An Enhanced Semisteady-State Jaya Algorithm With a Control Coefficient and a Self-Adaptive Multipopulation Strategy - Joshua Churchill Ankrah; Francis Boafo Effah; Elvis Twumasi (Journal of Electrical and Computer Engineering)
This paper introduces the enhanced semisteady-state Jaya (ESJaya) algorithm, an improved version of the semisteady-state Jaya (SJaya) algorithm. The ESJaya algorithm uses a control coefficient to regulate the influence of the best solution, achieving a better balance between exploration and exploitation. It also features a self-adaptive multipopulation strategy and removes absolute value operators in its update equation for improved performance. Using 20 benchmark functions, the ESJaya algorithm is compared with the traditional Jaya, SJaya, and elitism-based self-adaptive multipopulation Jaya (SAMPEJaya) algorithms. The ESJaya algorithm ranks first in obtaining optimum values, mean accuracy, and stability, showing better overall performance than the others. The ESJaya algorithm ranks first with an average overall rank of 1.750, followed by Jaya, SAMPEJaya, and SJaya with average overall ranks of 2.650, 2.700, and 2.900, respectively. The Wilcoxon signed-rank test confirms the statistical significance of the overall rankings. When compared with the Snow Geese Algorithm (SGA), Big Bang–Big Crunch Algorithm (BBBCA), firefly algorithm (FA), bat algorithm (BA), cuckoo search algorithm (CSA), and flower pollination algorithm (FPA), the ESJaya algorithm gives competitive results in terms of obtaining optimum values, mean accuracy, and stability. Using four real-world engineering problems, the ESJaya algorithm is compared with the recently proposed piranha predation optimization algorithm (PPOA) and four state-of-the-art algorithms, namely, the COLSHADE algorithm, the sCMAgES algorithm, the SASS algorithm, and the EnMODE algorithm. The ESJaya algorithm finds optimum solutions with good accuracy, consistency, and stability. Overall, it has a slower convergence and longer run time due to its multipopulation strategy and its search for the best solution after each solution update. However, it consistently finds more optimal solutions than other improved algorithms.