Genetic algorithms with elitism-based immigrants for dynamic shortest path problem in mobile ad hoc networks

Abstract

Abstract-In recent years, the static shortest path (SP) problem has been well addressed using intelligent optimization techniques, e.g., artificial neural networks (ANNs), genetic algorithms (GAs), particle swarm optimization (PSO), etc. However, with the advancement in wireless communications, more and more mobile wireless networks appear, e.g., mobile ad hoc network (MANET), wireless sensor network (WSN), etc. One of the most important characteristics in mobile wireless networks is the topology dynamics, that is, the network topology changes over time due to energy conservation or node mobility. Therefore, the SP problem turns out to be a dynamic optimization problem (DOP) in MANETs. In this paper, we propose to use elitism-based immigrants GA (EIGA) to solve the dynamic SP problem in MANETs. We consider MANETs as target systems because they represent new generation wireless networks. The experimental results show that the EIGA can quickly adapt to the environmental changes (i.e., the network topology change) and produce good solutions after each change.

GORTS: genetic algorithm based on one-by-one revision of two sides for dynamic travelling salesman problems

“…If the number of nodes varies significantly, the optimisation process needs to be recalculated. Cheng and Yang (2009) proposed a genetic algorithm based on the elite immigration strategy. The best population in the previous generation produce individuals through mutation operations, who will replace the least suitable ones in the current generation.…”

An Energy Distribution and Optimization Algorithm in Wireless Sensor Networks for Maritime Search and Rescue

“…On the other hand, if the environment of nodes only changes slightly in terms of severity of changes, random immigrants may not have any actual effect even when a change occurs because individuals in the previous environment may still be quite fit in the new environment. Based on the above considerations, an immigrants approach, called elitism-based immigrants [22], is proposed for GAs to address the energy deployment problem.…”

Elitism-based immigrants for ant colony optimization in dynamic environments: Adapting the replacement rate

“…DOPs are generated from three TSP stationary benchmark instances obtained from TSPLIB 3 and three VRP stationary benchmark instances obtained from VRPLIB 4 using the DBGP generator with f set to 10 and 100 indicating quickly and slowly changing environments, respectively, and m set to 0.1, 2 Random immigrant ACO is not considered because according to the adaptive rule, the effect in Eq. (12) will be zero for almost all of the times.…”

“…Elitism-based immigrants are generated via transferring knowledge from the previous environment, and hence the diversity generated is controlled. Elitism-based immigrants have shown more consistent performance when integrated with EAs for binary dynamic functions [22] and the dynamic shortest path problem in mobile ad hoc networks [3], with a CHC-based algorithm for the DTSP [21] and with ACO on different combinatorial routing problems [13], [15].…”