Yet, two classes of algorithms have been used in partial constraint satisfaction: local search methods and branch&bound search extended by the classical constraint-processing techniques like e.g. forward checking and backmarking. Both classes exhibit characteristic advantages and drawbacks. This article presents a novel approach for solving partial constraint satisfaction problems exhaustively ...

To practically solve NP-hard combinatorial optimization problems, local search algorithms and their parallel implementations on PVM or MPI have been frequently discussed. Since a huge number of neighbors may be examined to discover a locally optimal neighbor in each of local search calls, many of parallelization schemes, excluding so-called the multi-start parallel scheme, try to extract parall...

Multi-agent systems usually address one of two forms of interaction. One has completely competitive agents that act selfishly, each maximizing its own gain from the interaction. Auctions and voting scenarios usually assume such agents and follow game theoretic results. The other form of interaction has multiple agents that cooperatively search for some global goal, such as an optimal time slot ...

wireless sensor networks are the new generation of networks that typically are formed great numbers of nodes and the communications of these nodes are done as wireless. the main goal of these networks is collecting data from neighboring environment of network sensors. since the sensor nodes are battery operated and there is no possibility of charging or replacing the batteries, the lifetime of ...

Random search algorithms are useful for many ill-structured global optimization problems with continuous and/or discrete variables. Typically random search algorithms sacrifice a guarantee of optimality for finding a good solution quickly with convergence results in probability. Random search algorithms include simulated annealing, tabu search, genetic algorithms, evolutionary programming, part...

in this paper, we proposed an algorithm for solving the problem of task scheduling using particle swarm optimization algorithm, with changes in the selection and removing the guide and also using the technique to get away from the bad, to move away from local extreme and diversity. scheduling algorithms play an important role in grid computing, parallel tasks scheduling and sending them to appr...

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...

Memetic Algorithms have proven to be potent optimization frameworks which are capable of handling a wide range of problems. Stemming from the long-standing understating in the optimization community that no single algorithm can effectively accomplish global optimization [940], memetic algorithms combine global and local search components to balance exploration and exploitation [368, 765]: the g...

This paper deals with multi-period project portfolio selection problem. In this problem, the available budget is invested on the best portfolio of projects in each period such that the net profit is maximized. We also consider more realistic assumptions to cover wider range of applications than those reported in previous studies. A novel mathematical model is presented to solve the problem, con...