caa a22 4500 605470626 CHVBK 20210128100329.0 cr unu---uuuuu 210128e20150201xx s 000 0 eng 10.1007/s00500-014-1269-x doi (NATIONALLICENCE)springer-10.1007/s00500-014-1269-x Genetic algorithm with ensemble of immigrant strategies for multicast routing in Ad hoc networks [Elektronische Daten] [P. Karthikeyan, S. Baskar] In this paper, an Ensemble of Immigrant Strategies with Genetic Algorithm (EISGA) which optimizes the combined objectives of network lifetime and delay is proposed for solving multicast routing problem. Immigrant strategies are the specific replacement operators designed for dynamic optimization problems and are naturally suited for multicast routing in ad hoc networks. The proposed system ensembles random immigrant with random replacement, random immigrant with worst replacement, elitism-based immigrant and hybrid immigrant strategies. The sequence and topological coding with genetic operators such as modified topology crossover, energy mutation and node mutation are employed in EISGA. The performance of four variants of genetic algorithms formed from these immigrant strategies is evaluated in two different network topologies, with different range of immigrant probability values. Results show that fixing of probability values for various immigrant strategies is very difficult. The proposed EISGA, with equal probability and adaptive probability, is evaluated on four different networks with 10, 20, 30 and 40 nodes on two kinds of topologies. The performance of the proposed EISGA with adaptive probability is assessed in various Learning Period (LP) to determine the suitable LP and is compared with other existing algorithms using non-parametric statistical tests with average ranking. These results endorse that the proposed EISGA improves the performance of GA in solving multicast routing problems effectively. Springer-Verlag Berlin Heidelberg, 2014 Ad hoc networks nationallicence Multicast routing nationallicence Genetic operator combinations (GOCs) nationallicence Ensemble of immigrant strategies with genetic algorithm (EISGA) nationallicence Karthikeyan P. Department of Information Technology, Thiagarajar College of Engineering, 625015, Madurai, Tamilnadu, India aut Baskar S. Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, 625015, Madurai, Tamilnadu, India aut Soft Computing Springer Berlin Heidelberg 19/2(2015-02-01), 489-498 1432-7643 19:2<489 2015 19 500 https://doi.org/10.1007/s00500-014-1269-x text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1269-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Karthikeyan P. Department of Information Technology, Thiagarajar College of Engineering, 625015, Madurai, Tamilnadu, India aut NATIONALLICENCE 700 1- Baskar S. Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, 625015, Madurai, Tamilnadu, India aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/2(2015-02-01), 489-498 1432-7643 19:2<489 2015 19 500