In recent years, cloud computing plays a crucial role in many real applications. Thus, how to solve workflow scheduling problems, i.e., allocating and different resources, under the environment becomes more important. Although some evolutionary algorithms (EAs) can problems with small scale, they show disadvantages on larger scale this paper, multi-objective genetic algorithm (MOGA) is applied ...

The agent technology and genetic algorithms are integrated and is applied to solve multi-objective optimization problem. An agent in this algorithm represents a candidate solution to the multi-objective optimization problem. Agent lives in the grid environment and it possesses own local space called the neighborhood. In the neighborhood, an agent can compete and collaborate with other agents to...

Integrated production-distribution planning (PDP) is one of the most important approaches in supply chain networks. We consider a supply chain network (SCN) to consist of multi suppliers, plants, distribution centers (DCs), and retailers. A bi-objective mixed integer linear programming model for integrating production-distribution designed here aim to simultaneously minimize total net costs in ...

Abstract Due to the shortcomings of GA in path planning, there are too many control variables and it is easy fall into local areas. We introduce idea a gene bank store new chromosomes generated each time bank. The linear regression proposed predict probability crossover mutation next generation. At same time, method increasing chromosome diversity proposed, which avoids problem that difficult s...

Many real-world scientific and engineering applications involve finding solutions to “hard” Multiobjective Optimization Problems (MOPs). Genetic Algorithms (GAs) can be extended to find acceptable MOP Pareto solutions. The intent of this discussion is to illustrate that modifications made to the Multi-Objective messy GA (MOMGA) have further improved the efficiency of the algorithm. The MOMGA is...