Abstract
The Jaya algorithm is a recently developed novel population-based algorithm. The proposed work presents the modifications in the existing many-objective Jaya (MaOJaya) algorithm by integrating the chaotic sequence to improve the performance to optimize many-objective benchmark optimization problems. The MaOJaya algorithm has exploitation more dominating, due to which it traps in local optima. The proposed work aims to reduce these limitations by modifying the solution update equation of the MaOJaya algorithm. The purpose of the modification is to balance the exploration and exploitation, improve the divergence and avoid premature convergence. The well-known chaotic sequence - a logistic map integrated into the solution update equation. This modification keeps the MaOJaya algorithm simple as well as, preserves its parameterless feature. The other component of the existing MaOJaya algorithm, such as non-dominated sorting, reference vector and tournament selection scheme of NSGA-II is preserved. The decomposition approach used in the proposed approach simplifies the complex many-objective optimization problems. The performance of the proposed chaotic based many-objective Jaya (C-MaOJaya) algorithm is tested on DTLZ benchmark functions for three to ten objectives. The IGD and Hypervolume performance metrics evaluate the performance of the proposed C-MaOJaya algorithm. The statistical tests are used to compare the performance of the proposed C-MaOJaya algorithm with the MaOJaya algorithm and other algorithms from the literature. The C-MaOJaya algorithm improved the balance between exploration and exploitation and avoids premature convergence significantly. The comparison shows that the proposed C-MaOJaya algorithm is a promising approach to solve many-objective optimization problems.
Highlights
The solutions obtained to optimization problems using the approaches inspired by the natural phenomena are largely affected by the exploration and exploitation strategies used in that algorithm
The results obtained by the chaotic based many-objective Jaya (MaOJaya) algorithm are compared with the results found in the literature using well-known statistical tests
The result indicates that the proposed C-MaOJaya algorithm performs significantly better than NSGA-III, MOEA/D, MOEA/DD, RVEA, and MOEA/D-M2M algorithms for three, five, eight, and ten objectives
Summary
The optimization problems inherently exist in various scientific and engineering domains. The increased objective functions posed challenges in developing the evolutionary approaches to solve such problems. Authors have presented the pros and cons of the existing as well as newly developed methods to address the many-objective optimization problems (Taha, 2020, Mane & NarsingRao, 2017). The solutions obtained to optimization problems using the approaches inspired by the natural phenomena are largely affected by the exploration and exploitation strategies used in that algorithm. As the Jaya algorithm growing towards the best possible fitness value, so the exploitation is more dominating (Ingle & Jatoth, 2020).The Many Objective Jaya (MaOJaya) Optimization algorithm developed to solve the many-objective optimization problems (Mane et al, 2018).
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