![](bg8.png)
Hinrichs, C., Lehnhoff, S., and Sonnenschein, M. (2014).
A Decentralized Heuristic for Multiple-Choice Com-
binatorial Optimization Problems. In Operations Re-
search Proceedings 2012, pages 297–302. Springer.
Hinrichs, C., Sonnenschein, M., and Lehnhoff, S. (2013b).
Evaluation of a Self-Organizing Heuristic for Interde-
pendent Distributed Search Spaces. In Filipe, J. and
Fred, A. L. N., editors, International Conference on
Agents and Artificial Intelligence (ICAART 2013), vo-
lume Volume 1 – Agents, pages 25–34. SciTePress.
Horst, R. and Pardalos, P. M., editors (1995). Handbook of
Global Optimization. Kluwer Academic Publishers,
Dordrecht, Netherlands.
Karaboga, D. and Basturk, B. (2007). A powerful and ef-
ficient algorithm for numerical function optimization:
artificial bee colony (ABC) algorithm. Journal of Glo-
bal Optimization, 39(3):459–471.
Knight, J. N. and Lunacek, M. (2007). Reducing the space-
time complexity of the CMA-ES. In Genetic and Evo-
lutionary Computation Conference, pages 658–665.
Li, G. and Wang, Q. (2014). A cooperative harmony se-
arch algorithm for function optimization. Mathemati-
cal Problems in Engineering, 2014.
Li, Y., Mascagni, M., and Gorin, A. (2009). A decentrali-
zed parallel implementation for parallel tempering al-
gorithm. Parallel Computing, 35(5):269–283.
Liang, J., Qu, B., Suganthan, P., and Hern
´
andez-D
´
ıaz, A. G.
(2013). Problem definitions and evaluation criteria for
the cec 2013 special session on real-parameter optimi-
zation. Technical Report 201212, Computational In-
telligence Laboratory, Zhengzhou University, Zheng-
zhou, China and Nanyang Technological University,
Singapore, Technical Report.
Loshchilov, I., Schoenauer, M., and Sebag, M. (2012). Self-
adaptive surrogate-assisted covariance matrix adapta-
tion evolution strategy. CoRR, abs/1204.2356.
Luo, Z. Q. and Tseng, P. (1992). On the convergence of
the coordinate descent method for convex differentia-
ble minimization. Journal of Optimization Theory and
Applications, 72(1):7–35.
Lust, T. and Teghem, J. (2010). The multiobjective multi-
dimensional knapsack problem: a survey and a new
approach. CoRR, abs/1007.4063.
Mishra, S. K. (2006). Some new test functions for glo-
bal optimization and performance of repulsive parti-
cle swarm method. Technical Report SSRN 926132,
North-Eastern Hill University, Shillong (India).
Nieße, A. (2015). Verteilte kontinuierliche Einsatzplanung
in Dynamischen Virtuellen Kraftwerken. PhD thesis.
Nieße, A., Beer, S., Bremer, J., Hinrichs, C., L
¨
unsdorf,
O., and Sonnenschein, M. (2014). Conjoint dynamic
aggrgation and scheduling for dynamic virtual power
plants. In Ganzha, M., Maciaszek, L. A., and Papr-
zycki, M., editors, Federated Conference on Compu-
ter Science and Information Systems - FedCSIS 2014,
Warsaw, Poland.
Nieße, A. and Sonnenschein, M. (2013). Using grid related
cluster schedule resemblance for energy rescheduling
- goals and concepts for rescheduling of clusters in
decentralized energy systems. In Donnellan, B., Mar-
tins, J. F., Helfert, M., and Krempels, K.-H., editors,
SMARTGREENS, pages 22–31. SciTePress.
Ortega, J. M. and Rheinboldt, W. C. (1970). Iterative solu-
tion of nonlinear equations in several variables.
Picard, G. and Glize, P. (2006). Model and analysis of
local decision based on cooperative self-organization
for problem solving. Multiagent Grid Syst., 2(3):253–
265.
Poli, R., Kennedy, J., and Blackwell, T. (2007). Particle
swarm optimization. Swarm Intelligence, 1(1):33–57.
Rahnamayan, S., Tizhoosh, H. R., and Salama, M. M.
(2007). A novel population initialization method for
accelerating evolutionary algorithms. Computers &
Mathematics with Applications, 53(10):1605 – 1614.
Rigling, B. D. and Moore, F. W. (1999). Exploitation of
sub-populations in evolution strategies for improved
numerical optimization. Ann Arbor, 1001:48105.
Salomon, R. (1996). Re-evaluating genetic algorithm per-
formance under coordinate rotation of benchmark
functions. a survey of some theoretical and practical
aspects of genetic algorithms. Biosystems, 39(3):263
– 278.
Simon, D. (2013). Evolutionary Optimization Algorithms.
Wiley.
Suganthan, P. N., Hansen, N., Liang, J. J., Deb, K., Chen,
Y. P., Auger, A., and Tiwari, S. (2005). Problem Defi-
nitions and Evaluation Criteria for the CEC 2005 Spe-
cial Session on Real-Parameter Optimization. Techni-
cal report, Nanyang Technological University, Singa-
pore.
Talbi, E. (2009). Metaheuristics: From Design to Imple-
mentation. Wiley Series on Parallel and Distributed
Computing. Wiley.
Tseng, P. (2001). Convergence of a block coordinate
descent method for nondifferentiable minimization.
Journal of Optimization Theory and Applications,
109(3):475–494.
Ulmer, H., Streichert, F., and Zell, A. (2003). Evolu-
tion strategies assisted by gaussian processes with im-
proved pre-selection criterion. In in IEEE Congress
on Evolutionary Computation,CEC 2003, pages 692–
699.
Vanneschi, L., Codecasa, D., and Mauri, G. (2011). A com-
parative study of four parallel and distributed pso met-
hods. New Generation Computing, 29(2):129–161.
Watts, D. and Strogatz, S. (1998). Collective dynamics of
’small-world’ networks. Nature, (393):440–442.
Wright, S. J. (2015). Coordinate descent algorithms. Mat-
hematical Programming, 151(1):3–34.
Yao, X., Liu, Y., and Lin, G. (1999). Evolutionary program-
ming made faster. IEEE Trans. Evolutionary Compu-
tation, 3(2):82–102.
ICAART 2017 - 9th International Conference on Agents and Artificial Intelligence
136