Abstract
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.
Highlights
The methodology development to solve a specific problem is a process that entails the problem study and the analysis instances from such problem
The results obtained by the Particle Swarm Optimisation (PSO) and Particle Evolutionary Swarm Optimization (PESO) with the Grammars are shown in Table 4; these results are the median from 33 individual experiments
It was proposed using PESO as a search strategy based on Swarm Intelligence to avoid the problems observed in PSO
Summary
The methodology development to solve a specific problem is a process that entails the problem study and the analysis instances from such problem. It has been shown that is possible to use this methodology to generate BPP heuristics by using PESO and PSO as search strategies; it was shown that the heuristics generated with the proposed Grammar have better performance than the BPP’s classical heuristics, which were designed by an expert in Operational Research. Those results were obtained by comparing the results obtained by the GE and the BPP heuristics by means of Friedman nonparametric test [26].
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call