Studies On Intelligent Multi-objective Decision-making Methodologies

There are a lot of decision-making problems in our daily life. Most of them involve conflict and incommensurable multiple targets. Decision-making is a kind of thinking activities in which people make choices and judgments. Multi-objective decision making problems are acknowledged to be quite complicated, which demands effective enabling methods to solve them.This paper initially investigates several intelligent multi-objective decision-making algorithms. Regarding complicated multi-objective decision-making problems in practice, genetic algorithm is recognized as a compatible good global optimization method. Therefore, this paper is initially concerned with the determination of the crossover probability and mutation probability involved in genetic algorithm by means of simulations case studies. Additionally, it is conceivable that fuzzy logic is eligible to solve intelligent decision making problems, Based on in-depth analysis of multi-objective decision making nature, an adaptive fuzzy algorithm combined with genetic algorithm and fuzzy algorithm is proposed. This algorithm offers a mechanism of adaptive adjustments of membership functions, as well as rule-based integration of decision-makers'imaginations, which makes the final decision-making results more reasonable.A couple of example cases are presented to demonstrate the feasibility and effectiveness of the contributions.