A bi-objective load balancing model in a distributed simulation system using NSGA-II and MOPSO approaches

S. I. S. Committee, Others, of the IEEE Computer Society, 2000

Lwin, 2014, A learning-guided multi-objective evolutionary algorithm for constrained portfolio optimization, Appl. Soft Comput., 24, 757, 10.1016/j.asoc.2014.08.026

Boginski, 2011, vol. 61

Ding, 2015, Status and progress in deployment optimization of firepower units, Kongzhi Lilun Yu Yingyong/Control Theory Appl., 32, 1569

Cai, 2005, Federate migration in HLA-based simulation, Future Gener. Comput. Syst., 21, 87, 10.1016/j.future.2004.09.019

Tan, 2005, HLA federate migration, 243

Eklöf, 2004, Peer-to-peer-based resource management in support of HLA-based distributed simulations, Simulation, 80, 181, 10.1177/0037549704045050

Wu, 1994, Heuristic algorithms for task assignment and scheduling in a processor network, Parallel Comput., 20, 1, 10.1016/0167-8191(94)90109-0

Martino, 2002, Scheduling in a grid computing environment using genetic algorithms, Int. Parallel Distrib. Process. Symp., 235

Wei, 2006, Interaction priority algorithm based entities static scheduling strategy for simulation over the grids, J. Chin. Comput. Syst., 1, 23

Zhao, 2009

Pan, 2010, The research of static task scheduling strategy for distributed read-time simulation system(DRTSS), Comput. Simul., 27, 102

Ban, 2013, A two-stage allocation optimization in static load balancing of HLA distributed simulation, 56

Yue, 2013, Novel models and algorithms of load balancing for variable-structured collaborative simulation under HLA/RTI, Chin. J. Mech. Eng., 26, 629, 10.3901/CJME.2013.04.629

Deb, 2002, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput., 6, 182, 10.1109/4235.996017

Coello, 2004, Handling multiple objectives with particle swarm optimization, IEEE Trans. Evol. Comput., 8, 256, 10.1109/TEVC.2004.826067

Hu, 2015, Adaptive multiobjective particle swarm optimization based on parallel cell coordinate system, IEEE Trans. Evol. Comput., 19, 1, 10.1109/TEVC.2013.2296151

Fattahi, 2015, A bi-objective continuous review inventory control model: Pareto-based meta-heuristic algorithms, Appl. Soft Comput., 32, 211, 10.1016/j.asoc.2015.02.044

Li, 2015, Efficiently solving multi-objective dynamic weapon-target assignment problems by NSGA-II, Proc. 34th Chinese Control Conf. (2015 CCC), 2556

Li, 2015, Solving multi-objective multi-stage weapon target assignment problem via adaptive NSGA-II and adaptive MOEA/D: a comparison study, 3132

Mousavi, 2016, A bi-objective inventory optimization model under inflation and discount using tuned Pareto-based algorithms: NSGA-II, NRGA, and MOPSO, Appl. Soft Comput., 43, 57, 10.1016/j.asoc.2016.02.014

Afzalirad, 2017, A realistic variant of bi-objective unrelated parallel machine scheduling problem: NSGA-II and MOACO approaches, Appl. Soft Comput., 50, 109, 10.1016/j.asoc.2016.10.039

Salimi, 2014, Task scheduling using NSGA II with fuzzy adaptive operators for computational grids, J. Parallel Distrib. Comput., 74, 2333, 10.1016/j.jpdc.2014.01.006

Subtil, 2010, Using an enhanced integer NSGA-II for solving the multiobjective generalized assignment problem, 1

Dahmani, 2016, Solving a load balancing problem with a multi-objective particle swarm optimisation approach: application to aircraft cargo transportation, Int. J. Oper. Res., 27, 62, 10.1504/IJOR.2016.078455

Ramezani, 2017

Alkayal, 2016, Efficient task scheduling multi-objective particle swarm optimization in cloud computing, 17

Zhu, 2016, Multi-objective ant colony optimization algorithm based on load balance, 193

Raquel, 2005, An effective use of crowding distance in multiobjective particle swarm optimization, 257

Li, 2016, A novel ranking-based optimal guides selection strategy in MOPSO, Procedia Comput. Sci., 91, 1001, 10.1016/j.procs.2016.07.135

Zhan, 2009, Adaptive particle swarm optimization, IEEE Trans. Syst. Man Cybern. Part B, 39, 1362, 10.1109/TSMCB.2009.2015956

Zhou, 2002, 55

De Grande, 2010, Distributed dynamic balancing of communication load for large-scale HLA-based simulations, 1109

Rahmawan, 2009, The simulation of static load balancing algorithms, 640

Banino, 2004, Scheduling strategies for master-slave tasking on heterogeneous processor platforms, IEEE Trans. Parallel Distrib. Syst., 15, 319, 10.1109/TPDS.2004.1271181

Kennedy, 1995, Particle swarm optimization, 1942

Reyes-Sierra, 2006, Multi-objective particle swarm optimizers: a survey of the state-of-the-art, Int. J. Comput. Intell. Res., 2, 287

Bentley, 1998, Finding acceptable solutions in the Pareto-optimal range using multiobjective genetic algorithms, 231

Garza-Fabre, 2009, Ranking methods for many-objective optimization, 633

Moslemi, 2011, Comparisons of some improving strategies on MOPSO for multi-objective (r, Q) inventory system, Expert Syst. Appl., 38, 12051, 10.1016/j.eswa.2011.01.169

Ding, 2014, An improved particle swarm optimization deployment for wireless sensor networks, J. Adv. Comput. Intell. Intell. Inform., 18, 107, 10.20965/jaciii.2014.p0107

Zitzler, 1998, Multiobjective optimization using evolutionary algorithms – a comparative case study, 292

Schott, 1995

Zhang, 2007, MOEA/D: a multiobjective evolutionary algorithm based on decomposition, IEEE Trans. Evol. Comput., 11, 712, 10.1109/TEVC.2007.892759

Yen, 2014, Performance metric ensemble for multiobjective evolutionary algorithms, IEEE Trans. Evol. Comput., 18, 131, 10.1109/TEVC.2013.2240687

Taguchi, 1986

Rahmati, 2013, A soft-computing Pareto-based meta-heuristic algorithm for a multi-objective multi-server facility location problem, Appl. Soft Comput., 13, 1728, 10.1016/j.asoc.2012.12.016

Chen, 2017, DMOEA-εC: decomposition-based multi-objective evolutionary algorithm with the ε-constraint framework, IEEE Trans. Evol. Comput., 21, 714, 10.1109/TEVC.2017.2671462

Wang, 2013, Particle swarm optimization with adaptive mutation for multimodal optimization, Appl. Math. Comput., 221, 296

Pei, 2017, Single-machine and parallel-machine serial-batching scheduling problems with position-based learning effect and linear setup time, Ann. Oper. Res., 1

Wang, 2015, A human learning optimization algorithm and its application to multi-dimensional knapsack problems, Appl. Soft Comput., 34, 736, 10.1016/j.asoc.2015.06.004

Wang, 2017, A diverse human learning optimization algorithm, J. Glob. Optim., 67, 283, 10.1007/s10898-016-0444-2

Wang, 2017, A hybrid-coded human learning optimization for mixed-variable optimization problems, Knowl. Based Syst., 127, 114, 10.1016/j.knosys.2017.04.015

Li, 2016, Solving the uncertain multi-objective multi-stage weapon target assignment problem via MOEA/D-AWA, 4934