A Comprehensive Study on Metaheuristic Techniques Using Genetic Approach

Abstract

Most real-life optimization problems involve multiple objective functions. Finding  a  solution  that  satisfies  the  decision-maker  is  very  difficult  owing  to  conflict  between  the  objectives.  Furthermore,  the  solution  depends  on  the  decision-maker’s preference.  Metaheuristic solution methods have become common tools to solve these problems.  The  task  of  obtaining  solutions  that  take  account  of  a  decision-maker’s preference  is  at  the  forefront  of  current  research.  It  is  also  possible  to  have  multiple decision-makers with different preferences and with different  decision-making  powers. It may not be easy to express a preference using crisp numbers. In this study, the preferences of multiple decision-makers were simulated  and  a solution based on  a genetic  algorithm was  developed  to  solve  multi-objective  optimization  problems.  The  preferences  were collected  as  fuzzy  conditional  trade-offs  and  they  were  updated  while  running  the algorithm interactively with the decision-makers. The proposed method was tested using well-known benchmark problems.  The solutions were found to converge around the Pareto front of the problems.