A Largest Empty Hypersphere Metaheuristic for Robust Optimisation with Implementation Uncertainty

We consider box-constrained robust optimisation problems with implementation uncertainty. In this setting, the solution that a decision maker wants to implement may become perturbed. The aim is to find a solution that optimises the worst possible performance over all possible perturbances. Previously, only few generic search methods have been developed for this setting. We introduce a new approach for a global search, based on placing a largest empty hypersphere. We do not assume any knowledge of the structure of the original objective function, making this approach also viable for simulation-optimisation settings. In computational experiments we demonstrate a strong performance of our approach in comparison with state-of-the-art methods, which makes it possible to solve even high-dimensional problems

Grand Tour Algorithm: Novel Swarm-Based Optimization for High-Dimensional Problems

[EN] Agent-based algorithms, based on the collective behavior of natural social groups, exploit innate swarm intelligence to produce metaheuristic methodologies to explore optimal solutions for diverse processes in systems engineering and other sciences. Especially for complex problems, the processing time, and the chance to achieve a local optimal solution, are drawbacks of these algorithms, and to date, none has proved its superiority. In this paper, an improved swarm optimization technique, named Grand Tour Algorithm (GTA), based on the behavior of a peloton of cyclists, which embodies relevant physical concepts, is introduced and applied to fourteen benchmarking optimization problems to evaluate its performance in comparison to four other popular classical optimization metaheuristic algorithms. These problems are tackled initially, for comparison purposes, with 1000 variables. Then, they are confronted with up to 20,000 variables, a really large number, inspired in the human genome. The obtained results show that GTA clearly outperforms the other algorithms. To strengthen GTA's value, various sensitivity analyses are performed to verify the minimal influence of the initial parameters on efficiency. It is demonstrated that the GTA fulfils the fundamental requirements of an optimization algorithm such as ease of implementation, speed of convergence, and reliability. Since optimization permeates modeling and simulation, we finally propose that GTA will be appealing for the agent-based community, and of great help for a wide variety of agent-based applications.Meirelles, G.; Brentan, B.; Izquierdo Sebastián, J.; Luvizotto, EJ. (2020). Grand Tour Algorithm: Novel Swarm-Based Optimization for High-Dimensional Problems. Processes. 8(8):1-19. https://doi.org/10.3390/pr8080980S11988Mohamed, A. W., Hadi, A. A., & Mohamed, A. K. (2019). Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm. International Journal of Machine Learning and Cybernetics, 11(7), 1501-1529. doi:10.1007/s13042-019-01053-xMirjalili, S., & Lewis, A. (2016). The Whale Optimization Algorithm. Advances in Engineering Software, 95, 51-67. doi:10.1016/j.advengsoft.2016.01.008Chatterjee, A., & Siarry, P. (2006). Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization. Computers & Operations Research, 33(3), 859-871. doi:10.1016/j.cor.2004.08.012Dorigo, M., & Blum, C. (2005). Ant colony optimization theory: A survey. Theoretical Computer Science, 344(2-3), 243-278. doi:10.1016/j.tcs.2005.05.020Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3), 459-471. doi:10.1007/s10898-007-9149-xGandomi, A. H., Yang, X.-S., & Alavi, A. H. (2011). Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Engineering with Computers, 29(1), 17-35. doi:10.1007/s00366-011-0241-yKirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680. doi:10.1126/science.220.4598.671Wu, Z. Y., & Simpson, A. R. (2002). A self-adaptive boundary search genetic algorithm and its application to water distribution systems. Journal of Hydraulic Research, 40(2), 191-203. doi:10.1080/00221680209499862Trelea, I. C. (2003). The particle swarm optimization algorithm: convergence analysis and parameter selection. Information Processing Letters, 85(6), 317-325. doi:10.1016/s0020-0190(02)00447-7Brentan, B., Meirelles, G., Luvizotto, E., & Izquierdo, J. (2018). Joint Operation of Pressure-Reducing Valves and Pumps for Improving the Efficiency of Water Distribution Systems. Journal of Water Resources Planning and Management, 144(9), 04018055. doi:10.1061/(asce)wr.1943-5452.0000974Freire, R. Z., Oliveira, G. H. C., & Mendes, N. (2008). Predictive controllers for thermal comfort optimization and energy savings. Energy and Buildings, 40(7), 1353-1365. doi:10.1016/j.enbuild.2007.12.007Bollinger, L. A., & Evins, R. (2015). Facilitating Model Reuse and Integration in an Urban Energy Simulation Platform. Procedia Computer Science, 51, 2127-2136. doi:10.1016/j.procs.2015.05.484Yang, Y., & Chui, T. F. M. (2019). Developing a Flexible Simulation-Optimization Framework to Facilitate Sustainable Urban Drainage Systems Designs Through Software Reuse. Reuse in the Big Data Era, 94-99. doi:10.1007/978-3-030-22888-0_7Mavrovouniotis, M., Li, C., & Yang, S. (2017). A survey of swarm intelligence for dynamic optimization: Algorithms and applications. Swarm and Evolutionary Computation, 33, 1-17. doi:10.1016/j.swevo.2016.12.005Hybinette, M., & Fujimoto, R. M. (2001). Cloning parallel simulations. ACM Transactions on Modeling and Computer Simulation, 11(4), 378-407. doi:10.1145/508366.508370Proceedings of the 2004 Winter Simulation Conference (IEEE Cat. No.04CH37614C). (2004). Proceedings of the 2004 Winter Simulation Conference, 2004. doi:10.1109/wsc.2004.1371294Li, Z., Wang, W., Yan, Y., & Li, Z. (2015). PS–ABC: A hybrid algorithm based on particle swarm and artificial bee colony for high-dimensional optimization problems. Expert Systems with Applications, 42(22), 8881-8895. doi:10.1016/j.eswa.2015.07.043Montalvo, I., Izquierdo, J., Pérez-García, R., & Herrera, M. (2014). Water Distribution System Computer-Aided Design by Agent Swarm Optimization. Computer-Aided Civil and Infrastructure Engineering, 29(6), 433-448. doi:10.1111/mice.12062Heuristic Optimization. (s. f.). Advances in Computational Management Science, 38-76. doi:10.1007/0-387-25853-1_2Zong Woo Geem, Joong Hoon Kim, & Loganathan, G. V. (2001). A New Heuristic Optimization Algorithm: Harmony Search. SIMULATION, 76(2), 60-68. doi:10.1177/003754970107600201Blocken, B., van Druenen, T., Toparlar, Y., Malizia, F., Mannion, P., Andrianne, T., … Diepens, J. (2018). Aerodynamic drag in cycling pelotons: New insights by CFD simulation and wind tunnel testing. Journal of Wind Engineering and Industrial Aerodynamics, 179, 319-337. doi:10.1016/j.jweia.2018.06.011Clerc, M., & Kennedy, J. (2002). The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 6(1), 58-73. doi:10.1109/4235.985692GAMS World, GLOBAL Libraryhttp://www.gamsworld.org/global/globallib.htmlCUTEr, A Constrained and Un-Constrained Testing Environment, Revisitedhttp://cuter.rl.ac.uk/cuter-www/problems.htmlGO Test Problemshttp://www-optima.amp.i.kyoto-u.ac.jp/member/student/hedar/Hedar_files/TestGO.htmJamil, M., & Yang, X. S. (2013). A literature survey of benchmark functions for global optimisation problems. International Journal of Mathematical Modelling and Numerical Optimisation, 4(2), 150. doi:10.1504/ijmmno.2013.055204Sharma, G. (2012). The Human Genome Project and its promise. Journal of Indian College of Cardiology, 2(1), 1-3. doi:10.1016/s1561-8811(12)80002-2Li, W. (2011). On parameters of the human genome. Journal of Theoretical Biology, 288, 92-104. doi:10.1016/j.jtbi.2011.07.021Hughes, M., Goerigk, M., & Wright, M. (2019). A largest empty hypersphere metaheuristic for robust optimisation with implementation uncertainty. Computers & Operations Research, 103, 64-80. doi:10.1016/j.cor.2018.10.013Zaeimi, M., & Ghoddosian, A. (2020). Color harmony algorithm: an art-inspired metaheuristic for mathematical function optimization. Soft Computing, 24(16), 12027-12066. doi:10.1007/s00500-019-04646-4Singh, G. P., & Singh, A. (2014). Comparative Study of Krill Herd, Firefly and Cuckoo Search Algorithms for Unimodal and Multimodal Optimization. International Journal of Intelligent Systems and Applications in Engineering, 2(3), 26. doi:10.18201/ijisae.31981Taheri, S. M., & Hesamian, G. (2012). A generalization of the Wilcoxon signed-rank test and its applications. Statistical Papers, 54(2), 457-470. doi:10.1007/s00362-012-0443-

Considering stakeholders’ preferences for scheduling slots in capacity constrained airports

Airport slot scheduling has attracted the attention of researchers as a capacity management tool at congested airports. Recent research work has employed multi-objective approaches for scheduling slots at coordinated airports. However, the central question on how to select a commonly accepted airport schedule remains. The various participating stakeholders may have multiple and sometimes conflicting objectives stemming from their decision-making needs. This complex decision environment renders the identification of a commonly accepted solution rather difficult. In this presentation, we propose a multi-criteria decision-making technique that incorporates the priorities and preferences of the stakeholders in order to determine the best compromise solution