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Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 545191, 12 pages
Research Article

Improving the Bin Packing Heuristic through Grammatical Evolution Based on Swarm Intelligence

1División de Estudios de Posgrado e Investigación, Instituto Tecnológico de León, León 37290, GTO, Mexico
2División de Estudios de Posgrado e Investigación, Instituto Tecnológico de Ciudad Madero, Ciudad Madero 89440, TAMPS, Mexico

Received 11 April 2014; Revised 22 June 2014; Accepted 23 June 2014; Published 24 July 2014

Academic Editor: Ker-Wei Yu

Copyright © 2014 Marco Aurelio Sotelo-Figueroa et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In recent years Grammatical Evolution (GE) has been used as a representation of Genetic Programming (GP) which has been applied to many optimization problems such as symbolic regression, classification, Boolean functions, constructed problems, and algorithmic problems. GE can use a diversity of searching strategies including Swarm Intelligence (SI). Particle Swarm Optimisation (PSO) is an algorithm of SI that has two main problems: premature convergence and poor diversity. Particle Evolutionary Swarm Optimization (PESO) is a recent and novel algorithm which is also part of SI. PESO uses two perturbations to avoid PSO’s problems. In this paper we propose using PESO and PSO in the frame of GE as strategies to generate heuristics that solve the Bin Packing Problem (BPP); it is possible however to apply this methodology to other kinds of problems using another Grammar designed for that problem. A comparison between PESO, PSO, and BPP’s heuristics is performed through the nonparametric Friedman test. The main contribution of this paper is proposing a Grammar to generate online and offline heuristics depending on the test instance trying to improve the heuristics generated by other grammars and humans; it also proposes a way to implement different algorithms as search strategies in GE like PESO to obtain better results than those obtained by PSO.