4.7 Article

Elitist Non-dominated Sorting directional Bat algorithm (ENSdBA)

Journal

EXPERT SYSTEMS WITH APPLICATIONS
Volume 227, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2023.120292

Keywords

Directional Bat algorithm; Elitism; Metaheuristics; Multiobjective optimization; Nondominated sorting; Swarm optimization

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This paper proposes a novel method for performing nondominated sorting in a multiobjective optimization problem using a modified directional Bat algorithm. Unlike NSGA-II, the proposed algorithm generates and compares new solutions with all previous solutions, reducing computational time and generating diverse solutions. A unique sorting method using a Nondomination matrix is introduced, which can be easily updated to include new solutions and preserve elitism. Detailed criteria are provided for the selection of new solutions. Experimental results show that the proposed algorithm is competitive and outperforms other algorithms in terms of efficiency and other performance metrics for most benchmark optimization problems. The algorithm also provides a standardized platform for nondomination sorting, applicable to any other metaheuristic algorithm.
A novel way of performing nondominated sorting in a multiobjective optimization problem is proposed using a modified directional Bat algorithm. Unlike NSGA-II, where the solutions of two generations are merged and then sorted for elitism, in the proposed algorithm, the solution is generated and compared with all the previous so-lutions one by one. Hence, this method reduces the computational time by avoiding the comparison of solutions of two generations, at the same time, generates a diverse solution. A unique way of sorting the solutions is proposed using a Nondomination matrix, which can easily be updated if a new solution is accepted. The Non -domination matrix serves as an archiving strategy to preserve elitism. Detailed criteria are proposed for the selection of a new solution. We have tested the proposed algorithm on some of the standard benchmark opti-mization problems. The results show that the proposed algorithm is very competitive and outperforms other algorithms in terms of efficiency and other performance metrics for most problems. The algorithm also provides a standard platform for nondomination sorting, which can be applied to any other metaheuristic algorithm.

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