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PARCO (Parallel Computing)

  • Whitley, D., Starkweather, T. and Bogart, C., 1990. Genetic algorithms and neural networks: Optimizing connections and connectivity. Parallel Computing, 14(3), pp.347-361. [ www ]

2021

Ohira, R., Islam, M.S. and Kayesh, H., 2021. Speedup vs. quality: Asynchronous and cluster-based distributed adaptive genetic algorithms for ordered problems. Parallel Computing, 103, p.102755. [ www ]

2020

Hussain, M.M. and Fujimoto, N., 2020. GPU-based parallel multi-objective particle swarm optimization for large swarms and high dimensional problems. Parallel Computing, 92, p.102589. [ www ]

2019

Starzec, M., Starzec, G., Byrski, A. and Turek, W., 2019. Distributed ant colony optimization based on actor model. Parallel Computing, 90, p.102573. [ www ]

2018

González-Álvarez, D.L., Vega-Rodríguez, M.A. and Rubio-Largo, Á., 2018. Searching for common patterns on protein sequences by means of a parallel hybrid honey-bee mating optimization algorithm. Parallel Computing, 76, pp.1-17. [ www ]

2016

Lou, Z. and Reinitz, J., 2016. Parallel simulated annealing using an adaptive resampling interval. Parallel Computing, 53, pp.23-31. [ www ]

2015

Liu, Y.Y. and Wang, S., 2015. A scalable parallel genetic algorithm for the generalized assignment problem. Parallel Computing, 46, pp.98-119. [ www | C ]

2010

Hidalgo, J.I., Fernandez, F., Lanchares, J., Cant-Paz, E. and Zomaya, A., 2010. Parallel architectures and bioinspired algorithms. Parallel Computing, 36 (10-11), pp.553–554. [ www ]

2005

Wang, Z.G., Wong, Y.S. and Rahman, M., 2005. Development of a parallel optimization method based on genetic simulated annealing algorithm. Parallel Computing, 31(8-9), pp.839-857. [ www ]

2004

Cahon, S., Melab, N. and Talbi, E.G., 2004. Building with paradisEO reusable parallel and distributed evolutionary algorithms. Parallel Computing, 30(5-6), pp.677-697. [ www ]

2000

Marco, N. and Lanteri, S., 2000. A two-level parallelization strategy for genetic algorithms applied to optimum shape design. Parallel Computing, 26(4), pp.377-397. [ www ]

1995

Mahfoud, S.W. and Goldberg, D.E., 1995. Parallel recombinative simulated annealing: A genetic algorithm. Parallel Computing, 21(1), pp.1-28. [ www ]

Yong, L., Lishan, K. and Evans, D.J., 1995. The annealing evolution algorithm as function optimizer. Parallel Computing, 21(3), pp.389-400. [ www ]

1991

Mühlenbein, H., Schomisch, M. and Born, J., 1991. The parallel genetic algorithm as function optimizer. Parallel Computing, 17(6-7), pp.619-632. [ www ]

1989

Allwright, J.R. and Carpenter, D.B., 1989. A distributed implementation of simulated annealing for the travelling salesman problem. Parallel Computing, 10(3), pp.335-338. [ www ]

1988

Mühlenbein, H., Gorges-Schleuter, M. and Krämer, O., 1988. Evolution algorithms in combinatorial optimization. Parallel Computing, 7(1), pp.65-85. [ www ]

1987

Mühlenbein, H., Gorges-Schleuter, M. and Krämer, O., 1987. New solutions to the mapping problem of parallel systems: The evolution approach. Parallel Computing, 4(3), pp.269-279. [ www ]


2020

Gong, D., Tian, T., Wang, J., Du, Y. and Li, Z., 2020. A novel method of grouping target paths for parallel programs. Parallel Computing, 97, p.102665. [ www ]

2019

Durillo, J.J., Gschwandtner, P., Kofler, K. and Fahringer, T., 2019. Multi-objective region-aware optimization of parallel programs. Parallel Computing, 83, pp.3-21. [ www ]

2017

Verma, A. and Kaushal, S., 2017. A hybrid multi-objective particle swarm optimization for scientific workflow scheduling. Parallel Computing, 62, pp.1-19. [ www ]

2015

De Falco, I., Scafuri, U. and Tarantino, E., 2015. Mapping of time-consuming multitask applications on a cloud system by multiobjective differential evolution. Parallel Computing, 48, pp.40-58. [ www ]