Ameliorated Ensemble Strategy-Based Evolutionary Algorithm with Dynamic Resources Allocations

Authors

Wali Khan Mashwani^1, , Syed Nouman Ali Shah¹, Samir Brahim Belhaouari^2, , Abdelouahed Hamdi^{3, *}

¹Institute of Numerical Sciences, Kohat University of Science and Technology, Kohat, Pakistan

²Division of Information and Computing Technology, College of Science and Engineering Hamad Bin Khalifa University, Education City, Qatar Foundation, Doha, Qatar

³Department of Mathematics, Statistics and Physics, Qatar University, Doha, Qatar

^*Corresponding author. Email: abhamdi@qu.edu.qa

DOI

10.2991/ijcis.d.201215.005How to use a DOI?

Keywords

Global optimization; Soft computing; Evolutionary computing; Evolutionary algorithms (EAs); Ensemble strategy-based EAs

Abstract

In the last two decades, evolutionary computing has become the mainstream to attract the attention of the experts in both academia and industrial applications due to the advent of the fast computer with multi-core GHz processors have had a capacity of processing over billion instructions per second. Today's different evolutionary algorithms are found in the existing literature of evolutionary computing that is mainly belong to swarm intelligence and nature-inspired algorithms. In general, it is quite realistic that not always each developed evolutionary algorithms can perform all kinds of optimization and search problems. Recently, ensemble-based techniques are considered to be a good alternative for dealing with various benchmark functions and real-world problems. In this paper, an ameliorated ensemble strategy-based evolutionary algorithm is developed for solving large-scale global optimization problems. The suggested algorithm employs the particle swam optimization, teaching learning-based optimization, differential evolution, and bat algorithm with a self-adaptive procedure to evolve their randomly generated set of solutions. The performance of the proposed ensemble strategy-based evolutionary algorithm evaluated over thirty benchmark functions that are recently designed for the special session of the IEEE congress of evolutionary computation (CEC'17). The experimental results provided by the suggested algorithm over most CEC'17 benchmark functions are much promising in terms of proximity and diversity.

Copyright

© 2021 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Wali Khan Mashwani
AU  - Syed Nouman Ali Shah
AU  - Samir Brahim Belhaouari
AU  - Abdelouahed Hamdi
PY  - 2020
DA  - 2020/12/28
TI  - Ameliorated Ensemble Strategy-Based Evolutionary Algorithm with Dynamic Resources Allocations
JO  - International Journal of Computational Intelligence Systems
SP  - 412
EP  - 437
VL  - 14
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.201215.005
DO  - 10.2991/ijcis.d.201215.005
ID  - Mashwani2020
ER  -