  The Jaya algorithm is arguably one of the fastest-emerging metaheuristics
amongst the newest members of the evolutionary computation family. The present
paper proposes a new, improved Jaya algorithm by modifying the update
strategies of the best and the worst members in the population. Simulation
results on a twelve-function benchmark test-suite as well as a real-world
problem of practical importance show that the proposed strategy produces
results that are better and faster in the majority of cases. Statistical tests
of significance are used to validate the performance improvement.
