Attribute weight computation in a decision making problem by particle swarm optimization - Das, Satyajit; Guha, Debashree (Neural computing & applications, 2019)
The objective of this paper is to introduce a method for computing weights of attributes in a decision making problem under intuitionistic fuzzy environment. Many weight generation methods exist in the literature under intuitionistic fuzzy setting, but they have some limitations which can be pointed out as: the entropy measures used in entropy weight methods are invalid in many situations and also there are lots of entropy formulae for intuitionistic fuzzy sets, which will be better to use, and thus a confusion may arise; the other weight generation methods may lose some information since it needs to transform the intuitionistic fuzzy decision matrix into an interval-valued decision matrix. This conversion distorts experts original opinions. In this point of view, to overcome these demerits, we develop a weight generation method without changing the original decision information. The proposed method maximizes the average degree of satisfiability and minimizes the average degree of non-satisfiability of each alternative over a set of attributes, simultaneously. This leads to formulate a multi-objective programming problem (MOPP) to compute the final comprehensive value for each alternative. The scenario of an MOPP itself is subjective and can be modeled by fuzzy decision making problem due to the conflicting objectives and the way of human choice on conflict resolution. This problem is solved by using particle swarm optimization scheme, and the evaluation procedure is illustrated by means of a numerical example. This work has also justified the proposed approach by analyzing a comparative study.