Die vorliegende Arbeit behandelt die Entwicklung des genetischen Starrkörper-Algorithmus (rigid body genetic algorithm, RGBA), und seine Anwendung zur Untersuchung der Kohlenwasserstoff-Adsorption auf der MgO (001) Oberfläche. Die RBGA Methode ist ein modifizierter hybrid-genetischer Algorithmus mit Starrkörper-Optimierung im lokalen Optimierungsschritt. Diese Modifikation führt zu einer großen Vereinfachung des Optimierungsproblems und ermöglicht damit, eine große Anzahl von möglichen Konfigurationen zu analysieren. Die zentrale Annahme der Methode ist, dass die einzelnen Teile des Systems (starrer Körper) während der gesamten globalen Optimierung nicht ihre interne Konfiguration ändern. Daher ist diese Methode ein geeignetes Werkzeug, um Phänomene wie Adsorption zu studieren, in dem alle Teilsysteme - Oberfläche und einzelne Moleküle - ihre interne Struktur bewahren. Der Algorithmus ermöglicht das Auffinden der globalen Minima für die Starrkörper, die dann im nächsten Schritt vollständig optimiert („relaxiert“) werden, um Verformungen aufgrund der Entspannung der Oberfläche und des Adsorbats auszumachen. / The present work was concentrated on developing the Rigid Body Genetic Algorithm (RBGA), and applying it to investigate the hydrocarbon adsorption on the MgO(001) surface. The RBGA method is a modified hybrid genetic algorithm with rigid body optimization at the local optimization step. The modification allows for a vast simplification of the optimization problem, and, in turn, to search a large number of possible configuration. The key assumption of the method is that individual parts of the system (rigid bodies) do not change their internal configuration throughout the global optimization. Therefore, this method is a perfect tool to study phenomena like adsorption, where all the subsystems – surface and individual molecules – preserve their internal structure. The algorithm allows to obtain global minima, which then can be fully optimized and to account for deformations due to the relaxation of the surface and adsorbate molecules.

Piling is expensive but often necessary when building large structures, for example bridges. Some pile types, such as steel core piles, are very costly and it is therefore of great interest to keep the number piles in a pile group to a minimum. This thesis deals with optimization of pile groups with respect to placement, batter and angle of rotation in order to minimize the number of piles. A program has been developed, where two optimization algorithms named Genetic Algorithm and Direct Search, and four objective functions have been used. These have been tested and compared to find the most suitable for pile group optimization. Three real cases, two bridge supports and one culvert, have been studied, using the program.  It has been difficult to draw any clear conclusions since the results have been ambiguous. This is probably because only three cases have been tested and the results are very problemdependent.The outcome depends, for example, on the starting guess and settings for the optimization. However, the results show that the Genetic Algorithm is somewhat more robust in its ability to remove piles than Direct Search and is therefore to prefer in pile group optimization.

Product line planning aims at optimal planning of product variety. In addition, the traditional product line planning problem develops new product lines based on product attributes without considering existing product lines. However, in reality, almost all new product lines evolve from existing product lines, which leads to the product line evolution problem. Product line evolution involves trade-offs between the marketing perspective and engineering perspective. The marketing concern focuses on maximizing utility for customers; the engineering concern focuses on minimizing engineering cost. Utility represents satisfaction experienced by the customers of a product. Engineering cost is the total cost involved in the process of the development of a product line. These two goals are in conflict since the high utility requires high-end product attributes which could increase the engineering cost and vice versa. Rather than aggregating both problems as one single level optimization problem, the marketing and engineering concerns entail a non-collaborative game per se. This research investigates a game-theoretic approach to the product line evolution problem. A leader-follower joint optimization model is developed to leverage conflicting goals of marketing and engineering concerns within a coherent framework of game theoretic optimization. To solve the joint optimization model efficiently, a bi-level nested genetic algorithm is developed. A case study of smart watch product line evolution is reported to illustrate the feasibility and potential of the proposed approach.

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International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, Nevada / In recent years the telemetry community has encountered a growing demand for bandwidth from users and a corresponding loss of spectrum. The Advanced Range Telemetry (ARTM) Program has responded to this situation with an initiative to develop, demonstrate, and improve the management and control of telemetry resources using demand assigned multiple access (DAMA) techniques. This initiative has proceeded along two paths. The first path is in the development of an expert system to facilitate the scheduling of telemetry missions and the deconfliction of their frequencies. This system emphasizes the graphical manipulation of mission data and uses a genetic algorithm to search for an optimal set of mission frequencies. The second path is the development of a bidirectional command and control link to remotely control and configure the frequency of a telemetry link. This link uses the simple network management protocol (SNMP) over a wireless Internet Protocol (IP) network implemented with Digital Communications Network System (DCNS) units.

This thesis aims to review and analyze the scheduling problem in general and Job Shop Scheduling Problem (JSSP) in particular and the solution techniques applied to these problems. The JSSP is the most general and popular hard combinational optimization problem in manufacturing systems. For the past sixty years, an enormous amount of research has been carried out to solve these problems. The literature review showed the inherent shortcomings of solutions to scheduling problems. This has directed researchers to develop hybrid approaches, as no single technique for scheduling has yet been successful in providing optimal solutions to these difficult problems, with much potential for improvements in the existing techniques. The hybrid approach complements and compensates for the limitations of each individual solution technique for better performance and improves results in solving both static and dynamic production scheduling environments. Over the past years, hybrid approaches have generally outperformed simple Genetic Algorithms (GAs). Therefore, two novel priority heuristic rules are developed: Index Based Heuristic and Hybrid Heuristic. These rules are applied to benchmark JSSP and compared with popular traditional rules. The results show that these new heuristic rules have outperformed the traditional heuristic rules over a wide range of benchmark JSSPs. Furthermore, a hybrid GA is developed as an alternate scheduling approach. The hybrid GA uses the novel heuristic rules in its key steps. The hybrid GA is applied to benchmark JSSPs. The hybrid GA is also tested on benchmark flow shop scheduling problems and industrial case studies. The hybrid GA successfully found solutions to JSSPs and is not problem dependent. The hybrid GA performance across the case studies has proved that the developed scheduling model can be applied to any real-world scheduling problem for achieving optimal or near-optimal solutions. This shows the effectiveness of the hybrid GA in real-world scheduling problems. In conclusion, all the research objectives are achieved. Finaly, the future work for the developed heuristic rules and the hybrid GA are discussed and recommendations are made on the basis of the results.

The effect of mutual coupling between elements plays a crucial role to the performance of the antenna arrays. The radiation patterns of antenna arrays will be altered by the coupling effect from the adjacent elements thus reducing the accuracy and resolution in direction finding application. This research developed and validated the novel 3-D Algorithm to calculate the far-field pattern of dipole arrays arranged in three dimensions and in any configuration (both in straight and slanted position). The effect of mutual coupling has been accounted using the Induced EMF method. The computation is performed on 2x2 parallel dipoles and 12 dipoles arranged at the edge of a cube. The results are validated with other electromagnetic techniques such as Method of Moment (MoM) and Finite Difference Time-Domain (FDTD). Then, a 2x2 dipole array is chosen for beam steering and experiment validation due to its ease of implementation and feeding network. The array optimisation to control the pattern is performed using a genetic algorithm. The far-field pattern computed using the 3-D algorithm might be less accurate than other 3-D electromagnetic techniques but its array optimisation is faster and efficient. The simulation and measurement results are in good agreement with each other confirmed the validity of the 3-D algorithm.

This research is focused on the integration problem of process planning and scheduling in steel heat treatment operations environment using artificial intelligent techniques that are capable of dealing with such problems. This work addresses the issues involved in developing a suitable methodology for scheduling heat treatment operations of steel. Several intelligent algorithms have been developed for these propose namely, Genetic Algorithm (GA), Sexual Genetic Algorithm (SGA), Genetic Algorithm with Chromosome differentiation (GACD), Age Genetic Algorithm (AGA), and Mimetic Genetic Algorithm (MGA). These algorithms have been employed to develop an efficient intelligent algorithm using Algorithm Portfolio methodology. After that all the algorithms have been tested on two types of scheduling benchmarks. To apply these algorithms on heat treatment scheduling, a furnace model is developed for optimisation proposes. Furthermore, a system that is capable of selecting the optimal heat treatment regime is developed so the required metal properties can be achieved with the least energy consumption and the shortest time using Neuro-Fuzzy (NF) and Particle Swarm Optimisation (PSO) methodologies. Based on this system, PSO is used to optimise the heat treatment process by selecting different heat treatment conditions. The selected conditions are evaluated so the best selection can be identified. This work addresses the issues involved in developing a suitable methodology for developing an NF system and PSO for mechanical properties of the steel. Using the optimisers, furnace model and heat treatment system model, the intelligent system model is developed and implemented successfully. The results of this system were exciting and the optimisers were working correctly.

The focus of this thesis is to develop a generic approach for solving reliability design optimisation problems which could be applicable to a diverse range of real engineering systems. The basic problem in optimal reliability design of a system is to explore the means of improving the system reliability within the bounds of available resources. Improving the reliability reduces the likelihood of system failure. The consequences of system failure can vary from minor inconvenience and cost to significant economic loss and personal injury. However any improvements made to the system are subject to the availability of resources, which are very often limited. The objective of the design optimisation problem analysed in this thesis is to minimise system unavailability (or unreliability if an unrepairable system is analysed) through the manipulation and assessment of all possible design alterations available, which are subject to constraints on resources and/or system performance requirements. This thesis describes a genetic algorithm-based technique developed to solve the optimisation problem. Since an explicit mathematical form can not be formulated to evaluate the objective function, the system unavailability (unreliability) is assessed using the fault tree method. Central to the optimisation algorithm are newly developed fault tree modification patterns (FTMPs). They are employed here to construct one fault tree representing all possible designs investigated, from the initial system design specified along with the design choices. This is then altered to represent the individual designs in question during the optimisation process. Failure probabilities for specified design cases are quantified by employing Binary Decision Diagrams (BDDs). A computer programme has been developed to automate the application of the optimisation approach to standard engineering safety systems. Its practicality is demonstrated through the consideration of two systems of increasing complexity; first a High Integrity Protection System (HIPS) followed by a Fire Water Deluge System (FWDS). The technique is then further-developed and applied to solve problems of multi-phased mission systems. Two systems are considered; first an unmanned aerial vehicle (UAV) and secondly a military vessel. The final part of this thesis focuses on continuing the development process by adapting the method to solve design optimisation problems for multiple multi-phased mission systems. Its application is demonstrated by considering an advanced UAV system involving multiple multi-phased flight missions. The applications discussed prove that the technique progressively developed in this thesis enables design optimisation problems to be solved for systems with different levels of complexity. A key contribution of this thesis is the development of a novel generic optimisation technique, embedding newly developed FTMPs, which is capable of optimising the reliability design for potentially any engineering system. Another key and novel contribution of this work is the capability to analyse and provide optimal design solutions for multiple multi-phase mission systems. Keywords: optimisation, system design, multi-phased mission system, reliability, genetic algorithm, fault tree, binary decision diagram

Mobile Ad hoc Networks (MANETs) are becoming increasingly important because of their unique characteristics of connectivity. Several delay sensitive applications are starting to appear in these kinds of networks. Therefore, an issue in concern is to guarantee Quality of Service (QoS) in such constantly changing communication environment. The classical QoS aware solutions that have been used till now in the wired and infrastructure wireless networks are unable to achieve the necessary performance in the MANETs. The specialized protocols designed for multihop ad hoc networks offer basic connectivity with limited delay awareness and the mobility factor in the MANETs makes them even more unsuitable for use. Several protocols and solutions have been emerging in almost every layer in the protocol stack. The majority of the research efforts agree on the fact that in such dynamic environment in order to optimize the performance of the protocols, there is the need for additional information about the status of the network to be available. Hence, many cross layer design approaches appeared in the scene. Cross layer design has major advantages and the necessity to utilize such a design is definite. However, cross layer design conceals risks like architecture instability and design inflexibility. The aggressive use of cross layer design results in excessive increase of the cost of deployment and complicates both maintenance and upgrade of the network. The use of autonomous protocols like bio-inspired mechanisms and algorithms that are resilient on cross layer information unavailability, are able to reduce the dependence on cross layer design. In addition, properties like the prediction of the dynamic conditions and the adaptation to them are quite important characteristics. The design of a routing decision algorithm based on Bayesian Inference for the prediction of the path quality is proposed here. The accurate prediction capabilities and the efficient use of the plethora of cross layer information are presented. Furthermore, an adaptive mechanism based on the Genetic Algorithm (GA) is used to control the flow of the data in the transport layer. The aforementioned flow control mechanism inherits GA’s optimization capabilities without the need of knowing any details about the network conditions, thus, reducing the cross layer information dependence. Finally, is illustrated how Bayesian Inference can be used to suggest configuration parameter values to the other protocols in different layers in order to improve their performance.