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

Processes

Gustavo Meirelles¹  Bruno Brentan¹  Edevar Luvizotto²  Joaquín Izquierdo³ 

[1] Department of Hydraulic Engineering and Water Resources-ERH, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil;Department of Water Resources-DRH, Universidade Estadual de Campinas, Campinas 13083-889, Brazil;Fluing-Institute for Multidisciplinary Mathematics, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain;

关键词: optimization;    swarm optimization;    benchmarking problems;   

DOI  :  10.3390/pr8080980

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown