A state-of-the-art review of population-based parallel meta-heuristics

Mathematical models of many real life optimization problems turn out to be so complex that traditional optimization techniques such as gradient based methods and other deterministic techniques etc are not applicable for them. A new class of optimization techniques called population-based meta-heuristics (PBM) is applied for the solution of these problems. Particle Swarm Optimization algorithm (PSO) and Genetic Algorithm (GA) are two of the most popular algorithms in this category and have been extensively used in recent years for the solution of this kind of optimization problems. But for these techniques, computational cost (measured by elapsed time) is too high. Fortunately, these techniques have inherent parallelism in them. This inspires many researchers to implement them on the latest parallel computers. This review paper tries to present a state-of-the-art in parallel genetic algorithms and parallel particle swarm optimization.

[1]  Bu-Sung Lee,et al.  Efficient Hierarchical Parallel Genetic Algorithms using Grid computing , 2007, Future Gener. Comput. Syst..

[2]  Enrique Alba,et al.  Improving flexibility and efficiency by adding parallelism to genetic algorithms , 2002, Stat. Comput..

[3]  Tarek A. El-Ghazawi,et al.  2-phase GA-based image registration on parallel clusters , 2001, Future generations computer systems.

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

[5]  Kusum Deep,et al.  Mean particle swarm optimisation for function optimisation , 2009, Int. J. Comput. Intell. Stud..

[6]  Hartmut Schmeck,et al.  Experiences with fine‐grainedparallel genetic algorithms , 1999, Ann. Oper. Res..

[7]  Kusum Deep,et al.  Hybridization of particle swarm optimization with quadratic approximation , 2009 .

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

[9]  World Congress on Nature & Biologically Inspired Computing, NaBIC 2009, 9-11 December 2009, Coimbatore, India , 2009, NaBIC.

[10]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[11]  B J Fregly,et al.  Parallel global optimization with the particle swarm algorithm , 2004, International journal for numerical methods in engineering.

[12]  M G Pandy,et al.  Application of high-performance computing to numerical simulation of human movement. , 1995, Journal of biomechanical engineering.

[13]  Bernard Manderick,et al.  Fine-Grained Parallel Genetic Algorithms , 1989, ICGA.

[14]  Katya Rodríguez-Vázquez,et al.  Parallel particle swarm optimization applied to the protein folding problem , 2009, GECCO.

[15]  Paul Bryant Grosso,et al.  Computer Simulations of Genetic Adaptation: Parallel Subcomponent Interaction in a Multilocus Model , 1985 .

[16]  E.-G. Talbi,et al.  Hill-climbing, simulated annealing and genetic algorithms: a comparative study and application to the mapping problem , 1993, [1993] Proceedings of the Twenty-sixth Hawaii International Conference on System Sciences.

[17]  Erick Cantú-Paz,et al.  Topologies, Migration Rates, and Multi-Population Parallel Genetic Algorithms , 1999, GECCO.

[18]  Marcel Waintraub,et al.  Multiprocessor modeling of parallel Particle Swarm Optimization applied to nuclear engineering problems , 2009 .

[19]  Kusum Deep,et al.  Information sharing strategy among particles in Particle Swarm Optimization using Laplacian operator , 2009, 2009 IEEE Swarm Intelligence Symposium.

[20]  David Abramson,et al.  A PARALLEL GENETIC ALGORITHM FOR SOLVING THE SCHOOL TIMETABLING PROBLEM , 1992 .

[21]  Mohamed Barkaoui,et al.  A parallel hybrid genetic algorithm for the vehicle routing problem with time windows , 2004, Comput. Oper. Res..

[22]  Ziya Arnavut,et al.  Fault diagnosis for airplane engines using Bayesian networks and distributed particle swarm optimization , 2007, Parallel Comput..

[23]  Hwa-Seok Lee,et al.  PC Cluster based Parallel PSO Algorithm for Optimal Power Flow , 2007, 2007 International Conference on Intelligent Systems Applications to Power Systems.

[24]  Hesham Ahmed Hefny,et al.  Chaotic particle swarm optimization , 2010, 2010 The 7th International Conference on Informatics and Systems (INFOS).