International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 3, Issue 4, April 2013) International Conference on Innovative Trends in Science, Engineering and Management (ITICSEM 2014), Dubai, UAE Page 32 A Graphical Visualization Tool for Analyzing the Behavior of Metaheuristic Algorithms Joaquín Pérez 1 , Nelva Almanza 1 , Miguel Hidalgo 1 , Gerardo Vela, Lizbeth Alvarado 1 , Moisés García 1 , Adriana Mexicano 2 , Crispín Zavala 3 1 National Research Center and Technological Development, Morelos, México 2 Technical Institute of Victoria, Tamaulipas, México 3 University of the State of Morelos, Morelos, México 1 jpo_cenidet@yahoo.com.mx 2 mexicanoa@gmail.com 3 crispin_zavala@uaem.mx Abstract— This paper proposes a visualization tool for analyzing the behavior of metaheuristic algorithms. It works by reproducing graphically the solutions generated in execution time. The tool was evaluated analyzing the Hybrid Grouping Genetic Algorithm for Bin Packing (HGGA_BP). It was used to solve a benchmark with 17 instances of the one dimensional Bin Packing problem (1D-BPP). The use of the visualization tool allowed identifying some logical errors in the program code which avoided the algorithm converge in some cases to the optimal solution. After correcting errors the HGGA_BP algorithm increased its efficiency until 48%, in some cases. Keywords— Metaheuristic algorithms, bin packing problem, graphical visualization tools. I. INTRODUCTION Currently there are a variety of algorithms developed to solve combinatorial optimization problems such genetic algorithms [1], simulated annealing [2], ant colony optimization [3], tabu search [4], among others [5], [6], which are heuristic-based. By its nature, the analysis of these algorithms is a difficult task. Traditionally the analysis of algorithms has been focused on efficiency [7], [8], [9], [10], performance [1], [11], [12], [13], [14], [15], [16], application of some statistical methods [17], [18], [19], or descriptive statistics [20]. Previous approaches have been insufficient to improve the algorithms. According to the literature, one promising approach is the graphical visualization of the behavior of the algorithms. In this sense, this paper presents a graphical visualization tool for analyzing algorithms that solves the 1D-BPP with the objective of improving metaheuristic algorithms. II. RELATED WORK In this section are described some tools developed to analyze graphically the behavior of some metaheuristics. Visualizer for Metaheuristics Development Framework (V-MDF) [21], [22], this tool was developed to analyze algorithms for solving the Military Transport Planning problem (MTP). The tool addresses the problem of tuning search strategies for any search strategy. Until now the tool had been applied for Tabu Search, Ant Colony Optimization (ACO), Simulated Annealing and Genetic Algorithms, it uses a Distance Radar visualization module where the human and computer can collaborate to diagnose the occurrence of negative incidents along the search trajectory on a set of training instances. This tool allows observing the behavior of the search and some dynamic changes of the search strategies. The visualizer (VIZ) [23]. VIZ is able to reproduce in animated way the behavior of any search algorithm. It had been used for visualizing the behavior of three local search algorithms (iterative local search, reactive-Tabu search and stochastic local search). It allows observing: the objective value and the behavior of the objective function by means of applying fitness distance correlation. VIZ also has an event bar which allows highlight relevant information during a search. This tool allows solving the Traveling Salesman Problem (TSP), Low Autocorrelation Binary Sequence and Quadratic Assignment Problem. Several features of this tool can be found in [24], [25], [26]. TSPAntSim [27], this tool is a web-based simulation and analysis software (TSP AntSim) for solving TSP using ACO algorithms. Six different versions of ACO algorithm and two search algorithms (2-opt and 3-opt) were implemented.