Neural Networks (NNs) are one of the meta-heuristics methods to solve complex problems. NNs have been able to solve combinatorial optimization problems in many cases successfully. Quadratic Assignment problem (QAP) is an NP-hard combinatorial optimization problem. Some of QAP applications are: layout design, keyboard design, VLSI design and etc. Many approaches, so far, have been used to solve this kind of problems and one of them is NNs approach. In this study we suggest an algorithm based on Mean Field Theory (MFT), a kind of NNs with patts neurons. Computational results indicate that this algorithm produces better solutions in comparison with two previous NNs algorithms.

Todays, various heuristic optimization methods have been developed. Many of these algorithms are inspired from physical processes or swarm behaviors in nature. Gravitational Search Algorithm (GSA) is an optimization algorithm based on the law of gravity and mass interactions. In the proposed algorithm, the search agents are a collection of masses. In this paper, mentioned algorithm is used to solve of the Frequency Assignment problem (FAP). For ability test of the algorithm, CALMA benchmarks are used and results are good.

In this paper, a metahevristic algorithm, based on the Ant Colony Optimization (ACO) method, is presented for solving stochastic Assignment problems. In a stochastic Assignment problem, it is assumed that agents will arrive with a known distribution function and the working time of each agent is also a stochastic variable and can be determined by a normal distribution function. Furthermore, it is also assumed that the proficiency of each agent is a stochastic variable. After modeling the problem, an ACO algorithm is developed to solve the model. Furthermore, in an evaluation phase of the objective function, a simulation algorithm is also presented. Finally, the convergence of the proposed algorithm is shown on some randomly generated test problems. Computational results show the efficiency of this algorithm in comparison to the other techniques for solving stochastic Assignment problems.

In this paper we apply neural network method for solving generalized Assignment problem. This problem is the generalization of the well-known Assignment problem that can be formulated as a zero-one integer programming problem. For solving this problem applying neural network method, first we transform it to a nonlinear programming problem, then we use two types of neural network structure one based on penalty function and the other augmented lagrangean multiplier method and compare them with each other. It will be noticed that neural network based on penalty function method is not appropriate to combinatorial optimization problems because it reaches an infeasible solution or gets stuck in a nonoptimal feasible solution while neural network based on augmented lagrangean multiplier method can alleviate this problem to some extent.

Flight scheduling is one of the major problems for any airline company. This problem has been investigated as an optimization problem for many years. Usually, the flight scheduling problem is divided into a number of sub-problems, one of them being the fleet Assignment. In this problem, given a flight timetable and aircraft specifications, the type of aircraft is determined for each flight. In recent studies, the fleet Assignment problem has been modeled as a multi-commodity network flow problem with integer and real variables, which has been solved by conventional methods. In this research, one of the existing models has been modified for a domestic airline company and simulated annealing (SA) algorithm is used to solve it. This algorithm was implemented for the fleet Assignment problem and used to solve some sample problems. Comparison of the results of solving sample problems by SA algorithm and GAMS software indicates that the SA has a good capability for solving the fleet Assignment problem.

Task Assignment problem (TAP) involves assigning a number of tasks to a number of processors in distributed computing systems and its objective is to minimize the sum of the total execution and communication costs, subject to all of the resource constraints. TAP is a combinatorial optimization problem and NP-complete. This paper proposes a hybrid meta-heuristic algorithm for solving TAP in a heterogeneous distributed computing system. To compare our algorithm with previous ones, an extensive computational study on some benchmark problems was conducted. The results obtained from the computational study indicate that the proposed algorithm is a viable and effective approach for the TAP.