An adaptive multi-team perturbation-guiding Jaya algorithm for optimization and its applications

This study proposes an adaptive multi-team perturbation-guiding Jaya (AMTPG-Jaya) algorithm which uses multiple teams to explore the search space. The proposed algorithm adapts the number of teams to explore the search space based on the convergence to the optimum. Furthermore, each team uses the same set of the population, and there is a different perturbation or movement equation for each team. As each team has a different perturbation scheme, the set of the moves to new positions by each team is unique. The moving equation of the worst performing team will be updated by the superiority of solutions produced by each team. The superiority of the solutions for each team is calculated based on the fitness value and boundary violations of solutions. The proposed algorithm is examined using computationally expensive constrained optimization problems taken from the CEC-2017 technical report. Computational test results have demonstrated the effectiveness of the AMTPG-Jaya algorithm when compared to the other well-known approaches. Also, a multi-objective optimization is carried out on a solar dish Stirling engine system to find the optimal thermo-economic parameters to maximize dimensionless power and thermal efficiency. The computational results revealed that the AMTPG-Jaya algorithm results are superior to those achieved by the other algorithms presented in this work.