Job scheduling is one of the main factors affecting grid computing performance. Job scheduling problem classified as an NP-hard problem. Such a problem can be solved only by using approximate algorithms such as heuristic and meta-heuristic algorithms. Ant colony system algorithm is a meta-heuristic algorithm which has the ability to solve differenttypes of NP-hard problems.However, ant colony s...

In most real world application and problems, a homogeneous product is carried from an origin to a destination by using different transportation modes (e.g., road, air, rail and water). This paper investigates a fixed charge transportation problem (FCTP), in which there are different routes with different capacities between suppliers and customers. To solve such a NP-hard problem, four meta-heur...

A lot of heuristic approaches have been explored in the last two decades in order to tackle large size optimization problems. These areas include parallel meta-heuristics, hybrid meta-heuristic, and cooperative search algorithms. Different taxonomies have been proposed in the literature for parallel and hybrid meta-heuristics. In these taxonomies, one can realize that cooperative search algorit...

In this chapter, we review a few important concepts from Grid computing related to scheduling problems and their resolution using heuristic and meta-heuristic approaches. Scheduling problems are at the heart of any Grid-like computational system. Different types of scheduling based on different criteria, such as static vs. dynamic environment, multi-objectivity, adaptivity, etc., are identified...

We describe a hybrid meta-heuristic algorithm for combinatorial optimization problems with a specific reference to the travelling salesman problem (TSP). The method is a combination of a genetic algorithm (GA) and greedy randomized adaptive search procedure (GRASP). A new adaptive fuzzy a greedy search operator is developed for this hybrid method. Computational experiments using a wide range of...

In this paper, an online task scheduling and mapping method based on a fuzzy neural network (FNN) learned by evolutionary multi-objective algorithm (NSGA-II) to jointly optimize the main design challenges of heterogeneous MPSoCs is proposed. approach, first, FNN parameters are trained using NSGA-II-based optimization engine considering including temperature, power consumption, failure rate, exe...