Multi-population Genetic Algorithms with Immigrants Scheme for Dynamic Shortest Path Routing Problems in Mobile Ad Hoc Networks

Abstract

Abstract. The static shortest path (SP) problem has been well addressed using intelligent optimization techniques, e.g., artificial neural networks, genetic algorithms (GAs), particle swarm optimization, etc. However, with the advancement in wireless communications, more and more mobile wireless networks appear, e.g., mobile ad hoc network (MANET), wireless mesh network, 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 in mobile wireless networks. In this paper, we propose to use multi-population GAs with immigrants scheme to solve the dynamic SP problem in MANETs which is the representative of new generation wireless networks. The experimental results show that the proposed GAs can quickly adapt to the environmental changes (i.e., the network topology change) and produce good solutions after each change.

Application of an Evolutionary Optimization Technique to Routing in Mobile Wireless Networks

“…Figure (6) .A mobile wireless network can be expressed as a weighted graph G=(V,E),where V set of nodes and E set of links. Topology area: Nodes are distributed randomly on 1000*1000 m 2 .…”

“…GAs would seem to be quite promising for solving more complex WMN models than the one dealt with in this paper, such as those including multiple flows and multi-radio multi-channels. Hui Cheng et al [6], a multi-population GAs with immigrant's scheme was proposed to solve the dynamic shortest path (SP) problem in MANETs which is the representative of new generation wireless networks. The experimental results show that the proposed GAs can quickly adapt to the environmental changes (i.e., the network topology change) and produce good solutions after each change.…”

Evolutionary Dynamic Optimization: Methodologies

“…Similar examples can be found in [1,102], of which the latter proposed six different types of objectives, including retaining more old solutions; retaining more random solutions; reversing the first objective; keeping a distance from the closest neighbour; keeping a distance from all individuals; and keeping a distance from the best individual. The diversitymaintaining strategy is still the main strategy in many recent approaches, for example, see [6,9,10,29,35,39,42,50,70,125,126].…”

“…For the first goal, assigning different tasks to the sub-populations, there might be multiple small populations in P search searching for new solutions and there is only one large population in P track to track changing peaks [77], or there might be one large population to search and multiple sub-population for tracking changes [9,19,29,38,61,63,73], or each sub-population can both search for new solutions and track changes [39,57,58,104]. Relating to the goal of assigning the tasks to subpopulations, it should be noted that in dynamic optimization multiple populations are used not only for exploring different parts of the search space, but also for coevolution [40,73,75] or maintaining diversity and balancing between exploitation and exploration [110].…”

Genetic algorithms in wireless networking: techniques, applications, and issues

“…Several immigrants and memory schemes that have been developed for GAs for general dynamic optimization problems are adapted and integrated into the specialized GA to solve the DSPRP in MANETs. The experimental results indicate that both immigrants and [31] MANET MOGA Distributed multipopulation GA A distributed GA approach incorporating multi-population with random immigrant schemes solves the shortest path routing in MANET. Gen et al [32] LANs SOGA Integer coded GA for random distributions A priority encoding scheme is proposed using GAs to represent and optimize all possible paths in network graph.…”