In this research, we propose to use two local search methods (LSM's); Particle Swarm Optimization (PSO) and the Bees Algorithm (BA) to solve Multi-Criteria Travelling Salesman Problem (MCTSP) to obtain the best efficient solutions. The generating process of the population of the proposed LSM's may be randomly obtained or by adding some initial solutions obtained from some efficient heuristic methods. The obtained solutions of the PSO and BA are compared with the solutions of the exact methods (complete enumeration and branch and bound methods) and some heuristic methods. The results proved the efficiency of PSO and BA methods for a large number of nodes ( ). The proposed LSM's give the best efficient solutions for the MCTSP for

In this paper we will investigate some Heuristic methods to solve travelling salesman problem. The discussed methods are Minimizing Distance Method (MDM), Branch and Bound Method (BABM), Tree Type Heuristic Method (TTHM) and Greedy Method (GRM).

The weak points of MDM are manipulated in this paper. The Improved MDM (IMDM) gives better results than classical MDM, and other discussed methods, while the GRM gives best time for 5≤ n ≤500, where n is the number of visited cities.

This paper presents a hybrid metaheuristic algorithm which is Harmony-Scatter Search (HSS). The HSS provides Scatter Search (SS) with random exploration for search space of problem and more of diversity and intensification for promising solutions. The SS and HSS have been tested on Traveling Salesman Problem. A computational experiment with benchmark instances is reported. The results demonstrate that the HSS algorithm produce better performance than original Scatter Search algorithm. The HSS in the value of average fitness is 27.6% comparing with original SS. In other hand the elapsed time of HSS is larger than the original SS by small value. The developed algorithm has been compared with other algorithms for the same problem, and the r

     In this paper, one of the Machine Scheduling Problems is studied, which is the problem of scheduling a number of products (n-jobs) on one (single) machine with the multi-criteria objective function. These functions are (completion time, the tardiness, the earliness, and the late work) which formulated as . The branch and bound (BAB) method are used as the main method for solving the problem, where four upper bounds and one lower bound are proposed and a number of dominance rules are considered to reduce the number of branches in the search tree. The genetic algorithm (GA) and the particle swarm optimization (PSO) are used to obtain two of the upper bounds. The computational results are calculated by coding (progr

     The traveling salesman problem is addressed in this paper by introducing a distributed multi-ant colony algorithm that is implemented on a Raspberry Pi cluster. The implementation of a master and eight workers, each running on Raspberry Pi nodes, is the central component of this novel technique. Each worker is responsible for managing their own colony of ants, while the master coordinates communications among workers’ nodes and assesses the most optimal approach. To put the newly built cluster through its paces, several datasets of traveling salesman problem are used to test the created cluster. The findings of the experiment indicate that a single board computer cluster, which makes use of multi-ant colony algorithm, is a via

Transportation and distribution are the most important elements in the work system for any company, which are of great importance in the success of the chain work. Al-Rabee factory is one of the largest ice cream factories in Iraq and it is considered one of the most productive and diversified factories with products where its products cover most areas of the capital Baghdad, however, it lacks a distribution system based on scientific and mathematical methods to work in the transportation and distribution processes, moreover, these processes need a set of important data that cannot in any way be separated from the reality of fuzziness industrial environment in Iraq, which led to use the fuzzy sets theory to reduce the levels of uncertainty.

String matching is seen as one of the essential problems in computer science. A variety of computer applications provide the string matching service for their end users. The remarkable boost in the number of data that is created and kept by modern computational devices influences researchers to obtain even more powerful methods for coping with this problem. In this research, the Quick Search string matching algorithm are adopted to be implemented under the multi-core environment using OpenMP directive which can be employed to reduce the overall execution time of the program. English text, Proteins and DNA data types are utilized to examine the effect of parallelization and implementation of Quick Search string matching algorithm on multi-co

In this paper we investigate the use of two types of local search methods (LSM), the Simulated Annealing (SA) and Particle Swarm Optimization (PSO), to solve the problems ( ) and . The results of the two LSMs are compared with the Branch and Bound method and good heuristic methods. This work shows the good performance of SA and PSO compared with the exact and heuristic methods in terms of best solutions and CPU time.

The traveling salesman problem (TSP) is a well-known and important combinatorial optimization problem. The goal is to find the shortest tour that visits each city in a given list exactly once and then returns to the starting city. In this paper we exploit the TSP to evaluate the minimum total cost (distance or time) for Iraqi cities. So two main methods are investigated to solve this problem; these methods are; Dynamic Programming (DP) and Branch and Bound Technique (BABT). For the BABT, more than one lower and upper bounds are be derived to gain the best one. The results of BABT are completely identical to DP, with less time for number of cities (n), 5 ≤ n ≤ 25. These results proof the efficiency of BABT compared with so

In this article, the nonlinear problem of Jeffery-Hamel flow has been solved analytically and numerically by using reliable iterative and numerical methods. The approximate solutions obtained by using the Daftardar-Jafari method namely (DJM), Temimi-Ansari method namely (TAM) and Banach contraction method namely (BCM). The obtained solutions are discussed numerically, in comparison with other numerical solutions obtained from the fourth order Runge-Kutta (RK4), Euler and previous analytic methods available in literature. In addition, the convergence of the proposed methods is given based on the Banach fixed point theorem. The results reveal that the presented methods are reliable, effective and applicable to solve other nonlinear problems.