To get the distribution network to operate at its optimum performance in an automated distribution system reconfiguration was been proposed and researched. Considering, however, that optimum performance implies minimum loss, no overloading of transformers and cables, correct voltage profile, and absence of phase voltage and current imbalances, network reconfiguration alone is insufficient. It has to be complemented with techniques for phase rearrangement between the distribution transformer banks and the specific primary feeder with a radial structure and dynamic phase and load balancing along a feeder with a radial structure. This paper contributes such a technique at the low-voltage and medium-voltage levels of a distribution network simultaneously with reconfiguration at both levels. While the neural network is adopted for the network reconfiguration problem, this paper introduces a heuristic method for the phase balancing/loss minimization problem. A comparison of the heuristic algorithm with that of the neural network shows the former to be more robust. The approach proposed here, therefore for the combined problem, uses the neural network in conjunction with a heuristic method which enables different reconfiguration switches to be turned on/off and connected consumers to be switched between different phases to keep the phases balanced. An application example of the proposed method using real data is presented.

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Wireless sensor network has applications in environmental surveillance, healthcare, and military operations. Because the energy of sensor nodes is limited and nodes are unable to supply energy in real time, the purpose of many researches is to prolong lifetime of sensor network. Lifetime is times that the sink can collect data from all sensor nodes. When a user proposes a query, then the sink gathers data from all sensor nodes. The problem defined in the previous research is given a sensor network and residual energy of each node, and the energy consumption of transmitting a unit message between two nodes. Then this problem is to find a directed tree that maximize minimum residual energy. In this thesis, we define a new problem that given a sensor network and residual energy of each node, and the energy consumption of transmitting a unit message between two nodes. Then our problem is to find a path of each node, which maximize minimum residual energy. We prove this problem is NP-complete. We propose a heuristic algorithm and a similar heuristic algorithm for this problem.

The so-called heuristics have been widely used in solving combinatorial optimization problems because they provide a simple but effective way to find an approximate solution. These technologies are very useful for users who do not need the exact solution but who care very much about the response time. For every existing heuristic algorithm has its pros and cons, a hyper-heuristic clustering algorithm based on the diversity detection and improvement detection operators to determine when to switch from one heuristic algorithm to another is presented to improve the clustering result in this paper. Several well-known datasets are employed to evaluate the performance of the proposed algorithm. Simulation results show that the proposed algorithm can provide a better clustering result than the state-of-the-art heuristic algorithms compared in this paper, namely, k-means, simulated annealing, tabu search, and genetic k-means algorithm.

Software testing plays a necessary role in software development and maintenance. This activity is performed to support quality assurance. It is very common to design a number of testing suite to test their programs manually for most test engineers. To design test data manually is an expensive and labor-wasting process. Base on this reason, how to generate software test data automatically becomes a hot issue. Most researches usually use the meta-heuristic search methods like genetic algorithm or simulated annealing to gain the test data. In most circumstances, test engineers will generate the test suite first if they have a new program. When they debug or change some code to become a new one, they still design another new test suite to test it. Nearly no people will reserve the first test data and reuse it. In this research, we want to discuss whether it is useful to store the original test data.

This thesis focuses on the development and extension of nonlinear evolutionary model predictive control (NEMPC), a control algorithm previously developed by Phil Hyatt of the BYU RaD Lab. While this controller and its variants are applicable to any high degree-of-freedom (DoF) robotic system, particular emphasis is given in this thesis to control of a soft robot continuum joint. First, speed improvements are presented for NEMPC. Second, a Python package is presented as a companion to NEMPC, as a method of establishing a common interface for dynamic simulators and approximating each system by a deep neural network (DNN). Third, a method of training a DNN approximation of a hardware system that is generalize-able to more complex hardware systems is presented. This method is shown to reduce median tracking error on a soft robot hardware platform by 88%. Finally, particle swarm model predictive control (PSOMPC), a variant of NEMPC, is presented and modified to model and account for uncertainty in a dynamic system. Control performance of NEMPC and PSOMPC are presented for a set of control trials on simulated systems with uncertainty in parameters, states, and inputs, as well as on a soft robot hardware platform. PSOMPC is shown to have an increased robustness to system uncertainty, reducing expected collisions by 71% for a three-link robot arm with parameter uncertainty, input disturbances, and state measurement error.

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This diploma thesis deals with genetic algorithms and their properties. Particular emphasis is placed on finding the influence of mutation and population size. Genetic algorithms are applied on inverse heat conduction problems (IHCP) in the second part of the thesis. Several different approaches and coding methods were tested. Properties of genetic algorithms were improved by definition of two new genetic operators – manipulation and sorting. Reported theoretical findings were tested on the real data of inverse heat conduction problem. The library for easy implementation of GA for solving general optimization problems in C ++ was created and is described in the last chapter.

Commerce companies have experienced a rise in the number of parcels that need to be delivered each day. The goal of this study is to provide a decision-making procedure to assist carriers in taking a more significant role in selecting cost and risk-efficient truck-drone intermodal delivery routing plan. The congestion-based model is developed to select the method of parcel delivery utilizing a truck and a drone for optimizing cost and time. A study also has been conducted to compare drone-only and truck-only delivery routing plan. The proposed A* Heuristic algorithm and the OSRM application generate the travel path for drone and a truck along with the time of travel. Case studies have been conducted by varying the weight provided to cost and risk variable, studies indicate that there is a significant change in drone delivery travel time and cost with increase of cost weightage.