A multi-objective multicast routing optimization based on differential evolution in MANET

Abstract

Purpose The purpose of this paper is to propose a multi-objective differential evolution algorithm named as MOMR-DE to resolve multicast routing problem. In mobile ad hoc network (MANET), multicast routing is a non-deterministic polynomial -complete problem that deals with the various objectives and constraints. Quality of service (QoS) in the multicast routing problem mainly depends on cost, delay, jitter and bandwidth. So the cost, delay, jitter and bandwidth are always considered as multi-objective for designing multicast routing protocols. However, mobile node battery energy is finite and the network lifetime depends on node battery energy. If the battery power consumption is high in any one of the nodes, the chances of network’s life reduction due to path breaks are also more. On the other hand, node’s battery energy had to be consumed to guarantee high-level QoS in multicast routing to transmit correct data anywhere and at any time. Hence, the network lifetime should be considered as one objective of the multi-objective in the multicast routing problem. Design/methodology/approach Recently, many metaheuristic algorithms formulate the multicast routing problem as a single-objective problem, although it obviously is a multi-objective optimization problem. In the MOMR-DE, the network lifetime, cost, delay, jitter and bandwidth are considered as five objectives. Furthermore, three QoS constraints which are maximum allowed delay, maximum allowed jitter and minimum requested bandwidth are included. In addition, we modify the crossover and mutation operators to build the shortest-path multicast tree to maximize network lifetime and bandwidth, minimize cost, delay and jitter. Findings Two sets of experiments are conducted and compared with other algorithms for these problems. The simulation results show that our proposed method is capable of achieving faster convergence and is more preferable for multicast routing in MANET. Originality/value In MANET, most metaheuristic algorithms formulate the multicast routing problem as a single-objective problem. However, this paper proposes a multi-objective differential evolution algorithm to resolve multicast routing problem, and the proposed algorithm is capable of achieving faster convergence and more preferable for multicast routing.

Solving the Delay-Constrained Least-Cost routing problem using Tabu Search with Edge Betweenness

“…Zhang et al [14] proposed a method for least-cost QoS multicast routing based on genetic simulated annealing algorithm NGSA, the genetic algorithm et simulated annealing algorithm are combined to improve the computing performance in this method. Recently as in [15], a multiobjective differential evolution algorithm named as MOMR-DE is proposed using the modified crossover and mutation operators to build the shortest-path multicast tree. Constraint handling scheme is used to handle QoS constraints.…”

A Quality of Service (Qos) Load Balanced Hybrid Simulated Annealing -Stochastic Diffusion Search (Sa-Sds) Network Backbone for Manet

“…In recent times, there are several metaheuristic algorithms that formulate the problem of multicast routing which is taken as a problem of a single objective. Wei et al, [11] had been considering Multi-Objective Multicast Routing-Differential Evolution algorithm (MOMR-DE), its bandwidth, jitter, delay, cost and lifetime of the network as the five objectives. Additionally, the authors also modified the mutation and crossover operators for building the path that was the shortest to the multicast tree for maximizing the lifetime of the network, jitter, delay, bandwidth and minimizing cost.…”

Trusted Sensing Model for Mobile Ad HoC Network Using Differential Evolution Algorithm

“…Most of the Differential Evolution algorithm concentrates in optimal path selection, topology control maintenance and routing protocol. In MANET, the multicast routing problem is resolved using multi objective differential evolution algorithm [14] which converges faster. Here, the crossover and mutation operators are modified to build the shortest path to improve the network lifetime and bandwidth of the network.…”