Improving Robustness in Social Fabric-based Cultural Algorithms

In this thesis, we propose two new approaches which aim at improving robustness in social fabric-based cultural algorithms. Robustness is one of the most significant issues when designing evolutionary algorithms. These algorithms should be capable of adapting themselves to various search landscapes. In the first proposed approach, we utilize the dynamics of social interactions in solving complex and multi-modal problems. In the literature of Cultural Algorithms, Social fabric has been suggested as a new method to use social phenomena to improve the search process of CAs. In this research, we introduce the Irregular Neighborhood Restructuring as a new adaptive method to allow individuals to rearrange their neighborhoods to avoid local optima or stagnation during the search process. In the second approach, we apply the concept of Confidence Interval from Inferential Statistics to improve the performance of knowledge sources in the Belief Space. This approach aims at improving the robustness and accuracy of the normative knowledge source. It is supposed to be more stable against sudden changes in the values of incoming solutions. The IEEE-CEC2015 benchmark optimization functions are used to evaluate our proposed methods against standard versions of CA and Social Fabric. IEEE-CEC2015 is a set of 15 multi-modal and hybrid functions which are used as a standard benchmark to evaluate optimization algorithms. We observed that both of the proposed approaches produce promising results on the majority of benchmark functions. Finally, we state that our proposed strategies enhance the robustness of the social fabric-based CAs against challenges such as multi-modality, copious local optima, and diverse landscapes

Studying the effect of multisource Darwinian particle swarm optimization in search and rescue missions

Robotic Swarm Intelligence is considered one of the hottest topics within the robotics research eld nowadays, for its major contributions to di erent elds of life from hobbyists, makers and expanding to military applications. It has also proven to be more effective and effcient than other robotic approaches targeting the same problem. Within this research, we targeted to test the hypothesis that using more than a single starting/ seeding point for a swarm to explore an unknown environment will yield better solutions, routes and cover more area of the search space within context of Search and Rescue applications domain. We tested such hypothesis via extending existing Particle swarm optimization techniques for search and rescue operations (i.e. Robotic Darwinian Particle Swarm Optimization and we split the swarm into smaller groups that start exploration from di erent seed positions, then took the convergence time average for di erent runs of simulations and recorded the results for quanti cation. The results presented in this work con rms the hypothesis we started with, and gives insight to how the number of robots contributing in the experiments a ect the quality of the results. This work also shows a direct correlation between the swarm size and the search space

Firefly-Inspired Synchronization in Swarms of Mobile Agents

Synchronization can be a necessary prerequisite to perform coordinated actions or reach consensus in decentralized multi-agent systems, such as robotic swarms and sensor networks. One of the simplest distributed synchronization algorithms is firefly synchronization, also known as pulse-coupled oscillator synchronization. In this framework, each agent possesses an internal oscillator and the completion of oscillation cycles is signaled by means of short pulses, which can be detected by other neighboring agents. This thesis focuses on a realistic mode of interaction for practical implementations, in which agents have a restricted field of view used to detect pulses emitted by other agents. The effect of agent speed on the time required to achieve synchronization is studied. Simulations reveal that synchronization can be fostered or inhibited by tuning the agent (robot) speed, leading to distinct dynamical regimes. These findings are further validated in physical robotic experiments. In addition, an analysis is presented on the effect that the involved system parameters have on the time it takes for the ensemble to synchronize. To assess the effect of noise, the propagation of perturbations over the system is analyzed. The reported findings reveal the conditions for the control of clock or activity synchronization in swarms of mobile agents

Un algorisme basat en Artificial Bee Colony per al problema dels nodes crítics en xarxes

En aquest treball presentem una proposta algorítmica basada en l'Artificial Bee Colony per al problema dels nodes crítics, anomenada ABCCNP. El problema dels nodes crítics en xarxes (en anglès the Critical Node Problem -CNP-) és un conegut problema d'optimització combinatòria NP-complet que ha atret molta atenció investigadora recentment degut al seu gran nombre d'aplicacions en el món real. Essencialment, el problema tracta de trobar un subconjunt de com a molt k nodes, l'eliminació dels quals degrada al màxim la connectivitat entre parells de nodes del graf resultant. Nombrosos autors han proposat solucions per al CNP en la literatura fent ús d'algorismes exactes, algorismes greedy i conegudes metaheurístiques com el Simulated Annealing, Variable Neighborhood Search, etc. Recentment, s'han presentat molts estudis exitosos que han donat solució a desafiadors problemes d'optimització fent ús de tècniques metaheurísitques de Swarm Intelligence inspirades en el comportament col·lectiu de societats d'animals. Un exemple és l'algorisme d'Artificial Bee Colony que s'inspira en el comportament de cerca d'aliment dels eixams naturals d'abelles mel·líferes. En aquest estudi adaptem l'algorisme base d'ABC per al CNP proposant dos avançats algorismes de construcció de fonts d'alimentació i de cerca local, fent ús d'un conjunt de paràmetres de control per obtenir el millor balanç entre exploració i explotació de solucions. La qualitat de l'ABCCNP és avaluada sobre un conegut conjunt d'instàncies de xarxes aleatòries i comparada amb els altres algorismes de l'estat de l'art. Els resultats obtingut demostren que es tracta d'una solució molt competitiva en molts aspectes.In this paper we present an algorithmic approach based on the Artificial Bee Colony for the critical node problem, called ABCCNP. The Critical Node Problem (CNP) is a well-known NP-complete combinatorial optimisation problem that has attracted a lot of research attention recently due to the large number of real-world applications. The problem is about finding a subset of at most k nodes, whose deletion maximally degrades the connectivity between pairs of nodes in the resulting graph. Numerous authors have proposed solutions for the CNP in the literature using exact algorithms, greedy algorithms and some wellknown metaheuristics like Simulated Annealing, Variable Neighborhood Search, etc. Recently, many successful studies have been presented solving challenging optimisation problems using Swarm Intelligence metaheuristic techniques that are inspired by the collective behaviour of animal societies. One example is the Artificial Bee Colony algorithm that is specifically inspired by the foraging behaviour of natural honeybees. In this study, we adapt the base ABC algorithm for the CNP by proposing two advanced algorithms for the construction of food sources and local foraging, making use of a set of control parameters to obtain the best balance between exploration and exploitation. The quality of ABCCNP is evaluated on a set of random network instances and compared with other state-of-the-art algorithms. The results obtained show that it is a very competitive solution in many aspects

MULTI-AGENT SOURCE LOCALIZATION USING PASSIVE SENSING

Ph.DDOCTOR OF PHILOSOPH

Sistema de guía de múltiples drones de superficie acuática utilizando información dinámica

El objetivo del trabajo final de grado fue diseñar un sistema de guía de múltiples drones de superficie, que descentralice eficazmente la obtención de datos de un entorno, utilizando información dinámica. El presente trabajo diseña el sistema de guía de una flota de drones de superficie acuática que base sus decisiones en información cambiante con el transcurso del tiempo, desarrollándose con técnicas y métodos propios de sistemas multi-agente o multi-robot, no dejando de lado el desarrollo interno de un robot parte del sistema multi-robot, en cuanto a guía o planificación de movimiento, comunicación y cooperación.CONACYT – Consejo Nacional de Ciencia y TecnologíaPROCIENCI