The Research On Multi-objective Optimization Based On Evolutionary Algorithms And Game Strategy

Multi-objective evolutionary algorithms (MOEAs) are proposed from simulating the biological evolutionary theory. These algorithms are.high-performance,self-organization and robustness.MOEAs are not sensitive to the figure of Pareto front and can get lots of results implemented once.Beacause of their advantage, MOEAs have been the mainstream on solving multi-objectives optimization problems.Conventional MOGAs use non-dominated sorting methods to push the population to move toward the real Pareto front.This approach has a good performance at earlier stages of the evolution, however it becomes hypodynamic at the later stages.The objective of this study is to explore and research the optimization principle and the evolutionary mechanism of MOEAs.In this paper we add the static Bayesian game strategy into MOGA and propose a novel multi-objective genetic algorithm(SBG-MOGA),which improves the optimition ability at the later stages of algorithm.The study focuses on the following aspects:(1)This article discusses the principles of swarm intelligence algorithm optimization and the solution model of multi-objective evolutionary algorithm,and then describes the game theory. And also multi-objective evolutionary algorithm convergence and diversity has been studied.(2)Multi-Objective Genetic Algorithm Based on Static Bayesian Game Strategy (SBG-MOGA) has been introduced.In SBG-MOGA, a objective that needs to optimize is regarded as a participant of a game.The game strategy in SBG-MOGA generates a tensile force over the population and this will obtain a better multi-objective optimization performance.The convergence of SBG-MOGA has been proofed in the paper. Moreover, the algorithm is verified by several simulation experiments and its performance is tested by different benchmark functions.(3)According to the characteristic of grid scheduling,this article proposed a grid scheduling solution model based on SBG-MOGA. In the paper, we gave a detailed description of scheduling problems on the grid, established the multi-objective optimization model of grid scheduling problem,and then improved the SBG-MOGA in order solving Grid scheduling problem.Finally, simulation experiments were given to verify the convergence and feasibility.