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...

fuzzy cognitive maps (fcms) have successfully been applied in numerous domains to show the relations between essential components in complex systems. in this paper, a novel learning method is proposed to construct fcms based on historical data and by using meta-heuristic: genetic algorithm (ga), simulated annealing (sa), and tabu search (ts). implementation of the proposed method has demonstrat...

Premature Convergence and genetic drift are the inherent characteristics of genetic algorithms that make them incapable of finding global optimal solution. A memetic algorithm is an extension of genetic algorithm that incorporates the local search techniques within genetic operations so as to prevent the premature convergence and improve performance in case of NP-hard problems. This paper propo...

This paper looks at Memetic Algorithm for solving timetabling problems. We present a new memetic algorithm which consists of global search algorithm and local search algorithm. In the proposed method, a genetic algorithm is chosen for global search algorithm while a simulated annealing algorithm is used for local search algorithm. In particular, we could get an optimal solution through the .NET...

This paper proposes a model for obnoxious waste location-routing problem (LRP) considering various types of wastes and several treatment technologies. The investigated distribution network includes three echelons of generation nodes, treatment and disposal facilities. A multi-objective LRP model is developed with three objective functions minimizing the treatment and disposal facility undesirab...

The growing complexity in the electronic hardware now necessitates in improving the performance of searching algorithms. Genetic algorithms do not guarantee global optimum solution to NP-Hard problems but are generally good at finding acceptable solution to problems. In complex combinatorial spaces, hybridization with other optimization techniques can greatly improve the efficiency of search. M...

In order to improve the efficiency of test data generation during the software test, many studies have been focusing on the automation of test data generation with metaheuristic techniques such as genetic algorithm and tabu search etc. This paper proposes a hybrid algorithm in combination with genetic algorithm with tabu search to generate the test data based on decision coverage criteria. The ...

Facility location decisions play a prominent role in strategic planning of many firms, companies and governmental organizations. Since in many real-world facility location problems, the data are subject to uncertainty, in this paper, we consider the P-center problem under uncertainty of demands. Using Bertsimas and Sim approach, we develop a robust model of the problem as an integer programming...

in this study, we discuss the capacitated facility location-allocation problem with uncertain parameters in which the uncertainty is characterized by given finite numbers of scenarios. in this model, the objective function minimizes the total expected costs of transportation and opening facilities subject to the robustness constraint. to tackle the problem efficiently and effectively, an effici...