Semi-steady-state Jaya Algorithm
This is an incremental improvement for researchers and practitioners in evolutionary computation and optimization, offering enhanced performance in metaheuristic algorithms.
The paper tackled the problem of improving the Jaya algorithm's performance by modifying the update strategies for the best and worst members in the population, resulting in better and faster results on a twelve-function benchmark test-suite and a real-world problem, as validated by statistical tests.
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.