Abstract
This chapter proposes a new multi-population global optimization algorithm: the parallel 3-parent genetic algorithm (P3PGA). The performance of the new algorithm was compared with 16 other algorithms based on 30 benchmark functions from the 2014 Congress on Evolutionary Computation test suite. P3PGA was the best-performing algorithm on 14 out of the 30 benchmark functions.
We applied P3PGA to a minimal cost path routing problem in wireless mesh networks. The proposed approach was implemented in MATLAB and simulated for various wireless mesh network sizes and scenarios. We compared its performance on this problem with eight other approaches: Ad hoc On-Demand Distance Vector Routing, Dynamic Source Routing, Genetic Algorithm, Biogeography-Based Optimization, Firefly Algorithm, Ant Colony Optimization, the BAT Algorithm, and Big Bang–Big Crunch algorithm based routing approaches. P3PGA outperformed all other approaches for networks with 1000+ nodes.
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Appendix 1: Algorithm Performance Results
Appendix 1: Algorithm Performance Results
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Singh, A., Kumar, S., Singh, A., Walia, S.S. (2020). Parallel 3-Parent Genetic Algorithm with Application to Routing in Wireless Mesh Networks. In: Subair, S., Thron, C. (eds) Implementations and Applications of Machine Learning. Studies in Computational Intelligence, vol 782. Springer, Cham. https://doi.org/10.1007/978-3-030-37830-1_1
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