7th IEEE International Conference Humanoid, Nanotechnology, Information Technology Communication and Control, Environment and Management (HNICEM) The Institute of Electrical and Electronics Engineers Inc. (IEEE) – Philippine Section 12-16 November 2013 Hotel Centro, Puerto Princesa, Palawan, Philippines Optimization of Decentralized Information Dissemination in Quadrotor Swarm Using Genetic Algorithm Jose Martin Z. Maningo, Gerard Ely U. Faelden, Reiichiro Christian S. Nakano, Argel A. Bandala, Elmer P. Dadios Gokongwei College of Engineering De La Salle University Manila, Philippines jmmaningo10@gmail.com, elmer.dadios@dlsu.edu.ph Abstract—There is a glaring problem in communication systems when it comes to a decentralized robotic swarm. Since a decentralized swarm would limit the awareness of each agent to its immediate surroundings/neighbors, the exchange of information between agents may now prove to be challenging. An epidemic-based broadcasting technique is then presented to resolve the problem of end-to-end agent communication. This paper aims to optimize the information diffusion by means of implementing genetic algorithm to optimize the time it will take for each quadrotor individual to acquire the information coming from a single source (i.e. the quadrotor who first received the information from an external stimulus). The method by which this is done is epidemic in nature. Due to this, for each time there would be a signal broadcasting, the genetic algorithm would be run to determine the next ideal location of each individual. A genetic algorithm was looped several times to achieve the desired solution. The results showed that for each run of the GA, the number of quadrotors having received the information continually increased until the output converges to a fitness level. However this only worked under certain constraints that need to be weighed out properly. This includes the readjustment of the fitness and crossover functions. Also, the parameters of the GA must be well calibrated for proper output response. Index Terms—quadrotor swarm, decentralized communication, optimization, genetic algorithm. I. INTRODUCTION In the field of computational intelligence, swarm intelligence attracts itself a lot of potential for research [1]. Swarm Intelligence is based on the collective behavioral instincts exhibited by groups of organisms. As such, these metaheuristic agents can overcome their own limitations by working together as a group or network [2]. But, a huge hindrance in swarm elements is their limited capabilities in communications. Their mode of communication is only limited to the neighboring swarm member. This can be seen in the behavior of ants [3]. This typical behavior of a swarm is term as being decentralized. Also, in a swarm, no hierarchy is set between individuals. They do not follow a leader or a global plan [4]. As such, all individuals of the swarm are of equal priority. This characteristic of the swarm is what makes each individual autonomous in nature. This decentralized nature of a swarm offers improved scalability, modularity, and redundancy to the system [5]. After properly laying down the foundations of what constitutes a swarm, a formal definition can be stated as: “A swarm is a completely decentralized and autonomous mechanism that comprises of autonomous agents as its individuals” [4]. The problem with a decentralized group of individuals is their manner of communication between one another. Since each individual would only be aware of what is near it, it would be difficult for a swarm to work collectively. Similarly, artificial swarm intelligence holds the same problems. Since the behavior of swarm robots is patterned from real-life swarm behavior, they too would have trouble localizing themselves among the group. The localization of these robot individuals is a very big issue in the system as it plays an essential role in various mobile robot systems [6-7]. Common solutions to this problem may include: attaching cameras [8] on each agent or wireless communication between neighboring individuals. This paper focuses on the wireless communication between robot agents. Given a scenario in which a swarm needs to perform a complex task, all members of the swarm must have means of relaying information to the other agents [9]. Some natural examples of this kind of behavior include foraging [10], bee colonies [11], and ant systems [12]. In the case of foraging, parallel searching and cooperative transportation are required [13]. As such, when one agent is able to locate the food, all the agents must be made aware of its presence and location as fast as possible. The aim of this paper is to optimize the diffusion of information by means of using Genetic Algorithm. II. GENETIC ALGORITHM Genetic Algorithm (GA) is a metaheuristic approach in solving a problem that provides limited information in procuring an optimal solution. It is a subfield of stochastic optimization in which random search plays a huge role. GA adapts the concept of survival-of-the-fittest [14]. A population consisting of chromosomes is initially set. For each population generation, the chromosomes reproduce. The offspring/s that gives the better fitness as evaluated in a pre-defined fitness function then becomes the parent for the next generation while the less fit offspring/s is killed off. Each chromosome contains 978-1-4799-4020-2/14/$31.00 ©2014 IEEE