Taguchi-Grey Relational Analysis Method for Parameter Tuning of Multi-objective Pareto Ant Colony System Algorithm

Abstract

In any metaheuristic, the parameter values strongly affect the efficiency of an algorithm’s search. This research aims to find the optimal parameter values for the Pareto Ant Colony System (PACS) algorithm, which is used to obtain solutions for the generator maintenance scheduling problem. For optimal maintenance scheduling with low cost, high reliability, and low violation, the parameter values of the PACS algorithm were tuned using the Taguchi and Grey Relational Analysis (Taguchi-GRA) method through search-based approach. The new parameter values were tested on two systems. i.e., 26- and 36-unit systems for window with operational hours [3000-5000]. The grey relational grade (GRG) performance metric and the Friedman test were used to evaluate the algorithm’s performance. The Taguchi-GRA method that produced the new values for the algorithm’s parameters was shown to be able to provide a better multi-objective generator maintenance scheduling (GMS) solution. These values can be benchmarked in solving multi-objective GMS problems using the multi-objective PACS algorithm and its variants.