Jaya R Package -- A Parameter-Free Solution for Advanced Single and Multi-Objective Optimization

TL;DR

A case study on energy modeling explores the optimization of renewable energy shares, showcasing the Jaya R package's ability to minimize carbon emissions and costs while enhancing system reliability.

Abstract

The Jaya R package offers a robust and versatile implementation of the parameter-free Jaya optimization algorithm, suitable for solving both single-objective and multi-objective optimization problems. By integrating advanced features such as constraint handling, adaptive population management, Pareto front tracking for multi-objective trade-offs, and parallel processing for computational efficiency, the package caters to a wide range of optimization challenges. Its intuitive design and flexibility allow users to solve complex, real-world problems across various domains. To demonstrate its practical utility, a case study on energy modeling explores the optimization of renewable energy shares, showcasing the package's ability to minimize carbon emissions and costs while enhancing system reliability. The Jaya R package is an invaluable tool for researchers and practitioners seeking efficient and adaptive optimization solutions.

Figures (7)

• Figure 1: Block diagram of the Jaya algorithm.
• Figure 2: Convergence plot showing the evolution of the best objective value over iterations for minimizing a sphere function using the Jaya algorithm.
• Figure 3: Pairwise scatter plots of the Pareto front obtained from the multi-objective optimization of two sphere functions. The plots show trade-offs between objectives: minimizing the sum of squares and minimizing the sum of squares offset by 2.
• Figure 4: 3D Pareto Front of Energy System Optimization (Colored by Total Contribution).
• Figure 5: 3D Pareto Front of Energy System Optimization (Colored by Wind Contribution).