Semi-steady-state Jaya Algorithm

TL;DR

This paper introduces an improved version of the Jaya algorithm, enhancing its update strategies, which leads to better and faster results on benchmark tests and real-world problems, validated by statistical significance tests.

Contribution

The paper proposes a novel modification to the Jaya algorithm's update strategies, improving its efficiency and effectiveness in optimization tasks.

Findings

Better and faster results on benchmark functions

Improved performance on a real-world problem

Statistically significant performance enhancement

Abstract

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.