INTRODUCTION The Metaheuristics are general strategies for designing heuristic procedures with high performance. The term metaheuristic, which appeared in 1986 for the first time (Glover, 1986), is compound by the terms: " meta " , that means over or behind, and " heuristic ". Heuristic is the qualifying used for methods of solving optimization problems that are obtained from the intuition, exp...

Research efforts in metaheuristics have shown that an intelligent incorporation of more classical optimization techniques in metaheuristics can be very beneficial. In this paper, we combine the metaheuristic ant colony optimization with dynamic programming for the application to the NP-hard k-cardinality tree problem. Given an undirected graph G with node and/or edge weights, the problem consis...

In this article, an effective method to control a power system during emergency conditions is presented. Based on Fuzzy Linear Programming (FLP), a new technique is developed to solve the Load Shedding and Generation Reallocation (LSGR) optimization Problem. The objective function consists of terms of load curtailments and deviations in generation schedules. The constraints are power system var...

Hybrid metaheuristics are powerful methods for solving complex problems in science and industry. Nevertheless, the resolution time remains prohibitive when dealing with large problem instances. As a result, the use of GPU computing has been recognized as a major way to speed up the search process. However, most GPU-accelerated algorithms of the literature do not take benefits of all the availab...

in this article, an effective method to control a power system during emergency conditions is presented. based on fuzzy linear programming (flp), a new technique is developed to solve the load shedding and generation reallocation (lsgr) optimization problem. the objective function consists of terms of load curtailments and deviations in generation schedules. the constraints are power system var...

─ Ant Algorithms are techniques for optimizing which were coined in the early 1990‟s by M. Dorigo. The techniques were inspired by the foraging behavior of real ants in the nature. The focus of ant algorithms is to find approximate optimized problem solutions using artificial ants and their indirect decentralized communications using synthetic pheromones. In this paper, at first ant algorithms ...

the traveling salesman problem (tsp) is the problem of finding the shortest tour through all the nodes that a salesman has to visit. the tsp is probably the most famous and extensively studied problem in the field of combinatorial optimization. because this problem is an np-hard problem, practical large-scale instances cannot be solved by exact algorithms within acceptable computational times. ...

Metaheuristics have been established as one of the most practical approach to simulation optimization. However, these methods are generally designed for combinatorial optimization, and their implementations do not always adequately account for the presence of simulation noise. Research in simulation optimization, on the other hand, has focused on convergent algorithms, giving rise to the impres...

Local search metaheuristic algorithms are proven & powerful combinatorial optimization strategies to tackle hard problems like traveling salesman problem. These algorithms explore & evaluate neighbors of a single solution. Time Consuming LSM algorithms can be improved by parallelizing exploration & evaluation of neighbors of a solution. GPU architecture is suitable for algorithms of single prog...