This work deals with swarm intelligence, strictly speaking particle swarm intelligence. It shortly describes questions of optimization and some optimization techniques. Part of this work is recherché of variants of particle swarm optimization algorithm. These algorithms are mathematically described. Their advantages or disadvantages in comparison with the basic PSO algorithm are mentioned. The second part of this work describes mQPSO algorithm and created modification mQPSOPC. Described algorithms are compared with each other and with another evolution algorithm on several tests.

This research touches the basics of a clothes-trading process. The process occurs as an outsideparty is being politically absorbed by a mainstream party which aim is to oust the smaller party from the electoral arena. The outside-party would ensure survival by dismiss its policy dimension, thus moving towards an opposite strategic direction away from the incoming mainstream party. The toolkit is taking from the PSO-theory by Bonnie M. Meguid (2008) in order to describe the clothes-trading process by each step as a party strategy. It is a defeat fire with fire type of conflict, with the end not yet discovered, but assumed to be a total exchange between the parties’ issue ownership. So far it is too early to predict the outcome. Further studies have to be made on the future elections in order to elaborate the clothes-trading process more in detail. But the research has set the basics of how and why such a process would occur.

On planet Earth, fossil fuels are the most important sources of energy. However, these resources are limited and being depleted dramatically throughout last decades. Finding feasible substitutes of these resources is an essential duty for humanity. Fortunately, Mother Nature is providing us a number of good solutions for this crucial threat against our planet. Solar irradiance, wind blowing, oceanic and maritime waves are natural resources of energy that are capable of completely covering the annual consumption of all inhabitants on the Earth. In this research a set of components including “Northern Power NPS 100-24” wind generators, “Kyocera KD 145 SX-UFU” PV arrays, “Gildemeister 10kW-40kWh Cellcube” battery bank and HOMER bi-directional converter system were considered and successfully applied on HOMER tool and Particle Swarm Optimization (PSO) method. The main design goals of the presented hybrid system are to use 100% renewable energy resources in the commercial sector, where all power is produced in the immediate vicinity of the business place, adding strong advertising values to the setup. In order to supply hourly required load for a grocery store   (1000 ) in Malmö city with 115 kW peak load and 2002 kWh/d with maximum 0.1% unmet, the system was optimized to achieve minimum Levelized Cost of Energy (LCOE) and the lowest Net Present Cost (NPC). The HOMER simulation for quantitative analysis, along with a Particle Swarm Optimization (PSO) solution method is proposed and the results are compared. The results show that an optimized hybrid system with 3.12  LCOE, and power production of 28.5% by PV arrays and 71.5% by wind generators, is the best practice for this case study. / De fossila bränslena är idag de viktigaste energikällorna på jorden. Dessa resurser är dock begränsade och har utarmats i en allt högre takt under de senaste decennierna. Att hitta möjliga ersättare för dessa resurser är därför viktigt. Lyckligtvis tillhandahåller naturen ett antal bra lösningar för detta avgörande hot mot vår planet. Solstrålning, vind, havsströmmar och -vågor är naturliga resurser av energi som kan täcka hela den årliga globala förbrukningen. I den här rapporten studeras ett hybridsystem bestående av Northern Power NPS 100-24 vindkraftverk, Kyocera KD 145 SX-UFU solcellerspaneler, Gildemeister 10kW-40kWh Cellcube batteribank och HOMER dubbelriktad växelriktare. Detta modellerades och optimerades dels i mjukvaran HOMER, dels via optimeringsmetoden Particle Swarm Optimaization (PSO). Det övergripande designkravet för det presenterade hybridsystemet är att använda 100% förnyelsebar energi i en kommersiell verksamhet, där all elektricitet produceras i närhet av verksamheten, vilket kan ge tydliga marknadsföringsvärden till installationen. För att kunna möta energibehovet varje timme för en livsmedelsbutik (1000 ) i Malmö med 115 kW toppförbrukning och 2002 kWh/dag, med maximalt 0,1% ej mött behov, optimerades systemet för att uppnå minimal energikostnad (Levelized Cost of Energy, LCOE) och lägsta nettonuvärde (Net Present Cost, NPC). En HOMER-simulering för kvantitativ analys, tillsammans med en PSO-optimering, har genomförts och resultaten har jämförts. Resultaten visar att ett optimerat hybridsystem med LCOE på 3,12 SEK/kWh, där solceller står för 28,5% av kraftproduktionen och vindkraftverk för 71,5%, är den bästa lösningen för denna fallstudie.

This research is in three parts with different although related objectives. The first part developed an efficient, modified simulated annealing algorithm to solve the D-optimal (determinant maximization) design problem for 2-way polynomial regression with correlated observations. Much of the previous work in D-optimal design for regression models with correlated errors focused on polynomial models with a single predictor variable, in large part because of the intractability of an analytic solution. In this research, we present an improved simulated annealing algorithm, providing practical approaches to specifications of the annealing cooling parameters, thresholds and search neighborhoods for the perturbation scheme, which finds approximate D-optimal designs for 2-way polynomial regression for a variety of specific correlation structures with a given correlation coefficient. Results in each correlated-errors case are compared with the best design selected from the class of designs that are known to be D-optimal in the uncorrelated case: annealing results had generally higher D-efficiency than the best comparison design, especially when the correlation parameter was well away from 0. The second research objective, using Balanced Incomplete Block Designs (BIBDs), wasto construct weakly universal optimal block designs for the nearest neighbor correlation structure and multiple block sizes, for the hub correlation structure with any block size, and for circulant correlation with odd block size. We also constructed approximately weakly universal optimal block designs for the block-structured correlation. Lastly, we developed an improved Particle Swarm Optimization(PSO) algorithm with time varying parameters, and solved D-optimal design for linear regression with it. Then based on that improved algorithm, we combined the non-linear regression problem and decision making, and developed a nested PSO algorithm that finds (nearly) optimal experimental designs with each of the pessimistic criterion, index of optimism criterion, and regret criterion for the Michaelis-Menten model and logistic regression model.

This research is in three parts with different although related objectives. The first part developed an efficient, modified simulated annealing algorithm to solve the D-optimal (determinant maximization) design problem for 2-way polynomial regression with correlated observations. Much of the previous work in D-optimal design for regression models with correlated errors focused on polynomial models with a single predictor variable, in large part because of the intractability of an analytic solution. In this research, we present an improved simulated annealing algorithm, providing practical approaches to specifications of the annealing cooling parameters, thresholds and search neighborhoods for the perturbation scheme, which finds approximate D-optimal designs for 2-way polynomial regression for a variety of specific correlation structures with a given correlation coefficient. Results in each correlated-errors case are compared with the best design selected from the class of designs that are known to be D-optimal in the uncorrelated case: annealing results had generally higher D-efficiency than the best comparison design, especially when the correlation parameter was well away from 0. The second research objective, using Balanced Incomplete Block Designs (BIBDs), wasto construct weakly universal optimal block designs for the nearest neighbor correlation structure and multiple block sizes, for the hub correlation structure with any block size, and for circulant correlation with odd block size. We also constructed approximately weakly universal optimal block designs for the block-structured correlation. Lastly, we developed an improved Particle Swarm Optimization(PSO) algorithm with time varying parameters, and solved D-optimal design for linear regression with it. Then based on that improved algorithm, we combined the non-linear regression problem and decision making, and developed a nested PSO algorithm that finds (nearly) optimal experimental designs with each of the pessimistic criterion, index of optimism criterion, and regret criterion for the Michaelis-Menten model and logistic regression model.

In recent years, due to the international competition, soaring cost of land and personnel, aging population, low birth rate¡Ketc, resulting in the recession of the competitiveness of traditional industries in Taiwan. Manpower is needed to monitor the manufacturing process, however, only a worker can¡¦t endure such kind of repetitive workload; on the other hand, it¡¦s not economic to hire more workers to share the workload. Therefore, we expect robots to replace human resources in the manufacturing process. With the advance of science and technology, the mobile robot must equip intelligent judgments. For instance, obstacle avoidance, a way to avoid damage being caused by collision with the obstacles. In general, there are some tables, chairs and the electrical equipment in the office. In the dynamic obstacles case, the robot is effective and immediate response to determine while the people are walking, the staff members to maintain a work efficiency, and security through complex environments. It is the primary topics of discussion. Another important function is path planning, such as the patrol, and the global path planning. Let the mobile robot reach the specified target successfully. In the remote monitoring case, let users know the actual situation of the mobile robot. For example, records of patrol information and specify the action type to move. Therefore, this thesis presents a project of the indoor integrated intelligent mobile robots, including obstacle avoidance, path planning, and remote monitoring of the unknown environment.

One of the major problems encountered in wireless location is the effect caused by non-line of sight (NLOS) propagation. When the direct path from the mobile station (MS) to base stations (BSs) is blocked by obstacles or buildings, the signal arrival times will delay. That will make the signal measurements include an error due to the excess path propagation. If we use the NLOS signal measurements for localization, that will make the system localization performance reduce greatly. In the thesis, a time-of-arrival (TOA) based location system with NLOS mitigation algorithm is proposed. The proposed method uses least squares-support vector machine (LS-SVM) with optimal parameters selection by particle swarm optimization (PSO) for establishing regression model, which is used in the estimation of propagation distances and reduction of the NLOS propagation errors. By using a weighted objective function, the estimation results of the distances are combined with suitable weight factors, which are derived from the differences between the estimated measurements and the measured measurements. By applying the optimality of the weighted objection function, the method is capable of mitigating the NLOS effects and reducing the propagation range errors. Computer simulation results in ultra-wideband (UWB) environments show that the proposed NLOS mitigation algorithm can reduce the mean and variance of the NLOS measurements efficiently. The proposed method outperforms other methods in improving localization accuracy under different NLOS conditions.

Thermal power plants dominate electric power generation in Taiwan, which causes high Green House gases (GHG) emissions. CO2 is the most important greenhouse gas that cause global warming and sea-level to rise. This paper faces the relationship between CO2 limitation and power generation expansion planning (GEP) for the utility. Modify Particle Swarm Optimization (MPSO) is presented to determine the generation expansion planning strategy of the utility with independent power providers (IPPs). The utility has to take both the IPPs¡¦ participation and environmental impact into account when a new generation unit is expanded. This problem also takes into account the CO2 reduction and reliability issues, while satisfying all electrical constraints simultaneously from the supply point of view. MPSO scheme was improved to avoid getting a premature answer. Testing results shows that MPSO can offer an efficient way in determining the generation expansion planning. Generation expansion planning is an important decision-making activity in a competitive market, all utilities including IPPs need to maximize the profit while meeting the load demand with a pre-specified reliability criterion. In order to achieve the objective, utilities will perform the generation expansion planning to determine the minimal-cost capacity power addition. For better economy and efficiency, they will consider options of either constructing new generating units or purchasing electricity from other utilities or IPPs.

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Die Optimierung spielt in der Industrie und Technik eine entscheidende Rolle. Für einen Betrieb ist es beispielsweise äußerst wichtig, die zur Verfügung stehenden Ressourcen optimal zu nutzen und Betriebsabläufe effizient zu gestalten. Damit diese Vorhaben umgesetzt werden können, setzt man Methoden der Optimierung ein. Die Zielstellungen werden als eine abstrakte mathematische Aufgabe formuliert und anschließend wird versucht, dieses Problem mit einem Optimierungsverfahren zu lösen. Da die Komplexität der Problemstellungen in der Praxis ansteigt, sind exakte Verfahren in der Regel nicht mehr effizient anwendbar, sodass andere Methoden zum Lösen dieser Aufgaben entwickelt werden müssen, die in angemessener Zeit eine akzeptable Lösung finden. Solche Methoden werden als Approximationsalgorithmen bezeichnet. Im Gegensatz zu den exakten Verfahren ist der Verlauf der Optimierung bei dieser Verfahrensklasse vom Zufall abhängig. Dadurch lassen sich in der Regel keine Konvergenzaussagen beweisen. Dennoch hat sich gezeigt, dass Approximationsalgorithmen viel versprechende Ergebnisse für eine Vielzahl von unterschiedlichen Problemstellungen liefern. Zwei Approximationsalgorithmen werden in dieser Arbeit vorgestellt, untersucht und erweitert. Zum einen steht ein Verfahren im Vordergrund, welches aus Beobachtungen in der Natur entstanden ist. Es gibt Lebewesen, die durch verblüffend einfache Strategien in der Lage sind, komplexe Probleme zu lösen. Beispielsweise bilden Fische Schwärme, um sich vor Fressfeinden zu schützen. Der Fischschwarm kann dabei als selbstorganisierendes System verstanden werden, bei dem die Aktivitäten der einzelnen Fische hauptsächlich von den Bewegungen der Nachbarfische abhängig sind. An diesem erfolgreichen Schwarmverhalten ist der moderne Approximationsalgorithmus der Partikelschwarmoptimierung angelehnt. Weiterhin wird ein ersatzmodellgestütztes Verfahren präsentiert. Der Ausgangspunkt dieses Optimierungsverfahrens ist der Aufbau von Ersatzmodellen, um das Verhalten der Zielfunktionen anhand der bisherigen Auswertungen vorhersagen zu können. Damit so wenig wie möglich Funktionsauswertungen vorgenommen werden müssen, wird bei diesem Verfahren ein hoher Aufwand in die Wahl der Punkte investiert, welche auszuwerten sind. Die vorliegende Diplomarbeit gliedert sich wie folgt. Zunächst werden die mathematischen Grundlagen für das Verständnis der weiteren Ausführungen gelegt. Insbesondere werden multikriterielle Optimierungsaufgaben betrachtet und klassische Lösungsansätze aufgezeigt. Das dritte Kapitel beschäftigt sich mit der Partikelschwarmoptimierung. Dieser „naturanaloge Approximationsalgorithmus“ wird ausführlich dargelegt und analysiert. Dabei stehen die Funktionsweise und der Umgang mit mehreren Zielen und Restriktionen im Vordergrund der Ausarbeitung. Ein ersatzmodellgestütztes Optimierungsverfahren wird im Anschluss darauf vorgestellt und erweitert. Neben der Verfahrensanalyse, ist die Behebung der vorhandenen Schwachstellen ein vorrangiges Ziel dieser Untersuchung. Die eingeführten und implementierten Verfahren werden im fünften Kapitel an geeigneten analytischen und technischen Problemen verifiziert und mit anderen Approximationsalgorithmen verglichen. Anschließend werden Empfehlungen für die Verwendung der Verfahren gegeben. Die gewonnenen Kenntnisse werden im letzten Kapitel zusammengefasst und es wird ein Ausblick für zukünftige Forschungsthemen gegeben