Issue |
RAIRO-Oper. Res.
Volume 58, Number 1, January-February 2024
|
|
---|---|---|
Page(s) | 253 - 280 | |
DOI | https://doi.org/10.1051/ro/2023195 | |
Published online | 08 February 2024 |
Simple population-based algorithms for solving optimization problems
Panimalar Institute of Technology, Chennai 600123, India
* Corresponding author: a.baaskar@gmail.com
Received:
8
October
2022
Accepted:
20
December
2023
Heuristic algorithms are simple yet powerful tools that are capable of yielding acceptable results in a reasonable execution time. Hence, they are being extensively used for solving optimization problems by researchers nowadays. Due to the quantum of computing power and hardware available today, a large number of dimensions and objectives are considered and analyzed effectively. This paper proposes new population-based metaheuristic algorithms that are capable of combining different strategies. The new strategies help in fast converging as well as trying to avoid local optima. The proposed algorithms could be used as single-phase as well as two-phase algorithms with different combinations and tuning parameters. “Best”, “Mean” and “Standard Deviation” are computed for thirty trials in each case. The results are compared with many efficient optimization algorithms available in the literature. Sixty-one popular un-constrained benchmark problems with dimensions varying from two to thousand and fifteen constrained real-world engineering problems are used for the analyses. The results show that the new algorithms perform better for several test cases. The suitability of the new algorithms for solving multi-objective optimization problems is also studied using five numbers of two-objective ZDT problems. Pure Diversity, Spacing, Spread and Hypervolume are the metrics used for the evaluation.
Mathematics Subject Classification: 90C26 / 90C59 / 90C06
Key words: Optimization / population-based algorithms / performance metrics / benchmark functions
© The authors. Published by EDP Sciences, ROADEF, SMAI 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.