Enhanced Jaya Algorithm for Multi-objective Optimisation Problems

Abstract

Evolutionary algorithms are suitable techniques for solving complex problems. Many improvements have been made on the original structure algorithm in order to obtain more desirable solutions. The current study intends to enhance multi-objective performance with benchmark optimisation problems by incorporating the chaotic inertia weight into the current multi-objective Jaya (MOJaya) algorithm. Essentially, Jaya is a recently established population-oriented algorithm. Exploitation proves to be more dominant in MOJaya following its propensity to capture local optima. This research addressed the aforementioned shortcoming by refining the MOJaya algorithm solution to update the equation for exploration-exploitation balance, enhancing divergence, and deterring premature convergence to retain the algorithm fundamentals while simultaneously sustaining its parameter-free component. The recommended chaotic inertia weight-multi-objective Jaya (MOiJaya) algorithm was assessed using well-known ZDT benchmark functions with 30 variables, whereas the convergence matrix (CM) and divergence matrix (DM) analysed the suggested MOiJaya algorithm performances are inspected. As such, this algorithm enhanced the exploration-exploitation balance and substantially prevented premature convergence. Then, the proposed algorithm is compared with a few other algorithms. Based on the comparison, the convergence metric and diversity metric results show that the recommended MOiJaya algorithm potentially resolved multi-objective optimisation problems better than the other algorithms.