Swarm Robotics

This study analyzes and designs the Swarm intelligence (SI) that Self-organizing migrating algorithm (SOMA) represents to solve industrial practice as well as academic optimization problems, and applies them to swarm robotics. Specifically, the characteristics of SOMA are clarified, shaping the basis for the analysis of SOMA's strengths and weaknesses for the release of SOMA T3A, SOMA Pareto, and iSOMA, with outstanding performance, confirmed by well-known test suites from IEEE CEC 2013, 2015, 2017, and 2019. Besides, the dynamic path planning problem for swarm robotics is handled by the proposed algorithms considered as a prime instance. The computational and simulation results on Matlab have proven the performance of the novel algorithms as well as the correctness of the obstacle avoidance method for mobile robots and drones. Furthermore, two out of the three proposed versions achieved the tie for 3rd (the same ranking with HyDE-DF) and 5th place in the 100-Digit Challenge at CEC 2019, GECCO 2019, and SEMCCO 2019 competition, something that any other version of SOMA has yet to do. They show promising possibilities that SOMA and SI algorithms offer.Tato práce se zabývá analýzou a vylepšením hejnové inteligence, kterou představuje samoorganizující se migrační algoritmus s možností využití v průmyslové praxi a se zaměřením na hejnovou robotiku. Je analyzován algoritmus SOMA, identifikovány silné a slabé stránky a navrženy nové verze SOMA jako SOMA T3A, SOMA Pareto, iSOMA s vynikajícím výkonem, potvrzeným známými testovacími sadami IEEE CEC 2013, 2015, 2017 a 2019. Tyto verze jsou pak aplikovány na problém s dynamickým plánováním dráhy pro hejnovou robotiku. Výsledky výpočtů a simulace v Matlabu prokázaly výkonnost nových algoritmů a správnost metody umožňující vyhýbání se překážkám u mobilních robotů a dronů. Kromě toho dvě ze tří navržených verzí dosáhly na 3. a 5. místo v soutěži 100-Digit Challenge na CEC 2019, GECCO 2019 a SEMCCO 2019, což je potvrzení navržených inovací. Práce tak demonstruje nejen vylepšení SOMA, ale i slibné možnosti hejnové inteligence.460 - Katedra informatikyvyhově

Explaining SOMA: The relation of stochastic perturbation to population diversity and parameter space coverage

The Self-Organizing Migrating Algorithm (SOMA) is enjoying a renewed interest of the research community, following recent achievements in various application areas and renowned performance competitions. In this paper, we focus on the importance and effect of the perturbation operator in SOMA as the perturbation is one of the fundamental inner principles of SOMA. In this in-depth study, we present data, visualizations, and analysis of the effect of the perturbation on the population, its diversity and average movement patterns. We provide evidence that there is a direct relation between the perturbation intensity (set by control parameter prt) and the rate of diversity loss. The perturbation setting further affects the exploratory ability of the algorithm, as is demonstrated here by analysing the parameter space coverage of the population. We aim to provide insight and explanation of the impact of perturbation in SOMA for future researchers and practitioners. © 2021 ACM.IGA/CebiaTech/2021/00

Self-organizing migrating algorithm with clustering-aided migration and adaptive perturbation vector control

The paper proposes the Self-organizing Migrating Algorithm with CLustering-aided migration and adaptive Perturbation vector control (SOMA-CLP). The SOMA-CLP is the next iteration of the SOMA-CL algorithm, further enhanced by the linear adaptation of the prt control parameter used to generate a perturbation vector. The latest CEC 2021 benchmark set on a single objective bound-constrained optimization was used for the performance measurement of the improved variant. The proposed algorithm SOMA-CLP results were compared and tested for statistical significance against four other SOMA variants. © 2021 ACM.IGA/CebiaTech/2021/00

Improvement of Metaheuristic Algorithms Using Symbolic Regression

Jelikož na poli optimalizace stále dochází k vývoji a mnohým výzkumům, je cílem této práce nalezení zlepšení metaheuristických algoritmů SOMA, PSO a DE prostřednictvím analytického programování. Proto se tato práce v prvních částech zabývá rozborem těchto metaheuristických algoritmů a jsou zde také popsány principy analytického programování, jež bylo využito jako metoda symbolické regrese. Všechny algoritmy byly posléze implementovány v jazyce C++ pro účely experimentů, jejichž výsledky jsou poté prezentovány a vyhodnoceny v závěrečných částech. Zlepšení optimalizačních schopností se podařilo nalézt především u algoritmu SOMA. U algoritmů PSO a DE došlo ke zlepšení u vybraných testovacích funkcí.Optimization methods are still under development, and researchers are working on the improvement of current methods. The purpose of this thesis is to find an improvement of three metaheuristic algorithms - SOMA, PSO, and DE. The analytic programming is used as a method of symbolic regression for this purpose. The beginning of this thesis consists of descriptions of SOMA, PSO, and DE, as well as of analytic programming. All algorithms were implemented in the C++ programming language and experiments were performed. The results are evaluated at the end of this thesis. Significant improvement was found for the SOMA algorithm. For PSO and DE, improvements were observed for some of the objective functions.460 - Katedra informatikyvýborn

Efficient Learning Machines

Computer scienc

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field

An energy-efficient method for hybrid mobile cloud computing

Many benefits of cloud computing are now well established, as both enterprise and mobile IT have been transformed by its pervasiveness. Backed by the virtually unbounded resources of cloud computing, battery-powered mobile computing systems can meet the demands of even the most resource-intensive applications. However, many existing hybrid mobile-cloud (HMC) applications are inefficient in terms of optimising trade-offs between simultaneously conflicting objectives, such as minimising both battery power consumption and network usage. To tackle this problem, we propose a novel method that can be used not only to instrument HMC applications but also to search for its efficient configurations representing compromise solutions between the objectives. The method is based on a general purpose HMC framework, which provides scalability, and make runtime decisions that are based on: 1) changing of the environment (i.e. WiFi signal level variation), and 2) itself in a changing environment (i.e. actual observed packet loss in the network). Our experimental evaluation considers two Android-based applications for smartphones, and a Python-based foraging task performed by a battery powered and Raspberry Pi controlled Thymio robot. Analysis of our results shows that our method can be used for small to medium size HMC applications to achieve energy efficient computing systems. Furthermore, HMC applications can achieve better optimisation in a changing environment (i.e. signal level variation) than using static off loading or running the applications only on a mobile device. However, a self-adaptive decision would fall behind when the change in the environment happens within the system (i.e.network congestion). In such a case, a self-aware system can perform well, in terms of minimising the two objectives and better performance of applications

STOCHASTIC MOBILITY MODELS IN SPACE AND TIME

An interesting fact in nature is that if we observe agents (neurons, particles, animals, humans) behaving, or more precisely moving, inside their environment, we can recognize - tough at different space or time scales - very specific patterns. The existence of those patterns is quite obvious, since not all things in nature behave totally at random, especially if we take into account thinking species like human beings. If a first phenomenon which has been deeply modeled is the gas particle motion as the template of a totally random motion, other phenomena, like foraging patterns of animals such as albatrosses, and specific instances of human mobility wear some randomness away in favor of deterministic components. Thus, while the particle motion may be satisfactorily described with a Wiener Process (also called Brownian motion), the others are better described by other kinds of stochastic processes called Levy Flights. Minding at these phenomena in a unifying way, in terms of motion of agents \u2013 either inanimate like the gas particles, or animated like the albatrosses \u2013 the point is that the latter are driven by specific interests, possibly converging into a common task, to be accomplished. The whole thesis work turns around the concept of agent intentionality at different scales, whose model may be used as key ingredient in the statistical description of complex behaviors. The two main contributions in this direction are: 1. the development of a \u201cwait and chase\u201d model of human mobility having the same two-phase pattern as animal foraging but with a greater propensity of local stays in place and therefore a less dispersed general behavior; 2. the introduction of a mobility paradigm for the neurons of a multilayer neural network and a methodology to train these new kind of networks to develop a collective behavior. The lead idea is that neurons move toward the most informative mates to better learn how to fulfill their part in the overall functionality of the network. With these specific implementations we have pursued the general goal of attributing both a cognitive and a physical meaning to the intentionality so as to be able in a near future to speak of intentionality as an additional potential in the dynamics of the masses (both at the micro and a the macro-scale), and of communication as another network in the force field. This could be intended as a step ahead in the track opened by the past century physicists with the coupling of thermodynamic and Shannon entropies in the direction of unifying cognitive and physical laws