Improved multi-objective Jaya optimization algorithm for a solar dish Stirling engine

Abstract

This study proposes an adaptive multi-team perturbation guiding Jaya (AMTPG Jaya) algorithm. The proposed approach uses multiple teams to explore the search space. Depending on the percentage of consumed function evaluations, the AMTPG Jaya algorithm adjusts the number of teams in the search. Furthermore, all teams move from a single population set and are simultaneously guided by different perturbation equations to different regions of search space. As each team has a different perturbation scheme, the set of moves to new positions by each team is unique. During the search process, the perturbation equations are exchanged depending on the quality of the solutions produced by them. The proposed algorithm employs dominance principles and the crowding distance estimation method to handle the multiple objectives simultaneously. The proposed algorithm is examined using two multi-objective optimization case studies of a solar dish Stirling heat engine system and a multi-objective optimization case study of the Stirling heat pump. Also, the Technique for Order of Preference by Similarity to Ideal Solution decision-making method is employed for identifying an optimal solution. The computational results obtained by the proposed AMTPG Jaya algorithm are superior to those achieved by the other algorithms presented in this work.