Performance analysis of synchronous and asynchronous distributed genetic algorithms on multiprocessors

Swarm and Evolutionary Computation - Tập 49 - Trang 147-157 - 2019
Amr Abdelhafez1,2, Enrique Alba1, Gabriel Luque1
1Dpto. de Lenguajes y Ciencias de la Computación, Univ. de Málaga, E.T.S. Ingeniería Informática, Campus de Teatinos, 29071 Málaga, Spain
2Faculty of Science, Assiut University, 71515 Assiut, Egypt

Tài liệu tham khảo

Abdelhafez, 2017, Speed-up of synchronous and asynchronous distributed Genetic Algorithms: a first common approach on multiprocessors, 2677 Abdelhafez, 2019, A component-based study of energy consumption for sequential and parallel genetic algorithms, J. Supercomput., 1 Alba, 2002, Parallel evolutionary algorithms can achieve super-linear performance, Inf. Process. Lett., 82, 7, 10.1016/S0020-0190(01)00281-2 Alba, 2005 Alba, 2004, Advances in parallel heterogeneous genetic algorithms for continuous optimization, Int. J. Appl. Math. Comput. Sci., 14, 317 Alba, 2004, Parallel heterogeneous genetic algorithms for continuous optimization, Parallel Comput., 30, 699, 10.1016/j.parco.2003.12.011 Alba, 1999, A survey of parallel distributed genetic algorithms, Complexity, 4, 31, 10.1002/(SICI)1099-0526(199903/04)4:4<31::AID-CPLX5>3.0.CO;2-4 Alba, 2000, Influence of the migration policy in parallel distributed GAs with structured and panmictic populations, Appl. Intell., 12, 163, 10.1023/A:1008358805991 Alba, 2001, Analyzing synchronous and asynchronous parallel distributed genetic algorithms, Future Gener. Comput. Syst., 17, 451, 10.1016/S0167-739X(99)00129-6 Alba, 2002, Improving flexibility and efficiency by adding parallelism to genetic algorithms, Stat. Comput., 12, 91, 10.1023/A:1014803900897 Cantu-Paz, 2000 Cantu-Paz, 2000, Efficient parallel genetic algorithms: theory and practice, Comput. Methods Appl. Mech. Eng., 186, 221, 10.1016/S0045-7825(99)00385-0 Chen, 2012, A parallel ant colony algorithm on massively parallel processors and its convergence analysis for the travelling salesman problem, Inf. Sci., 199, 31, 10.1016/j.ins.2012.02.055 Cutillas-Lozano, 2014, Optimizing shared-memory hyperheuristics on top of parameterized metaheuristics, Procedia Comput. Sci., 29, 20, 10.1016/j.procs.2014.05.002 Del Ser, 2019, Bio-inspired computation: where we stand and what's next, Swarm Evol. Comput., 48, 220, 10.1016/j.swevo.2019.04.008 Dubreuil, 2006, Analysis of a master-slave architecture for distributed evolutionary computations, IEEE Trans. Syst., Man Cybern., Part B (Cybernetics), 36, 229, 10.1109/TSMCB.2005.856724 George, 2003 Gonalves-E-Silva, 2018, Parallel synchronous and asynchronous coupled simulated annealing, J. Supercomput., 74, 2841, 10.1007/s11227-018-2327-4 Gong, 2015, Distributed evolutionary algorithms and their models: a survey of the state-of-the-art, Appl. Soft Comput., 34, 286, 10.1016/j.asoc.2015.04.061 Harada, 2017, Performance comparison of parallel asynchronous multi-objective evolutionary algorithm with different asynchrony, 1215 Hedar, 2012, 295 Hughes, 2003 Jong, 1997, Using problem generators to explore the effects of epistasis, 338 Kim, 2005, 541 Kim, 2019, A comparison study of harmony search and genetic algorithm for the max-cut problem, Swarm Evol. Comput., 44, 130, 10.1016/j.swevo.2018.01.004 Koh, 2006, Parallel asynchronous particle swarm optimization, Int. J. Numer. Methods Eng., 67, 578, 10.1002/nme.1646 Liu, 2015, A scalable parallel genetic algorithm for the Generalized Assignment Problem, Parallel Comput., 46, 98, 10.1016/j.parco.2014.04.008 Luque, 2011 Mabrouk, 2009, On a parallel genetictabu search based algorithm for solving the graph colouring problem, Eur. J. Oper. Res., 197, 1192, 10.1016/j.ejor.2008.03.050 MacWilliams, 1977 Mcmahon, 2000, A distributed genetic algorithm with migration for the design of composite laminate structures, Parallel Algorithm Appl., 14, 329, 10.1080/10637199808947394 Molina, 2010, Memetic algorithms based on local search chains for large scale continuous optimisation problems: MA-SSW-Chains, Soft Computing, 15, 2201, 10.1007/s00500-010-0647-2 Mostaghim, 2008, Parallel multi-objective optimization using Master-Slave model on heterogeneous resources, 1981 Pedemonte, 2018, A theoretical and empirical study of the trajectories of solutions on the grid of systolic genetic search, Inf. Sci., 445446, 97, 10.1016/j.ins.2018.02.033 Qian, 2016, Parallel pareto optimization for subset selection, 1939 Rada-Vilela, 2013, A performance study on synchronicity and neighborhood size in particle swarm optimization, Soft Comput., 17, 1019, 10.1007/s00500-013-1015-9 Ruciski, 2010, On the impact of the migration topology on the Island Model, Parallel Comput., 36, 555, 10.1016/j.parco.2010.04.002 Santander-Jimenez, 2017, Asynchronous non-generational model to parallelize metaheuristics: a bioinformatics case study, IEEE Trans. Parallel Distrib. Syst., 28, 1825, 10.1109/TPDS.2016.2645764 Schaffer, 1991, On crossover as an evolutionary viable strategy, 61 Serani, 2016, Parameter selection in synchronous and asynchronous deterministic particle swarm optimization for ship hydrodynamics problems, Appl. Soft Comput., 49, 313, 10.1016/j.asoc.2016.08.028 Stinson, 1985 Talbi, 2009 Tang, 2016, Negatively correlated search, IEEE J. Sel. Area. Commun., 34, 1, 10.1109/JSAC.2016.2525458 Venter, 2006, Parallel particle swarm optimization algorithm accelerated by asynchronous evaluations, J. Aerosp. Comput. Inf. Commun., 3, 123, 10.2514/1.17873 Xu, 2018, Theoretical results of QoS-guaranteed resource scaling for cloud-based MapReduce, IEEE Trans. Cloud Comput., 6, 879, 10.1109/TCC.2016.2535277 Yang, 2018, Turning high-dimensional optimization into computationally expensive optimization, IEEE Trans. Evol. Comput., 22, 143, 10.1109/TEVC.2017.2672689 Zhan, 2017, Cloudde: a heterogeneous differential evolution algorithm and its distributed cloud version, IEEE Trans. Parallel Distrib. Syst., 28, 704, 10.1109/TPDS.2016.2597826