Multi-Objective Tabu Search to Balance Multihoming Loads in Heterogeneous Wireless Networks

Abstract

The advantages of the increasing usage of mobile devices that operate under the multihoming scheme are changing the communications world drastically. Therefore, next generation networks operators have the challenging task to distribute connections of mobile devices efficiently over their access networks, creating a big heterogeneous wireless network for telecommunications. We present a mixed integerlinear programming (MILP) model to balance the load of multiple services over wireless networks taking into account three key indicators: connection loads of access networks, connection costs, and battery consumption of connections. To solve the multi-objective problem, we propose a multi-objective Tabu Search procedure that is capable to find non-supported solutions in the online efficient set. To test the performance of our multi-objective Tabu Search, we tested it over four instances of the literature. In the first instance, a small instance, our procedure finds the true efficient set of solutions. For the other three instances, large instances with over a thousand mobile devices, our procedure finds good online efficient sets of solutions in less than 30 seconds. Finally, using appropriate multi-objective metrics, we compare the results of our multi-objective Tabu Search against the results of a state of the art multi-objective genetic algorithm in the literature for the same problem, outperforming the genetic algorithm in every instance tested.

Performance Evaluation of Meta-Heuristic Algorithms for Designing Multi-Objective Multi-Product Multi-Echelon Supply Chain Network

“…(Sun et al 2008) utilized an ant colony approach for dealing with a multi- In sum, the literature review shows that the simulated annealing and tabu search are two metaheuristic approaches that incorporate mechanisms for going beyond local optima. They have an ability of obtaining robust solutions for solving complex problems in many areas, such as the routing problem (Martínez-Puras and Pacheco 2016), the wireless networking problems (Huertas and Donoso 2018), problem of the allocation of resources (Belfares et al 2007), the location of the facilities problems (Sun 2006;Li et al 2009), sequencing problem (Mansouri 2006).…”

Network Optimization of CNT Yarn Sensor Based on NNIA Algorithm in Damage Monitoring of 3D Braided Composites

“…With the rise in artificial intelligence technology, intelligent optimization algorithm has become a good way to solve this problem. These algorithms include: Genetic algorithm (GA), swarm intelligence algorithm (ant colony, particle swarm), annealing algorithm, firefly algorithm, artificial immune algorithm, neural network algorithm, tabu search algorithm, and hybrid optimization algorithm [ 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. The artificial immune algorithm proposed by de Castro et al shows better effect in solving multi-objective optimization problems [ 26 ].…”

Vertical handover algorithm based on multi-attribute and neural network in heterogeneous integrated network

“…In order to adapt to the different requirements of users and the development of various types of business, wireless networks with different architectures form a multiple heterogeneous integrated network [5][6][7], in which the most representative is the integration of two kinds of heterogeneous networks: WLAN and cellular [8][9][10][11]. WLAN networks have relatively wide bandwidth and small coverage, while cellular series networks (such as current 3G/4G wireless networks) have relatively narrow bandwidth and wide coverage [12][13][14], so the integration of cellular series network and WLAN network can complement each other [15,16], giving full play to their respective strengths and restraining their limitations at the same time [17][18][19], in addition, with the birth of 5G technology, the integration between multiple networks with different architectures will make a revolutionary progress [20][21][22][23][24][25]. Therefore, the vertical handover algorithm should be considered with a variety of disturbing factors and a variety of candidate network types [26], and it is more suitable to be combined with intelligent algorithms [27].…”

“…Reference [11] proposed an adaptive vertical handover algorithm based on artificial neural network similar to the algorithm designed in this paper, but it only chose the LTE and WLAN network as candidate network. Reference [12] presented a mixed integer linear programming (MILP) model to balance multi-homing loads in heterogeneous wireless networks based on multi-objective tabu search method. Both the reference [13] and the reference [15] applied the genetic algorithm to the relevant handover decision, which had a high success rate of handover, but also lacked consideration of 5G network factors.…”