Abstract
We describe a new memetic algorithm scheme combining a genetic algorithm (GA) and a particle swarm optimization algorithm (PSO). This memetic scheme uses the basic dynamics of PSO instead of the concept of the survival of fittest (selection strategy) in GA. Even though the scheme does not use a selection strategy, it shows that the algorithm can find good results and can be an alternative approach for network based optimization problems.
We test it in the context of a memetic algorithm applied to well known spanning tree based optimization problem, the degree constrained minimum spanning tree problem (DCMST). We compare with existing evolutionary algorithms (EAs), including EA using edge window decoder and EA using edge-set encoding, which represent the current state of the art on the DCMST. The new memetic algorithm demonstrates superior performance on the smaller and lower degree instances of the well-used ‘Structured Hard’ DCMST problems, and similar performance on the larger and higher degree instances.
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Soak, SM., Lee, SW., Mahalik, N.P., Ahn, BH. (2006). A New Memetic Algorithm Using Particle Swarm Optimization and Genetic Algorithm. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11892960_15
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DOI: https://doi.org/10.1007/11892960_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46535-5
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