Enhancing the performance of hybrid genetic algorithms by differential improvement

Enhancing the performance of hybrid genetic algorithms by differential improvement

0.00 Avg rating0 Votes
Article ID: iaor2013756
Volume: 40
Issue: 4
Start Page Number: 1038
End Page Number: 1046
Publication Date: Apr 2013
Journal: Computers and Operations Research
Authors: ,
Keywords: heuristics: genetic algorithms
Abstract:

A differential improvement modification to Hybrid Genetic Algorithms is proposed. The general idea is to perform more extensive improvement algorithms on higher quality solutions. Our proposed Differential Improvement (DI) approach is of rather general character. It can be implemented in many different ways. The paradigm remains invariant and can be easily applied to a wider class of optimization problems. Moreover, the DI framework can also be used within other Hybrid metaheuristics like Hybrid Scatter Search algorithms, Particle Swarm Optimization, or Bee Colony Optimization techniques. Extensive experiments show that the new approach enables to improve significantly the performance of Hybrid Genetic Algorithms without adding extra computer time. Additional experiments investigated the trade‐off between the number of generations and the number of iterations of the improvement algorithm. These experiments yielded six new best known solutions to benchmark quadratic assignment problems. Many other variants of the proposed algorithm are suggested for future research.

Reviews

Required fields are marked *. Your email address will not be published.