Genetic Algorithm Designed for Solving Linear or Nonlinear Mixed-Integer Constrained Optimization Problems

Genetic algorithms (GA) initially were not developed to handle the integer restriction or discrete values to the design variables. In the recent years, researchers has focused their work on developing/modifying GAs for handling integer/discrete variables. We have modified the BEX-PM algorithm developed by Thakur et al. [1] to solve the nonlinear constrained mixed-integer optimization problems. Twenty test problems have been used to conduct a comparative study to test the effectiveness of proposed algorithm (MI-BEXPM) with other similar algorithms (viz. MILXPM, RST2ANU, and AXNUM) in this class available in the literature. The efficacy of the results acquired through MI-BEXPM is compared with other algorithms on two well-known criteria. The performance of MI-BEXPM is also analysed and compared for solving real-life mixed-integer optimization problems with other methods available in the literature. It is found that MI-BEXPM is significantly superior to the algorithms considered in this work.

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