This research presents the fundamental concepts of tabu search for optimization problem as symmetric traveling salesman problem. The purpose of this research is to solve the traveling salesman problem with tabu search method, to find an optimal solution of small search space and computational requirements. Keywords: Tabu algorithm, traveling salesman problem, NP problem. ‫طريقة‬ ‫ال‬ ‫بحث‬ ‫الممنوع‬ ‫لحل‬ ‫م‬ ‫سالة‬ ‫المتجول‬ ‫البائع‬ ‫الكال‬ ‫نذير‬ ‫اء‬ ‫إسر‬ ‫ك‬ ‫شعبان‬ ‫زيدان‬ ‫رقية‬ ‫يض‬ ‫التمر‬ ‫كلية‬ ‫الموصل‬ ‫جامعة‬ ، ‫الطب‬ ‫كلية‬ ‫الموصل‬ ‫جامعة‬ ، ‫االستالم:‬ ‫تاريخ‬ 10 / 12 / 2007 ‫ال‬ ‫تاريخ‬ ‫قبول:‬ 04 / 03 / 2008 ‫الملخص‬ ‫قت‬ ‫تطر‬ ‫هذه‬ ‫اسة‬ ‫الدر‬ ‫األمثلية‬ ‫مسائل‬ ‫من‬ ‫مسألة‬ ‫مع‬ ‫الممنوع‬ ‫البحث‬ ‫أساسيات‬ ‫إلى‬ -‫البائع‬ ‫مسألة‬ ‫المتجول‬ -‫مثالي‬ ‫حل‬ ‫على‬ ‫للحصول‬ ‫الممنوع،‬ ‫البحث‬ ‫يقة‬ ‫طر‬ ‫باستخدام‬ ‫المتجول‬ ‫البائع‬ ‫مسألة‬ ‫حل‬ ‫على‬ ‫وركزت‬ ‫قليلة.‬ ‫وحسابات‬ ‫صغير‬ ‫ـث‬ ‫ـ‬ ‫بح‬ ‫وبفضاء‬ ‫للمسألة‬ ‫المفتاحية:‬ ‫الكلمات‬ ‫المتجول‬ ‫البائع‬ ‫مسألة‬ ‫الممنوع،‬ ‫البحث‬ ‫الخطية‬ ‫غير‬ ‫البرمجة‬ ‫مسألة‬ ، .

The paper tackled a survey of two optimization methods to study spanning tree problem by modifying the spanning tree problem to generate all of possible solutions in undirected tree graph with simulated annealing algorithm and ant colony optimization algorithm. These algorithms are two of the optimization methods to find optimal solution from many of solutions in search space. A program is written in MATLAB 6.5 language to simulate these two algorithms with spanning tree problem. The experimental results in this paper show the effectiveness and easy implementation of each algorithm to find optimal solution, and to perform significantly better than the manual method.

This research studied ant colony optimization with optimization problem as an assignment model problem by Hungarian method. The proposed heuristic algorithm simulate ant colony optimization algorithm with Hungarian method for Assignment problem. The ant colony optimization algorithm simulates the behavior of real ant colony, to find the shortest path between many paths for solving the problem. It dependent on the path from the nest (problem of research) to food (optimal solution) by deposited pheromone on the path they take between the nest and food, so that other ants can smell it.The experiment in this research shows that the algorithm provides optimal solution. It has outperforms with computation and it is an effective approach and the algorithm performs significantly better than the classical method, to reduce the region of the space considered and computation as compared to the classical methods.

The research tackled the classical problem in artificial intelligence as 8-puzzle problem with genetic algorithm. The research present the fundamental of genetic algorithm with sliding tile 8-puzzle problem. Starting from current state for state space search into a goal state by depending on the tile's move (tiles out of place) in the current and compare with the solution of the problem (goal), without blank's move. population size chose by the summation of probabilities misplaced tile's Genetic Algorithm to Solve Sliding Tile 8-Puzzle Problem. ١٤٦move (tiles out of place) in current state comparing with goal state. In this research, depended on the Crossover and mutation for ordered chromosomes method. The experimental in this research show that the algorithm is efficient. The source code is written in Matlab language.

The research tackled artificial intelligent methods to solve one of the optimization problems by using artificial ant by applying ant colony optimization algorithm and also tabu search algorithm to find the solution of sliding tile 8-puzzel problem. In ant colony algorithm generated many possible solutions depending on finding the difference tiles in initial state from the goal and moving accordingly in the current state of the problem. In Tabu search, many possible solutions have been generated according to the replacement relation between different tiles in initial state to find the optimal solution from many solutions. In this research, the experimental show is very speed to obtain the goal. The source code is written in MATLAB language to simulate these two algorithms.