Open Access

ARTICLE

Modified Differential Evolution Algorithm for Solving Dynamic Optimization with Existence of Infeasible Environments

by Mohamed A. Meselhi^*, Saber M. Elsayed, Daryl L. Essam, Ruhul A. Sarker

School of Engineering and Information Technology University of New South Wales Canberra, Australia

* Corresponding Author: Mohamed A. Meselhi. Email:

Computers, Materials & Continua 2023, 74(1), 1-17. https://doi.org/10.32604/cmc.2023.027448

Received 19 January 2022; Accepted 06 June 2022; Issue published 22 September 2022

Abstract

Dynamic constrained optimization is a challenging research topic in which the objective function and/or constraints change over time. In such problems, it is commonly assumed that all problem instances are feasible. In reality some instances can be infeasible due to various practical issues, such as a sudden change in resource requirements or a big change in the availability of resources. Decision-makers have to determine whether a particular instance is feasible or not, as infeasible instances cannot be solved as there are no solutions to implement. In this case, locating the nearest feasible solution would be valuable information for the decision-makers. In this paper, a differential evolution algorithm is proposed for solving dynamic constrained problems that learns from past environments and transfers important knowledge from them to use in solving the current instance and includes a mechanism for suggesting a good feasible solution when an instance is infeasible. To judge the performance of the proposed algorithm, 13 well-known dynamic test problems were solved. The results indicate that the proposed algorithm outperforms existing recent algorithms with a margin of 79.40% over all the environments and it can also find a good, but infeasible solution, when an instance is infeasible.

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Keywords

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