  Differential Evolution (DE) is quite powerful for real parameter single
objective optimization. However, the ability of extending or changing search
area when falling into a local optimum is still required to be developed in DE
for accommodating extremely complicated fitness landscapes with a huge number
of local optima. We propose a new flow of DE, termed DE with individuals
redistribution, in which a process of individuals redistribution will be called
when progress on fitness is low for generations. In such a process, mutation
and crossover are standardized, while trial vectors are all kept in selection.
Once diversity exceeds a predetermined threshold, our opposition replacement is
executed, then algorithm behavior returns to original mode. In our experiments
based on two benchmark test suites, we apply individuals redistribution in ten
DE algorithms. Versions of the ten DE algorithms based on individuals
redistribution are compared with not only original version but also version
based on complete restart, where individuals redistribution and complete
restart are based on the same entry criterion. Experimental results indicate
that, for most of the DE algorithms, version based on individuals
redistribution performs better than both original version and version based on
complete restart.
