A Comprehensive Study on Metaheuristic Techniques Using Genetic Approach

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G. Kalyani
K. Krishna Jyothi
T. Pratyusha


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.

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How to Cite
Kalyani, G., K. K. Jyothi, and T. Pratyusha. “A Comprehensive Study on Metaheuristic Techniques Using Genetic Approach”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 7, no. 8, Aug. 2019, pp. 23-31, doi:10.17762/ijritcc.v7i8.5351.