Diversity and distribution of the scarab beetle tribe Phanaeini in the northern states of the Brazilian Northeast (Coleoptera: Scarabaeidae: Scarabaeinae)

The fauna of Phanaeini of the northeast of Brazil was investigated through fieldwork in the States of Ceará, Maranhão and Piauí, and through study of preserved material from other states. Seven species of Phanaeini are newly recorded from these three states. Of these, two species are also new records for the northeast region: Phanaeus melibaeus Blanchard and an unidentified Dendropaemon Perty species. A total of 13 new state records are given for eight of the 15 species of Phanaeini recorded from the northeast to date, including three new state genus records. A key is provided for identification of all species. Detailed distributional information is presented together with habitat and bait preferences and other ecological data for each species. The diversity and distribution of the tribe in the northeast is discussed in the context of regional biotopes and wider geographic ranges. The fauna is shown to be more diverse than previously believed, containing both endemic and widespread elements occurring in species assemblages that differ according to habitat type and elevation, leading to substantial complementarity of diversity amongst the main biogeographic provinces and biotopes of the region. A fauna de Phanaeini do Nordeste brasileiro é sumarizada, como resultado de novas coletas nos estados do Ceará, Maranhão e Piauí, e pelo estudo de material preservado de outros estados. Sete espécies de Phanaeini são reportadas pela primeira vez para esses estados. Destas, duas espécies são também novos registros para a Região Nordeste: Phanaeus melibaeus Blanchard e uma espécie não identificada de Dendropaemon Perty. Um total de treze novos registros de estatais é apresentado para oito das quinze espécies de Phanaeini reportadas do Nordeste até agora, incluindo treis novos registros estatais de gêneros. Uma chave é dada para permitir a identificação de todas as espécies. Apresenta-se informação detalhada de distribuição, hábitat e preferências por isca e outros dados ecológicos para cada espécie. A diversidade e a distribuição da tribo no Nordeste são discutidas no contexto de biótopos regionais e áreas geográficas maiores. A fauna é considerada mais diversa do que previamente esperado, contendo tanto elementos endêmicos como de ampla distribuição, ocorrendo em assembléias de espécies que variam de acordo com tipos de hábitat e altitude, levando a grande complementariedade da diversidade entre as principais províncias biogeográficas e biótopos da região

A Simulated Annealing/Tabu Search Algorithm for the Vehicle Routing Problem

The Vehicle Routing Problem is an NP-complete problem that has been studied extensively since it was introduced in 1958 by G. B. Dantzig and J. H. Ramser. This thesis creates three algorithms that endeavor to find an optimal solution for each problem tested. Two of the algorithms (Simulated Annealing and Tabu Search) have been used previously to solve this problem. These two solution methods are revisited to discover whether a new approach to creating routes will produce the best-known optimal values every time. New routes are created by forming route neighborhoods and then selecting cities from these neighborhoods for insertion. The third algorithm is an original algorithm which combines Simulated Annealing and Tabu Search. The algorithms presented do not produce the best-known optimal values, but are competitive with previously published algorithms

Heuristic Coloring Algorithm for the Composite Graph Coloring Problem

A composite graph is a finite undirected graph in which a positive integer known as a chromaticity is associated with each vertex of the graph. The composite graph coloring problem (CGCP) is the problem of finding the chromatic number of a composite graph, i.e., the minimum number of colors (positive integers) required to assign a sequence of consecutive colors to each vertex of the graph in a manner such that adjacent vertices are not assigned sequences with colors in common and the sequence assigned to a vertex has the number of colors indicated by the chromaticity of the vertex. The CGCP problem is an NP-complete problem that has applications to scheduling and resource allocation problems in which the tasks to be scheduled are of unequal durations. The pigeonhole principle gives rise to a problem reduction technique for the CGCP and a vertex ordering used in the vertex-sequentia1-with-interchange (VSI) algorithm. LFPHI. An upper bound on the chromatic number of a composite graph is obtained from the definition of a color-sequential coloring algorithm for the CGCP. The performances of twelve heuristic coloring algorithms are compared on a variety of random composite graphs. Three VSI algorithms (LF1I, LFPHI, and LFCDI) performed superior to the other algorithms on graphs having the lower numbers of vertices and low edge densities while two color-sequential algorithms (RLF1 and RLFD1) were superior on graphs having the higher numbers of vertices and high edge densities

The Difficulty of Approximating the Chromatic Number for Random Composite Graphs

Combinatorial Optimization is an important class of techniques for solving Combinatorial Problems. Many practical problems are Combinatorial Problems, such as the Traveling Salesman Problem (TSP) and Composite Graph Coloring Problem (CGCP). Unfortunately, both of these problems are NP-complete and it is not known if efficient algorithms exist to solve these problems. Even approximation with guaranteed results can be just as difficult. Recently, many generalized search techniques have been developed to improve upon the solutions found by the heuristic algorithms. This paper presents results for CGCP. In particular, exact and heuristic algorithms are presented and analyzed. This study is made, to show empirically that CGCP cannot provide guarantees on the approximation using these heuristic methods. In addition, an improvement is presented on the interchange method by Clementson and Elphick that is used with vertex sequential algorithms. This improvement allows graphs of up to 1000 vertices to be colored in considerably less time than previous studies. The study also shows that CDSaturl heuristic does not compete as well with CDSatur as expected for large graphs with edge density of 0.2. Several NP-completeness theorems are presented and proved. Approximation of CGCP is shown to be as difficult as finding exact solutions if we expect the approximate solutions to fall within a specified bound. These bounds on approximate solutions are shown to be directly related to the bounds that have been proved to exist for the Standard Graph Coloring Problem (SGCP). Finally, a model of CGCP is developed so that the Tabu Search technique can be applied. Several neighborhoods are developed and tested on 50 and 100 vertex graphs. Timing and performance is analyzed against the heuristics in the previous study. Instances of larger order graphs are used to test the best neighborhood searches with Tabu Search

A Parallel Branch and Bound Algorithm for Integer Linear Programming Models

A parallel branch and bound algorithm is developed for use with MIMD computers to study the efficiency of parallel processors on general integer linear programming problems. The Haldi and IBM test problems and a System Design model are used in the implementation of the algorithm. Initially the algorithm solves the Haldi and IBM test problems on a single processor computer which simulates a multiple processor computer. The algorithm is then implemented on the Denelcor HEP multiprocessor using two of the IBM problems to compare the results of the simulation to the results using an MIMD computer. Finally the algorithm is implemented on the HEP using the System Design model to show a case in which the number of pivots decreases as the number of processes are increased from seven to the process limit of sixteen. In general, it is shown that super linear efficiency can be achieved using multiple processors

Simulating Adaptive Load Sharing Policies On An IPSC/2 Multicomputer

Unlike most other adaptive load sharing (LS) policy studies, each node in the distributed system is modeled as a central server model represented by a closed queueing network (QN). The primary objective of this study is to use a simulation model to find the improvement for an adaptive LS policy in a distributed system. In homogeneous distributed systems, the simulation results in this study show that the performance improvements between no LS, LS with task placement, and LS with task migration are very small. These results are quite different from other studies, which show a significant improvement of mean response time by executing the LS policy. The performance improvement for the heterogeneous system is very consistent. That is, the benefit is given to every congested node in the system

Genetic Algorithm with 3-parent Uniform Crossover

A new genetic algorithm which uses a 3-parent uniform crossover operator is developed and analyzed. Uniform crossover operators are shown to be based on the premise that all bit-level genetic information should be passed from parents to children. The 3-parent uniform crossover operator is shown to adhere to this premise. The 3-parent uniform crossover operator is shown to be better than the 2-parent uniform crossover operator on the De Jong test functions. Two new genetic algorithms which use 3-parent traditional crossover operators are developed and analyzed. The first uses a strategy of randomly selecting 3 of the 6 children resulting from 3-parent reproduction. The second uses a strategy of selecting the best 3 of the 6 children resulting from 3-parent reproduction. Each of the 3-parent traditional crossover operators is shown to be superior to the 2-parent traditional crossover operator on the De Jong test functions. The strategy of selecting the best 3 out of 6 children is shown to be superior to the strategy of randomly selecting 3 out of 6 children. In addition to these 3-parent genetic algorithms, a relationship between the Metropolis algorithm from simulated annealing and the two-membered evolution strategy is developed. The Metropolis algorithm is shown to be a special case of the two- membered evolution strategy