Multi-objective genetic optimisation for self-organising fuzzy logic control

A multi-objective genetic algorithm is developed for the purpose of optimizing the rule-base of a self-organising fuzzy logic control algorithm (SOFLC). The tuning of the SOFLC optimization is based on selection of the best shaped performance index for modifying the rulebase online. A comparative study is conducted between various methods of multi-objective genetic optimisation using the SOFLC algorithm on a muscle relaxant anaesthesia system, which includes a severe nonlinearity, varying dynamics and time-delay.