Title
Enhancing the performance of hybrid genetic algorithms by differential improvement
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.
Year
DOI
Venue
2013
10.1016/j.cor.2012.10.014
Computers & OR
Keywords
DocType
Volume
extensive improvement algorithm,Hybrid metaheuristics,proposed Differential Improvement,Bee Colony Optimization technique,Hybrid Genetic Algorithms,Hybrid Scatter Search algorithm,hybrid genetic algorithm,improvement algorithm,DI framework,proposed algorithm,differential improvement modification
Journal
40
Issue
ISSN
Citations 
4
0305-0548
7
PageRank 
References 
Authors
0.50
36
2
Name
Order
Citations
PageRank
Zvi Drezner11195140.69
Alfonsas Misevičius2402.95