Genetic algorithms are a population-based Meta heuristics. They have been successfully applied to many optimization problems. However, premature convergence is an inherent characteristic of such classical genetic algorithms that makes them incapable of searching numerous solutions of the problem domain. A memetic algorithm is an extension of the traditional genetic algorithm. It uses a local se...

This paper presents a new wavelet-network-based technique for 1D and 2D functions’ approximation. Classical training algorithms start with a predetermined network structure which can be either insufficient or overcomplicated. Furthermore, the resolutions of wavelet networks training problems by gradient are characterized by their noticed inability to escape of local optima. The main feature of ...

Although lot streaming scheduling is an active research field, lot streaming flexible flow lines problems have received far less attention than classical flow shops. This paper deals with scheduling jobs in lot streaming flexible flow line problems. The paper mathematically formulates the problem by a mixed integer linear programming model. This model solves small instances to optimality. Moreo...

In recent years genetic algorithms have emerged as a useful tool for the heuristic solution of complex discrete optimisation problems. In particular there has been considerable interest in their use in tackling problems arising in the areas of scheduling and timetabling. However, the classical genetic algorithm paradigm is not well equipped to handle constraints and successful implementations u...

Distributed processing is recognized as a practical way to achieve high performance in various computational applications. Many dynamic loadbalancing algorithms have been proposed for parallel and discrete simulations. But the actual performances of these algorithms have been far from ideal, especially in the heterogeneous environment. In this paper, a hybrid approach using fuzzy supervised lea...

the capacitated vehicle routing problem (cvrp) is a well-known combinatorial optimization problem that holds a central place in logistics management. the vehicle routing is an applied task in the industrial transportation for which an optimal solution will lead us to better services, save more time and ultimately increase in customer satisfaction. this problem is classified into np-hard problem...

the paper is devoted to solution of some problems in nuclear power station generating unit intellectual control systems using genetic algorithms on the basis of control system model development, optimizations methods of their direct quality indices and improved integral quadratic estimates. some mathematical vector models were obtained for control system multicriterion quality indices with due ...

logistic network design is one of the most important strategic decisions in supply chain management that has recently attracted the attention of many researchers. transportation network design is then one of the most important fields of logistic network. this study is concerned with designing a multi-stage and multi-product logistic network. at first, a mixed integer nonlinear programming model...

neural network is one of the most widely used algorithms in the field of machine learning, on the other hand, neural network training is a complicated and important process. supervised learning needs to be organized to reach the goal as soon as possible. a supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples.  hen...