The scheduling policy on agent software evolution

Abstract

Task allocation and scheduling in MAS systems utilized genetic algorithm is a focus for more and more computer scholars. Aiming at the low speed of typical genetic algorithm, the global convergence for traditional genetic algorithm, and the local convergence for simulated annealing algorithm, this paper proposes a new task allocation algorithm in multiple Agent systems with the advantages of both methods as inclusive based on the formal description for the task allocation. This paper describes the foundermental ideas and key steps of the proposed algorithm, which is validated by simulated experiment. The results demonstrate that the genetic algorithm based on simulated annealing has faster convergence speed and more optimal solution than a genetic algorithm or a simulated annealing algorithm.

Balancing Inventory Management: Genetic Algorithm Optimization for A Novel Dynamic Lot Sizing Model in Perishable Product Manufacturing

“…GAs have been widely recognized as powerful optimization tools for solving lot sizing problems due to their robustness and ability to handle complex problems [60]- [62]. GAs are heuristic search algorithms that have shown effectiveness in adaptation and optimization problems [63].…”