Design Optimization Using Modified Differential Evolution Algorithm

Pooja Tiwari; Vishnu Narayan Mishra; Raghav Prasad Parouha

doi:10.17485/IJST/v16i43.1392

Article

Design Optimization Using Modified Differential Evolution Algorithm

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v16i43.1392

Year: 2023, Volume: 16, Issue: 43, Pages: 3917-3926

Original Article

Design Optimization Using Modified Differential Evolution Algorithm

Pooja Tiwari¹, Vishnu Narayan Mishra^1*, Raghav Prasad Parouha¹

¹Department of Mathematics, Indira Gandhi National Tribal University, , Amarkantak, 484887, Madhya Pradesh, India

*Corresponding Author
Email: [email protected]

Received Date:06 June 2023, Accepted Date:09 October 2023, Published Date:17 November 2023

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: The key objective of this article is to suggest a modified differential evolution (MDE) algorithm for design problem optimization particularly reactor network design (RND) problem. Methods: During the few decades differential evolution (DE) algorithm achieved noticeable progress and solved a wide variety of optimization issues. However, the DE suffers from low diversification, poor exploration ability and stagnation. Hence, using concept of the particle swarm optimization mechanism (PSO), suggested MDE employed new mutation operator, to balance exploitation and exploration activities. Also, on the basis of time-varying scheme new mutation operator integrates new control parameter, to avoid stagnation. A group of 6 unconstrained benchmark functions are solved, to investigate the presentation of MDE algorithm. Moreover, its practical superiority is further verified by solving RND problem. Findings: The experiential results show that the suggested MDE performs well in each case of unconstrained benchmark functions with the highest rate of success. Moreover, optimize the RND problem very effectively with the lowest time (2.98s) and fewer number of function evaluations (12729). Furthermore, outcomes suggest that the proposed MDE exhibits a better or at least competitive performance compared to evolutionary algorithms. Novelty: The exploitation and exploration ability of the suggested MDE are balanced efficiently due to use of memory facts (i.e. novel mutation operator) and adapted (i.e. new time-varying) control parameters.

Keywords: Evolutionary algorithms, differential evolution, mutation, control parameter, design optimization

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Copyright

© 2023 Tiwari et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)