Journal
INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION
Volume 3, Issue 1, Pages 17-30Publisher
INDERSCIENCE ENTERPRISES LTD
DOI: 10.1504/IJBIC.2011.038701
Keywords
differential evolution; DE; mutation; crossover; decentralisation; centralisation
Ask authors/readers for more resources
Differential evolution (DE) is a reliable and versatile function optimiser especially suited for continuous optimisation problems. Practical experience, however, shows that DE easily looses diversity and is susceptible to premature and/or slow convergence. This paper proposes a modified variant of DE algorithm called improved differential evolution (IDE). It works in three phases: decentralisation, evolution and centralisation of the population. Initially, the individuals of the population are partitioned into several groups of subpopulations (decentralisation phase) through a process of shuffling. Each subpopulation is allowed to evolve independently from each other with the help of DE (evolution phase). Periodically, the subpopulations are merged together (centralisation phase) and again new subpopulations are reassigned to different groups. These three phases helps in searching all the potential regions of the search domain effectively, thereby, maintaining the diversity. The promising nature of IDE is demonstrated on a testbed of 16 benchmark problems having box constraints. Comparison of numerical results shows that IDE is either better or at par with other contemporary algorithms.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available