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In the present study a Modified Differential Evolution (MDE) algorithm is proposed. This algorithm is different in three ways from basic DE. For initialization it utilizes opposition-based learning while in basic DE uniform random numbers serve this task. Secondly, in basic DE mutant individual is random while in MDE it is tournament best and finally MDE utilizes only one set of population as against two sets as used in basic DE. The performance of proposed algorithm is investigated and compared with basic differential evolution. The experiments conducted shows that proposed algorithm outperform the basic DE algorithm in all the benchmark problems and real life applications.
Date of Conference: 4-7 Dec. 2009