Breadth-First Search Multi-Dimensional Binary Search Tree based Algorithms for Structural

This research proposes a set of novel algorithms for structural reliability estimation based on muti-dimensional binary search tree and breadth-first search, namely the reliability accuracy supervised searching algorithm, the limit-state surface resolution supervised searching algorithm and the reliability index precision supervised fast searching algorithm. The proposed algorithms have the following strengths: 1, all the proposed algorithms have satisfactory computational efficiency by reducing redundant samplings; 2, their computational costs are stable and computable; 3, performance functions of high non-linearity can be will handled; 4, the reliability accuracy supervised searching algorithm can adapt its computational cost according to a prescribed accuracy; 5, the limit-state surface resolution supervised searching algorithm is able to probe sharp changes on limit-state surfaces; 6, the reliability index precision supervised fast searching algorithm computes the reliability index with sufficient precision in a fast way.

Breadth First Search and Inference Methods Integration to satisfy table constraints

Within the Constraint Programming technology, so-called table constraints such as typical tables, compressed tables, smart tables, segmented tables, etc, are widely used. They can be used to represent any other types of constraints, and algorithms of the table constraint propagation (logical inference on constraints) allow eliminating a lot of "redundant" values from the domains of variables, while having low computational complexity. In the previous studies, the author proposed to divide smart tables into structures of C- and D-types. The generally accepted methodology for solving con-straint satisfaction problems is the combined application of constraint propagation methods and backtracking depth-first search methods. In the study, it is proposed to integrate breadth-first search methods and author`s method of table con-straint propagation. D-type smart tables are proposed to be represented as a join of several orthogonalized C-type smart tables. The search step is to select a pair of C-type smart tables to be joined and then propagate the restrictions. To de-termine the order of joining orthogonalized smart tables at each step of the search, a specialized heuristic is used, which reduces the search space, taking into account further calculations. When the restrictions are extended, the acceleration of the computation process is achieved by applying the developed reduction rules for the case of C-type smart tables. The developed hybrid method allows one to find all solutions to the problems of satisfying constraints modeled using one or several D-type smart tables, without decomposing tabular constraints into elementary tuples.

Tide-Inspired Path Planning Algorithm for Autonomous Vehicles

With the extensive developments in autonomous vehicles (AV) and the increase of interest in artificial intelligence (AI), path planning is becoming a focal area of research. However, path planning is an NP-hard problem and its execution time and complexity are major concerns when searching for optimal solutions. Thus, the optimal trade-off between the shortest path and computing resources must be found. This paper introduces a path planning algorithm, tide path planning (TPP), which is inspired by the natural tide phenomenon. The idea of the gravitational attraction between the Earth and the Moon is adopted to avoid searching blocked routes and to find a shortest path. Benchmarking the performance of the proposed algorithm against rival path planning algorithms, such as A*, breadth-first search (BFS), Dijkstra, and genetic algorithms (GA), revealed that the proposed TPP algorithm succeeded in finding a shortest path while visiting the least number of cells and showed the fastest execution time under different settings of environment size and obstacle ratios.

Analisis Perbandingan Algoritma Breadth First Search (BFS) dan Algoritma A*

Pada path planning terdapat beberapa metode yang dapat digunakan. Metode yang dapat digunakan untuk path planning yaitu algoritma Breadth First Search (BFS) dan algoritma A*. Metode yang digunakan tergantung dari keadaan lingkungan. Melakukan path planning pada tiap lingkungan dengan metode berbeda sehingga dapat mengetahui bagaimana peformansinya tiap metode tersebut. Sehingga tujuan penelitian ini adalah untuk membandingkan antara algoritma BFS dengan algoritma A* dalam melakukan path planning. Proses perbandingan dilakukan pada lima lingkungan pengujian. Lingkungan pengujian yang digunakan yaitu tanpa obstacle, obstacle trap, obstacle sederhana, obstacle maze dan obstacle narrow. Perbandingan algoritma BFS dan algoritma A* berdasarkan waktu eksekusi dan jumlah node yang dibutuhkan untuk melakukan path planning. Hasil penelitian menunjukkan bahwa pencarian jalur dari titik start ke titik goal dapat diselesaikan dengan algoritma BFS dan A*. Pada algoritma BFS maupun A* menghasilkan biaya jalur yang sama. Perbedaan terjadi pada node-node yang dibutuhkan algoritma BFS dan A* untuk menghasilkan jalur dari titik start hingga titik goal. Algoritma BFS membutuhkan node lebih banyak dibandingkan dengan A* untuk mencapai titik goal. Perbedaan jumlah node tersebut membuat waktu eksekusi menjadi berbeda. Waktu eksekusi pada algoritma BFS membutuhkan waktu lebih banyak dibandingkan dengan A*. Berdasarkan pengujian yang telah dilakukan maka algoritma A* lebih cepat dalam melakukan path planning. Tetapi pada lingkungan pengujian maze terjadi perbedaan waktu yang sedikit. Pada BFS memerlukan waktu tercepat 4,09 detik serta pada A* memerlukan waktu tercepat 3,88 detik. Serta pada lingkungan maze memiliki perbedaan jumlah node cukup sedikit yaitu 26 node. Hal tersebut membuktikan bahwa A* tidak selalu unggul jauh dengan BFS.

List Point Marker Path Finding for Artificial Intelligence Movement in 3D Games

In making a path finding algorithm in a 3D game to determine the direction of the NPC agent towards the destination, the Djiksra algorithm, Depth First Search, Breadth First Search and so on, usually the shortest distance is directly proportional to the travel duration to the target point. In this study, a test will be made using a list marker point such as the Djiksra algorithm to get the shortest distance and fastest time to reach the destination, in making this algorithm the C# language is used and the Unity software is used. After experimenting with various list points in different places in two directions, it was found that the distance traveled is always directly proportional to duration. So the selection of the fastest or shortest path can be done with this list point marker algorithm.

SaturBFS: Um Algoritmo de Coloração de Grafos para Resolução do Sudoku

Este estudo propõe um algoritmo de coloração de grafos intitulado Saturated Breadth-First Search (SaturBFS) para determinar soluções válidas do Sudoku. Concebeu-se o algoritmo por meio da combinação dos algoritmos de Busca em Largura (BFS) e DSatur. O objetivo é realizar uma análise comparativa do SaturBFS com os algoritmos BFS não saturado e Busca em Profundidade (DFS) para diferentes níveis (fácil, médio e difícil) e instâncias do Sudoku (4x4, 6x6, 8x8, 9x9, 10x10 e 12x12). Para a avaliação, considerou-se o tempo que os algoritmos levam para determinar uma solução válida e as porcentagens de soluções válidas encontradas. O algoritmo SaturBFS apresentou maior porcentagem de soluções válidas encontradas em um menor tempo.