Browsing by Subject "Heuristic programming"
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Item A comparative analysis of graph partitioning tools(Texas Tech University, 2004-08) Chinthapanti, Pavan KNot availableItem Characterizing neighborhoods favorable to local search techniques(2004) Dimova, Boryana Slavcheva; Barnes, J. Wesley.; Popova, ElmiraNP-Complete problems are the most difficult problems to solve and polynomial time algorithms to solve these problems do not exist. One of the more powerful approaches for such problems are heuristic direct search techniques. For a given problem, a landscape is composed of (1) a solution space, (2) an objective function value defined at all elements of the solution space and (3) a direct search neighborhood defined for each element of the solution space. The goal of the research documented in this dissertation was to extend previous characterizations of landscapes conducive to the success of direct search methodologies. The primary contributions of this dissertation are as follows: (1) The extension of the characterization of AR(1) elementary landscapes to include arbitrary neighborhood definitions (2) The creation of an entirely new class of landscapes favorable to direct search methods, a subset of the AR(p) neighborhoods where p>1 (3) The development of a lower (upper) bound for a local minima (maxima) in AR(1) elementary landscapes using information stability (4) The characterization multistep composite AR(1) elementary landscapesItem Controlled search over finite permutation groups(Texas Tech University, 1984-05) Aranha, Mario AA penetrance learning system is implemented to mechanically find a heuristic to perform a heuristically controlled search over finite permutation group graphs. The learning system is based on probabilistic analysis. A sample solution is evaluated in detail. Seasonably good results were obtained.Item A group theoretic approach to metaheuristic local search for partitioning problems(2005) Kinney, Gary W.; Barnes, J. Wesley.Item Heuristics for the family scheduling problems to minimize total tardiness(Texas Tech University, 2002-12) Chantaravarapan, SamarnRecently, family scheduling problems have attracted several researchers because of its real-life applications. The family scheduling problems involve multiple job types where jobs are classified into specific families by their similarity characteristics. Setup occurs only when the two consecutive jobs are from different families. An example of application of family scheduling problems is the colored plastic injecfion machine. Changing from one color to another color requires a setup. The purpose of this research is to implement methodologies to minimize total tardiness on single machine problems. However, the complexity of the proposed problem is NP-Hard. Seeking for optimal solutions for large problem sizes within a reasonable time is quite questionable. Therefore, this research aims to implement good heuristics that could provide good solutions within a reasonable time. This research can be divided into two main parts: problems with sequence independent setups and problems with sequence dependent setups. Integer programming models for both problems are proposed in this research. In case of problems with sequence independent setups, three constructive heuristics and five improving heuristics are proposed. The constructive heuristics are implemented to provide initial sequences for improving heuristics. In case of problems with sequence dependent setups, a hybrid genetic algorithm (HGA) is presented. The implementation of the proposed HGA is discussed thoroughly in this study. Furthermore, the proper genetic algorithm settings, such as crossover probability and population size, can enhance the performance of the algorithm. Thus, prior to investigating the performance of the proposed HGA, a pilot study is performed to determine proper genetic algorithm values. Experiments for each problem can be divided into two parts: experiments with small problem sizes, and experiments with large problem sizes. In the case of experiments of small problem sizes, the solutions of heuristics/HGA were compared with the solutions from CPLEX solver. In the experiments of large problem sizes, the solutions of heuristics/HGA were compared among each other. Furthermore, nonparametric tests, such as the Friedman tests and Kruskal-Wallis tests, were performed to investigate the effect of parameters, such as number of jobs and number of families, on the performance of heuristics/HGA.Item Inference of task execution times using linear regression techniques(Texas Tech University, 2002-12) Muniyappa, VinayNot availableItem Modular Abstract Self-learning Tabu Search (MASTS) : metaheuristic search theory and practice(2008-05) Ciarleglio, Michael Ian, 1979-; Barnes, J. WesleyMASTS is an extensible, feature rich, software architecture based on tabu search (TS), a metaheuristic that relies on memory structures to intelligently organize and navigate the search space. MASTS introduces a new methodology of rule based objectives (RBOs), in which the search objective is replaced with a binary comparison operator more capable of expressing a variety of preferences. In addition, MASTS supports a new metastrategy, dynamic neighborhood selection (DNS), which “learns” about the search landscape to implement an adaptive intensification-diversification strategy. DNS can improve search performance by directing the search to promising regions and reducing the number of required evaluations. To demonstrate the flexibility and range of capabilities, MASTS is applied to two complex decision problems in conservation planning and groundwater management. As an extension of MASTS, ConsNet addresses the spatial conservation area network design problem (SCANP) in conservation biology. Given a set of possible geographic reserve sites, the goal is to select which sites to place under conservation to preserve unique elements of biodiversity. Structurally, this problem resembles the NP-hard set cover problem, but also considers additional spatial criteria including compactness, connectivity, and replication. Modeling the conservation network as a graph, ConsNet uses novel techniques to quickly compute these spatial criteria, exceeding the capabilities of classical optimization methods and prior planning software. In the arena of groundwater planning, MASTS demonstrates extraordinary flexibility as both an advanced search engine and a decision aid. In House Bill 1763, the Texas state legislature mandates that individual Groundwater Conservation Districts (GCDs) must work together to set specific management goals for the future condition of regional groundwater resources. This complex multi-agent multi-criteria decision problem involves finding the best way to meet these goals considering a host of decision variables such as pumping locations, groundwater extraction rates, and drought management policies. In two separate projects, MASTS has shaped planning decisions in the Barton Springs/Edwards Aquifer Conservation District and Groundwater Management Area 9 (GMA9). The software has been an invaluable decision support tool for planners, stakeholders, and scientists alike, allowing users to explore the problem from a multicriteria perspective.Item Optimal transit route network design problem : algorithms, implementations, and numerical results(2004-05) Fan, Wei, 1974-; Machemehl, Randy B.Item Parallel machine scheduling with time windows(2004) Rojanasoonthon, Siwate; Bard, Jonathan F.Item Performance of assignment heuristics with scheduling enhancements(Texas Tech University, 2002-12) Neergunda, PhanirajNot availableItem Reliability improvement of task allocation heuristics using task redundancy(Texas Tech University, 2002-12) Sangam, SankethNot availableItem Statistically-biased task allocation heuristics in heterogeneous computing systems(Texas Tech University, 2000-08) Cervera-Huerta, GabrielNot available