dario Izzo,et al.  Global Optimization of Low-Thrust Trajectories via Impulsive Delta-V Transcription , 2009 .

[26]  Slawomir J. Nasuto,et al.  Search space pruning and global optimisation of multiple gravity assist spacecraft trajectories , 2007, J. Glob. Optim..

[27]  John J. Grefenstette,et al.  A Parallel Genetic Algorithm , 1987, ICGA.

[28]  Per S. Laursen.  Problem-Independent Parallel Simulated Annealing using Selection and Migration , 1994, PPSN.

[29]  Erick Cantú-Paz,et al.  Efficient and Accurate Parallel Genetic Algorithms , 2000, Genetic Algorithms and Evolutionary Computation.

[30]  Dario Izzo,et al.  Parallel global optimisation meta-heuristics using an asynchronous island-model , 2009, 2009 IEEE Congress on Evolutionary Computation.

[31]  Béla Bollobás,et al.  The Diameter of a Scale-Free Random Graph , 2004, Comb..

[32]  Francisco Sandoval Hernández,et al.  Genetic Algorithms on LAN-message Passing Architectures using PVM: Application to the Routing Problem , 1994, PPSN.

[33]  J. Northby.  Structure and binding of Lennard‐Jones clusters: 13≤N≤147 , 1987 .

[34]  Sandro Ridella,et al.  Minimizing multimodal functions of continuous variables with the “simulated annealing” algorithmCorrigenda for this article is available here , 1987, TOMS.

[35]  H. Chatley.  Cohesion , 1921, Nature.

[37]  Reiko Tanese,et al.  Parallel Genetic Algorithms for a Hypercube , 1987, ICGA.

[38]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[39]  J. Doye,et al.  Global Optimization by Basin-Hopping and the Lowest Energy Structures of Lennard-Jones Clusters Containing up to 110 Atoms , 1997, cond-mat/9803344.

[40]  Markus Schwehm,et al.  Parallel Population Models for Genetic Algorithms , 1996 .

[41]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[42]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[43]  L. Darrell Whitley,et al.  Optimization Using Distributed Genetic Algorithms , 1990, PPSN.

[44]  Tamás Vinkó,et al.  Benchmarking different global optimisation techniques for preliminary space trajectory design , 2007 .

[45]  Heinz Mühlenbein,et al.  The parallel genetic algorithm as function optimizer , 1991, Parallel Comput..

[46]  Heinrich Braun,et al.  On Solving Travelling Salesman Problems by Genetic Algorithms , 1990, PPSN.

[47]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[48]  Ron Shonkwiler,et al.  Parallel Genetic Algorithms , 1993, ICGA.

[49]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.