Version 1
: Received: 16 August 2022 / Approved: 17 August 2022 / Online: 17 August 2022 (08:35:44 CEST)
How to cite:
Saha, D.; Sallam, K. M.; De, S.; Mohamed, A. W. Framework of Ensemble Parmeter Adapted Evolutionary Algorithm for Solving Constrained Optimization Problems. Preprints2022, 2022080307. https://doi.org/10.20944/preprints202208.0307.v1
Saha, D.; Sallam, K. M.; De, S.; Mohamed, A. W. Framework of Ensemble Parmeter Adapted Evolutionary Algorithm for Solving Constrained Optimization Problems. Preprints 2022, 2022080307. https://doi.org/10.20944/preprints202208.0307.v1
Saha, D.; Sallam, K. M.; De, S.; Mohamed, A. W. Framework of Ensemble Parmeter Adapted Evolutionary Algorithm for Solving Constrained Optimization Problems. Preprints2022, 2022080307. https://doi.org/10.20944/preprints202208.0307.v1
APA Style
Saha, D., Sallam, K. M., De, S., & Mohamed, A. W. (2022). Framework of Ensemble Parmeter Adapted Evolutionary Algorithm for Solving Constrained Optimization Problems. Preprints. https://doi.org/10.20944/preprints202208.0307.v1
Chicago/Turabian Style
Saha, D., Shuvodeep De and Ali W. Mohamed. 2022 "Framework of Ensemble Parmeter Adapted Evolutionary Algorithm for Solving Constrained Optimization Problems" Preprints. https://doi.org/10.20944/preprints202208.0307.v1
Abstract
Real-world optimization problems are often governed by one or more constraints. Over the last few decades, extensive research has been performed in Constrained Optimization Problems (COPs) fueled by advances in computational intelligence. In particular, Evolutionary Algorithms (EAs) are a preferred tool for practitioners for solving these COPs within practicable time limits. We propose an ensemble of multi- method hybrid EA framework with four mutation operators, two crossover operators, multi-search [Differential Evolution (DE) & Gaining Sharing Knowledge (GSK)] optimization algorithm, and ensemble of constraint handling techniques to solve global real- world constrained optimization problem. The proposed frame- work FEPEA has an ascendancy of multiple adaptation strategies concerning the control parameters, search mechanisms, two sub-populations as well as uses knowledge sharing mechanism between junior and senior phases. The algorithm also combines the power of four popular constraint handling techniques (CHT) and uses a voting mechanism to select any particular CHT. On top of that, this algorithm also uses both linear and non- linear population size reduction in every step of the evolutionary process. We test our method on 57 real-world problems provided as part of the CEC 2020 special session & competition on real- world constrained optimization benchmark suite. Experimental results indicate that FEPEA is able to achieve state-of-the- art performance on real-world constrained global optimization when compared against other well-known real-world constrained optimizers.
Computer Science and Mathematics, Data Structures, Algorithms and Complexity
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
