Study of The Parallel Genetic Algorithm models for Multi Objective Optimization Problems
Shinya Watanabe · 2001
In this paper, we discuss the parallel genetic algorithm models for multi objective op- timization problems. The parallel models are dealt with Distributed GA and Divided Range Multi- Objective GA (DRMOGA), Master Slave Local Cultivation (MSLC). To clarify the characteristics and effectiveness of these models, these models are applied to a knapsack problem. Through the numerical examples, it becomes cleared that DRMOGA and MSLC are suited to parallel computers and can keep the diversity of the solutions.