Abstract
Differential Evolution(DE) is one of the most versatile evolutionary techniques that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. Recent developments on DE includes self adaptation of its parameters (F=step size and CR=cross-over probability) making it a parameter free optimizer. A new self adaptive DE(jDE) proposed by Janez Brest, is a robust improvement of DE, where the self adaptive parameters undergo similar operations of genetic operators. This paper aims at introducing a unique mutation strategy by modifying the existing ”DE/rand/1/bin” strategy of jDE with Difference Mean Based Perturbation (DMP) technique. The algorithm addressed as ADE-DMP is basically a variant of jDE, but the modified mutation scheme ensues within the algorithm effective search of area near the current best that effectively proves it to be a better and fast optimizer in complex real world problems of diverse domains.
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Kundu, R., Mukherjee, R., Das, S. (2013). Adaptive Differential Evolution with Difference Mean Based Perturbation for Practical Engineering Optimization Problems. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2013. Lecture Notes in Computer Science, vol 8297. Springer, Cham. https://doi.org/10.1007/978-3-319-03753-0_28
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DOI: https://doi.org/10.1007/978-3-319-03753-0_28
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