Journal of Computer Networks and Communications

Volume 2019, Article ID 7983583, 6 pages

https://doi.org/10.1155/2019/7983583

## The Characteristics of Metaheuristic Method in Selection of Path Pairs on Multicriteria Ad Hoc Networks

^{1}Department of Electrical Engineering, Universitas Udayana, Bali, Indonesia^{2}Department of Marine Science, Universitas Udayana, Bali, Indonesia

Correspondence should be addressed to Nyoman Gunantara; di.ca.dunu@aratnanug

Received 3 September 2018; Accepted 5 December 2018; Published 6 January 2019

Academic Editor: Youyun Xu

Copyright © 2019 Nyoman Gunantara and I Dewa Nyoman Nurweda Putra. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

This research analyzes the metaheuristic methods, that is, ant colony optimization (ACO), genetic algorithm (GA), and particle swarm optimization (PSO), in the selection of path pairs on multicriteria ad hoc network. Multicriteria used are signal-to-noise ratio (SNR), load variance, and power consumption. Analysis of the simulation result is done as follows: first, in terms of computing time, the ACO method takes the most time compared with GA and PSO methods. Second, in terms of multicriteria performance, i.e., the performance of SNR, load variance, and power consumption, the GA method shows the same value in each repetition. It is different from ACO and PSO that show varying values. Finally, the selection of the path pairs by the GA method indicates the pairs of the path that are always the same as by the ACO and PSO methods indicate those that vary.

#### 1. Introduction

A metaheuristic method helps in solving the optimization problem. Problems in optimization can be found in many daily life aspects. The kinds of the metaheuristic method are various which are ant colony optimization (ACO), genetic algorithm (GA), and particle swarm optimization (PSO).

The application of ACO is done in the traveling salesman problem by renewing and with the strategy of adaptive pheromone adjustment that is modified [1]. The problem-solving is done in job-shop scheduling with a method that tries to reduce the delay of appointment of process operation through the elitist ant system (EAS) [2] and in vehicle traffic system to guide vehicles so that it can reduce congestion [3]. Furthermore, it is applied to the conical tank system to optimize the parameter in controlling design [4], in a sensor network for node selection that requires smaller energy, reducing packet loss rate [5].

The GA method is applied to measure bandwidth available from cache hit rates with bandwidth as the function of cache hit rates [6] and in an ad hoc network with multiple criteria problem resulted in path pairs that create cooperative communication [7].

Next, the field-effect transistor element (FET) is determined with a modified PSO method. The problem solved is multiple objective optimization problems with Pareto-optimal solution [8]. PSO is applied for instructional optimization problem design that is emphasized in the form and the size of the problem [9]. The application of PSO is to optimize the route of vehicles and salesmen’s trip [10]. PSO is applied to plural optimization problems by making it adaptable for inertial weight and acceleration coefficients that change with iteration.

Not only using one metaheuristic method, furthermore, the merging of two metaheuristic methods is also done to solve the optimization problem. The merging of ACO and GA is used to find the solution to the identification problem on fed-batch of *E. coli* MC4110 [11]. Next, the comparison between GA and PSO methods is done. The comparison between GA and PSO methods is done to find out the feature and the effectiveness in the simulation. It is also used to choose path pair in an ad hoc network with multiple criteria with rank sum weights [12].

The results of the review of these studies have not yet used the three metaheuristic methods. In this research, the three metaheuristic methods are applied in the selection of pairs of tracks on an ad hoc network with multiple criteria. The main contribution of this research is to know the characteristics of ACO, GA, and PSO methods that are analyzed from computing time and performance of multicriteria, such as SNR, load variance, and power consumption. The characteristics of these three metaheuristic methods become very important to acknowledge when choosing an algorithm in finishing one optimization problem.

Section 2 of this paper explains an ad hoc network and scalarization of multicriteria. Section 3 explains the metaheuristic method. Computing time and multicriteria performance or SNR, load variance, and power consumption are summarized in the simulation result in Section 4. In Sections 5 and 6, there are discussion and conclusion consecutively.

#### 2. Ad Hoc Networks and Scalarization

A node communicates with another node and is dynamic and has no infrastructure is a characteristic of an ad hoc network. In the formed communication, the node can act as a source or as a relay or destination. The nodes use broadcast routing and amplify and forward (AF) relays, where the node as the source sends information to each node that has the potential as a relay has, so the information reaches the destination [13]. Broadcast routing is chosen because the data sent could be received by all nodes that are next to each other at the same time, so it will save transmission time.

Nodes on the ad hoc network in communication tend to look for other nodes that are closer to the relay node so that it will form a multihop path, irrespective of 2 hops, 3 hops, 4 hops. The multihop path can search for path pairs that form multihop path pairs. The combination of path pairs that can occur can be in the form of a pair of path consisting of 1 hop with 2 hops, 2 hops with 2 hops, 2 hops with 3 hops, 3 hops with 3 hops, or others. In this research, the maximum number of hops considered for a path is limited to 3. The combination of the path pairs that occur for the 3 hops with 3 hops is [12].

Selection of nodes in an ad hoc network to form a path can be done based on the criteria used. In this study, various multicriteria, namely, SNR, load variance, and power consumption, are used. Mathematical equations, path pairs, and optimal path pairs of each criterion used have been determined [12].

Every criterion is arranged to make a scalar form by giving weight on every criterion [14]. The function that minimizes is given a negative mark, while the function that maximizes is given a positive mark. In this research, to obtain fairness of every object problem, weight is given and normalized with the root mean square (RMS). This normalization is used to give a sense of fairness in every criterion.

The scalarization form from the three criteria is as follows [7]:where states the fitness function; , , and state criteria function of consecutively SNR, load variance, and power consumption; and , , and state the weight to 1, 2, and 3, respectively. In this research, weight is determined by RS weight. RS weight can be determined with the following equation [15]:where *i* states the index of criteria and states many criteria with and . For = 3, the weights that are resulted are = 0.500, = 0.333, and = 0.167.

#### 3. Metaheuristic Methods

Broadly speaking, the optimization problem can be solved by using the exact method and approach method. The word metaheuristic derived from the word meta and heuristic. Meta means a high-level methodology, while heuristics means art in finding new strategies for solving a problem. Metaheuristic is an approach method based on a heuristic method that does not rely on the type of the problem. The metaheuristic method can be distinguished into two which are metaheuristic with single-solution based (local search) and metaheuristic based on population (random search). The examples of a metaheuristic method based on population are ACO, GA, and PSO. This research uses three metaheuristic methods, which are ACO, GA, and PSO.

##### 3.1. Ant Colony Optimization

Ant colony optimization (ACO) is inspired by the behavior of colony of ants. Communication between ants or ants with their environments is based on the use of chemical compound produced by ants. The chemical compound is called pheromone. The ACO algorithm is as follows [16].

The ACO algorithm starts from parameter initialization and pheromone networks. This is done repeatedly iteratively on the cycle so that the path of the first ant is formed and then the path of the ant is made continuously according to local search. Finally, the pheromone network is updated (Algorithm 1).