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

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.

[1]  Changhe Li,et al.  A survey of swarm intelligence for dynamic optimization: Algorithms and applications , 2017, Swarm Evol. Comput..

[2]  Amir Hossein Gandomi,et al.  Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems , 2011, Engineering with Computers.

[3]  Ross Gore,et al.  Applying causal inference to understand emergent behavior , 2008, 2008 Winter Simulation Conference.

[4]  Zheng Li,et al.  PS-ABC: A hybrid algorithm based on particle swarm and artificial bee colony for high-dimensional optimization problems , 2015, Expert Syst. Appl..

[5]  Michael Wright,et al.  A largest empty hypersphere metaheuristic for robust optimisation with implementation uncertainty , 2018, Comput. Oper. Res..

[6]  Nathan Mendes,et al.  Predictive controllers for thermal comfort optimization and energy savings , 2008 .

[7]  Anas A. Hadi,et al.  Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm , 2019, International Journal of Machine Learning and Cybernetics.

[8]  Ali Ghoddosian,et al.  Color harmony algorithm: an art-inspired metaheuristic for mathematical function optimization , 2020, Soft Comput..

[9]  Andrew Lewis,et al.  The Whale Optimization Algorithm , 2016, Adv. Eng. Softw..

[10]  Seyyed Meisam Taheri,et al.  A generalization of the Wilcoxon signed-rank test and its applications , 2013 .

[11]  Wentian Li On parameters of the human genome. , 2011, Journal of theoretical biology.

[12]  Xin-She Yang,et al.  A literature survey of benchmark functions for global optimisation problems , 2013, Int. J. Math. Model. Numer. Optimisation.

[13]  L. Andrew Bollinger,et al.  Facilitating Model Reuse and Integration in an Urban Energy Simulation Platform , 2015, ICCS.

[14]  Yang Yang,et al.  Developing a Flexible Simulation-Optimization Framework to Facilitate Sustainable Urban Drainage Systems Designs Through Software Reuse , 2019, ICSR.

[15]  Richard M. Fujimoto,et al.  Cloning parallel simulations , 2001, TOMC.

[16]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

[17]  Angus R. Simpson,et al.  A self-adaptive boundary search genetic algorithm and its application to water distribution systems , 2002 .

[18]  Ioan Cristian Trelea,et al.  The particle swarm optimization algorithm: convergence analysis and parameter selection , 2003, Inf. Process. Lett..

[19]  Joaquín Izquierdo,et al.  Joint Operation of Pressure-Reducing Valves and Pumps for Improving the Efficiency of Water Distribution Systems , 2018, Journal of Water Resources Planning and Management.

[20]  Y. Toparlar,et al.  Aerodynamic drag in cycling pelotons: New insights by CFD simulation and wind tunnel testing , 2018, Journal of Wind Engineering and Industrial Aerodynamics.

[21]  Amitava Chatterjee,et al.  Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization , 2006, Comput. Oper. Res..

[22]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[23]  Abhay Singh,et al.  Comparative Study of Krill Herd, Firefly and Cuckoo Search Algorithms for Unimodal and Multimodal Optimization , 2014 .

[24]  Zhiwen Yu,et al.  Orthogonal learning particle swarm optimization with variable relocation for dynamic optimization , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[25]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[26]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[27]  Ross Gore,et al.  Explanation Exploration: Exploring Emergent Behavior , 2007, 21st International Workshop on Principles of Advanced and Distributed Simulation (PADS'07).

[28]  Richard M. Fujimoto,et al.  Cloning: a novel method for interactive parallel simulation , 1997, WSC '97.

[29]  Marco Dorigo,et al.  Ant colony optimization theory: A survey , 2005, Theor. Comput. Sci..