A multi‐objective optimization model based on immune algorithm in wireless mesh networks

Abstract

With the characteristics of high self-organized, dynamic, and interoperable, the wireless mesh network (WMN) is deemed as a potential technology to be applied widely for home, enterprise, and social public service. Many current optimization schemes usually focus on a single metric such as network deployment cost, throughput, QoS, and so on, but few schemes consider that the optimized metric may affect other metrics of WMN. In practice, the influence among the different metrics is often nonignorable. To optimize the performance from a global perspective, we propose a multi-objective optimization model based on immune algorithm (MOM-IA), which provides a paradigm to find the optimal solution satisfying some different restriction conditions. To simplify, MOM-IA mainly analyzes the restriction relationship of connectivity, redundancy, and throughput, which are the multiple objects. Considering the characteristic of dynamic and the discrete integer parameters in WMN, we design a longtime evolution immune algorithm to solve the MOM. Finally, the analysis of experiments presents that MOM-IA has good performance in solution set diversity and Pareto-front distribution, which means the probability to find the optimal solution in WMN.

Multicast routing based on data envelopment analysis with multiple Quality of Service parameters

“…The problem of constructing a constrained Steiner tree is also NP-complete [7]. Many heuristic algorithms for the constrained Steiner tree problem have been proposed such as heuristic algorithm, genetic algorithm, and ant algorithm [8][9][10][11][12][13][14][15][16][17][18].…”