Fuzzy Inference Based A Posterior Decision-Making for Multi-Objective Diet Optimization Problem

We propose a Mamdani-Type Fuzzy Inference based posterior decision-making approach to multi-objective diet optimization problem. optimize the problem with evolutionary algorithms that result in tens/hundreds of non-dominated solutions which is too large pick one them by decision-maker. Even though all are optimized for objectives simultaneously, not objective functions may be equally important user and, also their importance change over time. Our main goal develop an applicable method representing and incorporating decision maker's (DM) instant preferences into stage. The FIS making can guide users decide on most suitable menus. User's each form rule sets. Using Mamdani type post-decision process novel contribution. A desirability measure calculated using sets membership considering values, preferred menu(s) provided user. direct DM region interest search space Thus, daily menu suggestions become more applicable, practical, desirable users.