Reactive approach for automating exploration and exploitation in ant colony optimization

Ant colony optimization (ACO) algorithms can be used to solve nondeterministic polynomial hard problems. Exploration and exploitation are the main mechanisms in controlling search within the ACO. Reactive search is an alternative technique to maintain the dynamism of the mechanics. However, ACO-based reactive search technique has three (3) problems. First, the memory model to record previous search regions did not completely transfer the neighborhood structures to the next iteration which leads to arbitrary restart and premature local search. Secondly, the exploration indicator is not robust due to the difference of magnitudes in distance matrices for the current population. Thirdly, the parameter control techniques that utilize exploration indicators in their feedback process do not consider the problem of indicator robustness. A reactive ant colony optimization (RACO) algorithm has been proposed to overcome the limitations of the reactive search. RACO consists of three main components. The first component is a reactive max-min ant system algorithm for recording the neighborhood structures. The second component is a statistical machine learning mechanism named ACOustic to produce a robust exploration indicator. The third component is the ACO-based adaptive parameter selection algorithm to solve the parameterization problem which relies on quality, exploration and unified criteria in assigning rewards to promising parameters. The performance of RACO is evaluated on traveling salesman and quadratic assignment problems and compared with eight metaheuristics techniques in terms of success rate, Wilcoxon signed-rank, Chi-square and relative percentage deviation. Experimental results showed that the performance of RACO is superior than the eight (8) metaheuristics techniques which confirmed that RACO can be used as a new direction for solving optimization problems. RACO can be used in providing a dynamic exploration and exploitation mechanism, setting a parameter value which allows an efficient search, describing the amount of exploration an ACO algorithm performs and detecting stagnation situations

Architectures and protocols for sub-wavelength optical networks: contributions to connectionless and connection-oriented data transport

La ràpida evolució d’Internet i l’àmplia gamma de noves aplicacions (per exemple, multimèdia, videoconferència, jocs en línia, etc.) ha fomentat canvis revolucionaris en la manera com ens comuniquem. A més, algunes d’aquestes aplicacions demanden grans quantitats de recursos d’ample de banda amb diversos requeriments de qualitat de servei (QoS). El desenvolupament de la multiplexació per divisió de longitud d’ona (WDM) en els anys noranta va fer molt rendible la disponibilitat d’ample de banda. Avui dia, les tecnologies de commutació òptica de circuits són predominants en el nucli de la xarxa, les quals permeten la configuració de canals (lightpaths) a través de la xarxa. No obstant això, la granularitat d’aquests canals ocupa tota la longitud d’ona, el que fa que siguin ineficients per a proveir canals de menor ample de banda (sub-longitud d’ona). Segons la comunitat científica, és necessari augmentar la transparència dels protocols, així com millorar l’aprovisionament d’ample de banda de forma dinàmica. Per tal de fer això realitat, és necessari desenvolupar noves arquitectures. La commutació òptica de ràfegues i de paquets (OBS/OPS), són dues de les tecnologies proposades. Aquesta tesi contribueix amb tres arquitectures de xarxa destinades a millorar el transport de dades sub-longitud d’ona. En primer lloc, aprofundim en la naturalesa sense connexió en OBS. En aquest cas, la xarxa incrementa el seu dinamisme a causa de les transmissions a ràfega. A més, les col·lisions entre ràfegues degraden el rendiment de la xarxa fins i tot a càrregues molt baixes. Per fer front a aquestes col·lisions, es proposa un esquema de resolució de col·lisions pro actiu basat en un algorisme d’encaminament i assignació de longitud d’ona (RWA) que balanceja de forma automàtica i distribuïda la càrrega en la xarxa. En aquest protocol, el RWA i la transmissió de ràfegues es basen en l’explotació i exploració de regles de commutació que incorporen informació sobre contencions i encaminament. Per donar suport a aquesta arquitectura, s’utilitzen dos tipus de paquets de control per a l’encaminament de les ràfegues i l’actualització de les regles de commutació, respectivament. Per analitzar els beneficis del nou algorisme, s’utilitzen quatre topologies de xarxa diferents. Els resultats indiquen que el mètode proposat millora en diferents marges la resta d’algorismes RWA en funció de la topologia i sense penalitzar altres paràmetres com el retard extrem a extrem. La segona contribució proposa una arquitectura híbrida sense i orientada a connexió sobre la base d’un protocol de control d’accés al medi (MAC) per a xarxes OBS (DAOBS). El MAC ofereix dos mètodes d’accés: arbitratge de cua (QA) per a la transmissió de ràfegues sense connexió, i pre-arbitratge (PA) per serveis TDM orientats a connexió. Aquesta arquitectura permet una àmplia gamma d’aplicacions sensibles al retard i al bloqueig. Els resultats avaluats a través de simulacions mostren que en l’accés QA, les ràfegues de més alta prioritat tenen garantides zero pèrdues i latències d’accés molt baixes. Pel que fa a l’accés PA, es reporta que la duplicació de la càrrega TDM augmenta en més d’un ordre la probabilitat de bloqueig, però sense afectar en la mateixa mesura les ràfegues sense connexió. En aquest capítol també es tracten dos dels problemes relacionats amb l’arquitectura DAOBS i el seu funcionament. En primer lloc, es proposa un model matemàtic per aproximar el retard d’accés inferior i superior com a conseqüència de l’accés QA. En segon lloc, es formula matemàticament la generació i optimització de les topologies virtuals que suporten el protocol per a l’escenari amb tràfic estàtic. Finalment, l’última contribució explora els beneficis d’una arquitectura de xarxa òptica per temps compartit (TSON) basada en elements de càlcul de camins (PCE) centralitzats per tal d’evitar col·lisions en la xarxa. Aquesta arquitectura permet garantir l’aprovisionament orientat a connexió de canals sub-longitud d’ona. En aquest capítol proposem i simulem tres arquitectures GMPLS/PCE/TSON. A causa del enfocament centralitzat, el rendiment de la xarxa depèn en gran mesura de l’assignació i aprovisionament de les connexions. Amb aquesta finalitat, es proposen diferents algorismes d’assignació de ranures temporals i es comparen amb les corresponents formulacions de programació lineal (ILP) per al cas estàtic. Per al cas de tràfic dinàmic, proposem i avaluem mitjançant simulació diferents heurístiques. Els resultats mostren els beneficis de proporcionar flexibilitat en els dominis temporal i freqüencial a l’hora d’assignar les ranures temporals.The rapid evolving Internet and the broad range of new data applications (e.g., multimedia, video-conference, online gaming, etc.) is fostering revolutionary changes in the way we communicate. In addition, some of these applications demand for unprecedented amounts of bandwidth resources with diverse quality of service (QoS). The development of wavelength division multiplexing (WDM) in the 90's made very cost-effective the availability of bandwidth. Nowadays, optical circuit switching technologies are predominant in the core enabling the set up of lightpaths across the network. However, full-wavelength lightpath granularity is too coarse, which results to be inefficient for provisioning sub-wavelength channels. As remarked by the research community, an open issue in optical networking is increasing the protocol transparency as well as provisioning true dynamic bandwidth allocation at the network level. To this end, new architectures are required. Optical burst/packet switching (OBS/OPS) are two such proposed technologies under investigation. This thesis contributes with three network architectures which aim at improving the sub-wavelength data transport from different perspectives. First, we gain insight into the connectionless nature of OBS. Here, the network dynamics are increased due to the short-lived burst transmissions. Moreover, burst contentions degrade the performance even at very low loads. To cope with them, we propose a proactive resolution scheme by means of a distributed auto load-balancing routing and wavelength assignment (RWA) algorithm for wavelength-continuity constraint networks. In this protocol, the RWA and burst forwarding is based on the exploitation and exploration of switching rule concentration values that incorporate contention and forwarding desirability information. To support such architecture, forward and backward control packets are used in the burst forwarding and updating rules, respectively. In order to analyze the benefits of the new algorithm, four different network topologies are used. Results indicate that the proposed method outperforms the rest of tested RWA algorithms at various margins depending on the topology without penalizing other parameters such as end-to-end delay. The second contribution proposes a hybrid connectionless and connection-oriented architecture based on a medium access control (MAC) protocol for OBS networks (DAOBS). The MAC provides two main access mechanisms: queue arbitrated (QA) for connectionless bursts and pre-arbitrated (PA) for TDM connection-oriented services. Such an architecture allows for a broad range of delay-sensitive applications or guaranteed services. Results evaluated through simulations show that in the QA access mode highest priority bursts are guaranteed zero losses and very low access latencies. Regarding the PA mode, we report that doubling the offered TDM traffic load increases in more than one order their connection blocking, slightly affecting the blocking of other connectionless bursts. In this chapter, we also tackle two of the issues related with the DAOBS architecture and its operation. Firstly, we model mathematically the lower and upper approximations of the access delay as a consequence of the connectionless queue arbitrated access. Secondly, we formulate the generation of the virtual light-tree overlay topology for the static traffic case.Postprint (published version

Solution of combined heat and power economic dispatch problem using genetic algorithm.

Masters Degree. University of KwaZulu-Natal, Durban.The combination of heat and power constitutes a system that provides electricity and thermal energy concurrently. Its high efficiency and significant emission reduction makes it an outstanding prospect in the future of energy production and transmission. The broad application of combined heat and power units requires the joint dispatch of power and heating systems, in which the modelling of combined heat and power units plays a vital role. This research paper employed genetic algorithm, artificial bee colony, differential evolution, particle swarm optimization and direct solution algorithms to evaluate the cost function as well as output decision variables of heat and power in a system that has simple cycle cogeneration unit with quadratic cost function. The system was first modeled to determine the various parameters of combined heat and power units in order to solve the economic dispatch problem with direct solution algorithm. In order for modelling to be done, a general structure of combined heat and power must be defined. The system considered in this research consists of four test units, i.e. two conventional power units, one combined heat and power unit and one heat-only unit. These algorithms were applied to on the research data set to determine the required decision variables while taking into account the power and heat units, operation bound of power and heat-only units as well as feasible operation region of the cogeneration unit. Power and heat output decision variables plus cost functions from Genetic Algorithm, differential evolution, Particle Swarm Optimization and artificial bee colony were determined using codes. Also, the decision variables and cost function value were obtained by calculations using direct solution algorithm. The findings of the research paper show that there are different ways in which combined heat and power economic dispatch variables can be determined, which include genetic algorithm, differential evolution, artificial bee colony, particle swarm optimization and direct solution algorithms. However, each solution method allows for different combined heat and power output decision variables to be found, with some of the methods (particle swarm optimization and artificial bee colony) having setbacks such as: large objective function values, slower convergence and large number solution. The analysis revealed that the differential evolution algorithm is a viable alternative to solving combined heat and power problems. This is due in most part to its faster convergence, minimum cost function value, and high quality solution which are diverse and widespread, more as a result of its effective search capability than genetic algorithm, particle swarm optimization, direct solution and artificial bee colony algorithms. The methods investigated in this research paper can be used and expanded on to create useful and accurate technique of solving combined heat and power problems

Novel approach for integrated biomass supply chain synthesis and optimisation

Despite looming energy crises, fossil resources are still widely used for energy and chemical production. Growing awareness of the environmental impact from fossil fuels has made sustainability one of the main focuses in research and development. Towards that end, biomass is identified as a promising renewable source of carbon that can potentially replace fossil resources in energy and chemical productions. Although many researches on converting biomass to value-added product have been done, biomass is still considered underutilised in the industry. This is mainly due to challenges in the logistic and processing network of biomass. An integrated biomass supply chain synthesis and optimisation are therefore important. Thus, the ultimate goal of this thesis is to develop a novel approach for an integrated biomass supply chain. Firstly, a multiple biomass corridor (MBC) concept is presented to integrate various biomass and processing technologies into existing biomass supply chain system in urban and developed regions. Based on this approach, a framework is developed for the synthesis of a more diversified and economical biomass supply chain system. The work is then extended to consider the centralisation and decentralisation of supply chain structure. In this manner, P-graph-aided decomposition approach (PADA) is proposed, whereby it divides the complex supply chain problem into two smaller sub-problems – the processing network is solved via mixed-integer linear programming (MILP) model, whereas the binaries-intensive logistic network configuration is determined through P-graph framework. As existing works often focus on supply chain synthesis in urban regions with well-developed infrastructure, resources integrated network (RIN) – a novel approach for the synthesis of integrated biomass supply chain in rural and remote regions is introduced to enhance rural economies. This approach incorporates multiple resources (i.e. bioresources, food commodities, rural communities’ daily needs) into the value chain and utilises inland water system as the mode of transport, making the system more economically feasible. It extends the MBC approach for technology selection and adopts vehicle routing problem (VRP) for inland water supply and delivery network. To evaluate the performance of the proposed integrated biomass supply chain system, a FANP-based (fuzzy analytical network process) sustainability assessment tool is established. A framework is proposed to derive sustainability index (SI) from pairwise comparison done by supply chain stakeholders to assess the sustainability of a system. Fuzzy limits are introduced to reduce uncertainties in human judgment while conducting the pairwise comparison. To design a sustainable integrated biomass supply chain, a FANP-aided, a novel multiple objectives optimisation framework is proposed. This approach transforms multiple objective functions into single objective function by prioritising each of the objective through the FANP framework. The multiple objectives are then normalised via max-min aggregation to ensure the trade-off between objectives is performed on the same scale. At the end of this thesis, viable future works of the whole programme is presented for consideration

Parallelizing a nondeterministic optimization algorithm

This research explores the idea that for certain optimization problems there is a way to parallelize the algorithm such that the parallel efficiency can exceed one hundred percent. Specifically, a parallel compiler, PC, is used to apply shortcutting techniquest to a metaheuristic Ant Colony Optimization (ACO), to solve the well-known Traveling Salesman Problem (TSP) on a cluster running Message Passing Interface (MPI). The results of both serial and parallel execution are compared using test datasets from the TSPLIB

Demand side management of plug-in electric vehicles and coordinated unit commitment: A novel parallel competitive swarm optimization method

Decreasing initial costs, the increased availability of charging infrastructure and favorable policy measures have resulted in the recent surge in plug-in electric vehicle (PEV) ownerships. PEV adoption increases electricity consumption from the grid that could either exacerbate electricity supply shortages or smooth demand curves. The optimal coordination and commitment of power generation units while ensuring wider access of PEVs to the grid are, therefore, important to reduce the cost and environmental pollution from thermal power generation systems, and to transition to a smarter grid. However, flexible demand side management (DSM) considering the stochastic charging behavior of PEVs adds new challenges to the complex power system optimization, and makes existing mathematical approaches ineffective. In this research, a novel parallel competitive swarm optimization algorithm is developed for solving large-scale unit commitment (UC) problems with mixed integer variables and multiple constraints typically found in PEV integrated grids. The parallel optimization framework combines binary and real-valued competitive swarm optimizers for solving the UC problem and demand side management of PEVs simultaneously. Numerical case studies have been conducted with multiple scales of unit numbers and various demand side management strategies of plug-in electric vehicles. The results show superior performance of proposed parallel competitive swarm optimization based method in successfully solving the proposed complex optimization problem. The flexible demand side management strategies of plug-in electric vehicles have shown large potentials in bringing considerable economic benefit

Large-scale dynamic observation planning for unmanned surface vessels

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2007.Includes bibliographical references (p. 129-134).With recent advances in research and technology, autonomous surface vessel capabilities have steadily increased. These autonomous surface vessel technologies enable missions and tasks to be performed without the direction of human operators, and have changed the way scientists and engineers approach problems. Because these robotic devices can work without manned guidance, they can execute missions that are too difficult, dangerous, expensive, or tedious for human operators to attempt. The United States government is currently expanding the use of autonomous surface vessel technologies through the United States Navy's Spartan Scout unmanned surface vessel (USV) and NASA's Ocean-Atmosphere Sensor Integration System (OASIS) USV. These USVs are well-suited to complete monotonous, dangerous, and time-consuming missions. The USVs provide better performance, lower cost, and reduced risk to human life than manned systems. In this thesis, we explore how to plan multiple USV observation schedules for two significant notional observation scenarios, collecting water temperatures ahead of the path of a hurricane, and collecting fluorometer readings to observe and track a harmful algal bloom.(cont.) A control system must be in place that coordinates a fleet of USVs to targets in an efficient manner. We develop three algorithms to solve the unmanned surface vehicle observation-planning problem. A greedy construction heuristic runs fastest, but produces suboptimal plans; a 3-phase algorithm which combines a greedy construction heuristic with an improvement phase and an insertion phase, requires more execution time, but generates significantly better plans; an optimal mixed integer programming algorithm produces optimal plans, but can only solve small problem instances.by John V. Miller.S.M

Resources management architecture and algorithms for virtualized IVR applications in cloud environment

Interactive Voice Response (IVR) applications are ubiquitous nowadays. IVR is a telephony technology that allows interactions with a wide range of automated information systems via a telephone keypad or voice commands. Cloud computing is a newly emerging paradigm that hosts and provides services over the Internet with many inherent benefits. It has three major service models: Infrastructure as a service (IaaS), Platform as a service (PaaS), and Software as a Service (SaaS). Cloud computing is based on the virtualization technology that enables the co-existence of entities in general on the same substrates. These entities may be operating systems co-existing on the same hardware, applications co-existing on the same operating system, or even full-blown networks co-existing on the same routers. The key benefit is efficiency through the sharing of physical resources. Several multimedia applications are provided in cloud environments nowadays. However, to the best of our knowledge, there is no architecture that creates and manages IVR applications in cloud environment. Therefore, we propose to develop a new virtualized architecture that can create, deploy and manage IVR applications in cloud environment. We also propose two new algorithms for resources management and task scheduling as an essential part of resource sharing in such environment

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications