Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Measurements, Models, Systems and Design

531 s.

Transmission strategies for wireless energy harvesting nodes

Over the last few decades, transistor miniaturization has enabled a tremendous increase in the processing capability of commercial electronic devices, which, combined with the reduction of production costs, has tremendously fostered the usage of the Information and communications Technologies (ICTs) both in terms of number of users and required data rates. In turn, this has led to a tremendous increment in the energetic demand of the ICT sector, which is expected to further grow during the upcoming years, reaching unsustainable levels of greenhouse gas emissions as reported by the European Council. Additionally, the autonomy of battery operated devices is getting reduced year after year since battery technology has not evolved fast enough to cope with the increase of energy consumption associated to the growth of the node¿s processing capability. Energy harvesting, which is known as the process of collecting energy from the environment by different means (e.g., solar cells, piezoelectric generators, etc.), has become a potential technology to palliate both of these problems. However, when energy harvesting modules are placed in wireless communication devices (e.g., sensor nodes or hand-held devices), traditional transmission strategies are no longer applicable because the temporal variations of the node¿s energy availability must be carefully accounted for in the design. Apart from not considering energy harvesting, traditional transmission strategies assume that the transmission radiated power is the unique energy sink in the node. This is a reasonable assumption when the transmission range is large, but it no longer holds for low consumption devices such as sensor nodes that transmit to short distances. As a result, classical transmission strategies become suboptimal in short-range communications with low consumption devices and new strategies should be investigated. Consequently, in this dissertation we investigate and design transmission strategies for Wireless Energy Harvesting Nodes (WEHNs) by paying a special emphasis on the different sinks of energy consumption at the transmitter(s). First, we consider a finite battery WEHN operating in a point-to-point link through a static channel and derive the transmission strategy that minimizes the transmission completion time of a set of data packets that become available dynamically over time. The transmission strategy has to satisfy causality constrains in data transmission and energy consumption, which impose that the node cannot transmit data that is not yet available nor consume energy that has not yet been harvested. Second, we consider a WEHN that has an infinite backlog of data to be transmitted through a point-to-point link in a time-varying linear vector Gaussian channel and study the linear precoding strategy that maximizes the mutual information given an arbitrary distribution of the input symbols while satisfying the Energy Causality Constraints (ECCs) at the transmitter. Next, apart from the transmission radiated power, we take into account additional energy sinks in the power consumption model and analyze how these energy sinks affect to the transmission strategy that maximizes the mutual information achieved by a WEHN operating in a point-to-point link. Finally, we consider multiple transmitter and receiver pairs sharing a common channel and investigate a distributed power allocation strategy that aims at maximizing the network sum-rate by taking into account the energy availability in the different transmitters and a generalized power consumption model.Durant les últimes dècades, la miniaturització del transistor i la reducció dels seus costos de fabricació han provocat un augment substancial del nombre de terminals de comunicacions i del tràfic de dades requerit per aquests dispositius. Així doncs, el consum energètic del sector de les Tecnologies de la Informació i Comunicacions ha incrementat notablement. A més a més, s’espera que aquest consum segueixi creixent durant els propers anys arribant a nivells insostenibles d’emissions de gasos d’efecte hivernacle segons ha informat el Consell Europeu. D’altra banda, la tecnologia de les bateries no ha evolucionat suficientment ràpid com per fer front a l’augment del consum energètic associat al creixement de la capacitat de processament dels dispositius. Això ha ocasionat que l’autonomia dels dispositius que operen amb bateries empitjori any rere any. Les energies renovables (per exemple, energia solar, cinètica, etc.) s’han convertit en una solució potencial per pal•liar aquests dos problemes. No obstant això, quan els dispositius de comunicació sense fils incorporen mòduls de captació d’energies renovables, les estratègies tradicionals de transmissió deixen de ser vàlides, ja que les variacions temporals de la disponibilitat d’energia en el dispositiu han de ser considerades en el disseny. A més a més, les estratègies de transmissió tradicionals assumeixen que la potència radiada és l’única font de consum energètic del node. Aquesta és una suposició raonable per distàncies de transmissió llargues, però deixa de ser vàlida quan es consideren dispositius de baix consum que transmeten en distàncies curtes. Com a resultat, les estratègies de transmissió clàssiques són subòptimes en comunicacions de curt abast amb dispositius de baix consum i per això, s’han d’investigar noves estratègies. En conseqüència, en aquesta tesi doctoral s’investiguen i es dissenyen noves estratègies de transmissió per nodes sense fils que operen amb energies renovables (WEHN) posant un èmfasi especial en les diferents fonts de consum d’energia en el transmissor. En primer lloc, la tesi investiga l’estratègia de transmissió en un enllaç¸ punt a punt a través d’un canal estàtic que minimitza el temps de transmissió d’un conjunt de paquets de dades que s’adquireixen al llarg del temps. L’estratègia de transmissió ha de satisfer les limitacions per causalitat en la transmissió de dades i en el consum d’energia les quals imposen que el node no pot transmetre dades que no han estat encara obtingudes o utilitzar energia que encara no ha estat adquirida. En segon lloc, es considera un WEHN que sempre disposa de dades per a transmetre a través d’un enllaç¸ punt a punt en un canal lineal Gaussià amb variacions temporals. En aquest escenari i, també, donada una distribució arbitrària dels símbols d’entrada, s’estudia l’estratègia de precodificació lineal que maximitza la informació mútua alhora que satisfà la causalitat d’energia en el transmissor. A continuació, a part de la potència radiada en transmissió, s’inclouen en el model de consum energètic els costos d’activació per accés al canal i per portadora. Donat aquest model, s’analitza com aquestes fonts de consum addicionals afecten a l’estratègia de transmissió que maximitza la informació mútua d’un WEHN que opera en un enllaç punt a punt. Finalment, la tesi considera diversos parells transmissor i receptor que comparteixen un canal comú i investiga una estratègia d’assignació de potència distribuïda la qual té com a objectiu maximitzar la suma de les taxes de transmissió dels diferents nodes tenint en compte la disponibilitat energètica en cada transmissor que està basada en un model de consum energètic generalitzat

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Research on Information Flow Topology for Connected Autonomous Vehicles

Information flow topology plays a crucial role in connected autonomous vehicles (CAVs). It describes how CAVs communicate and exchange information with each other. It predominantly affects the platoon\u27s performance, including the convergence time, robustness, stability, and scalability. It also dramatically affects the controller design of CAVs. Therefore, studying information flow topology is necessary to ensure the platoon\u27s stability and improve its performance. Advanced sliding mode controllers and optimisation strategies for information flow topology are investigated in this project. Firstly, the impact of information flow topology on the platoon is studied regarding tracking ability, fuel economy and driving comfort. A Pareto optimal information flow topology offline searching approach is proposed using a non-dominated sorting genetic algorithm (NSGA-II) to improve the platoon\u27s overall performance while ensuring stability. Secondly, the concept of asymmetric control is introduced in the topological matrix. For a linear CAVs model with time delay, a sliding mode controller is designed to target the platoon\u27s tracking performance. Moreover, the Lyapunov analysis is used via Riccati inequality to guarantee the platoon\u27s internal stability and input-to-output string stability. Then NSGA-II is used to find the homogeneous Pareto optimal asymmetric degree to improve the platoon\u27s performance. A similar approach is designed for a nonlinear CAVs model to find the Pareto heterogeneous asymmetric degree and improve the platoon\u27s performance. Thirdly, switching topology is studied to better deal with the platoon\u27s communication problems. A two-step switching topology framework is introduced. In the first step, an offline Pareto optimal topology search with imperfect communication scenarios is applied. The platoon\u27s performance is optimised using a multi-objective evolutionary algorithm based on decomposition (MOEA/D). In the second step, the optimal topology is switched and selected from among the previously obtained Pareto optimal topology candidates in real-time to minimise the control cost. For a continuous nonlinear heterogeneous platoon with actuator faults, a sliding mode controller with an adaptive mechanism is developed. Then, the Lyapunov approach is applied to the platoon\u27s tracking error dynamics, ensuring the systems uniformly ultimately bounded stability and string stability. For a discrete nonlinear heterogeneous platoon with packet loss, a discrete sliding mode controller with a double power reaching law is designed, and a modified MOEA/D with two opposing adaptive mechanisms is applied in the two-step framework. Simulations verify all the proposed controllers and frameworks, and experiments also test some. The results show the proposed strategy\u27s effectiveness and superiority in optimising the platoon\u27s performance with multiple objectives

Applied Methuerstic computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC