research-article

A hybrid Aquila Optimizer sine cosine Algorithm for Numerical Optimization

Authors:

Fulin TianAuthors Info & Claims

CACML '23: Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning

Pages 258 - 263

https://doi.org/10.1145/3590003.3590048

Published: 29 May 2023 Publication History

Abstract

To address the shortcomings of the Aquila optimizer algorithm (AO), this paper proposes a novel hybrid Aquila Optimizer sine cosine Algorithm(AO-SCA). Firstly, Singer chaotic mapping is used for initialization, so that the initial solution position distribution was more homogeneous, and increased the richness of the population. Secondly, in the exploration phase of AO, the concept of sine and cosine algorithm is integrated and the nonlinear sine learning factor is introduced to balance the local and global digging ability and accelerate the convergence speed. Finally, through the numerical experiment simulation of 8 benchmark functions, the results show that the optimization ability and convergence speed of the proposed algorithm is better.

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Cited By

Zhu YZhang YZhang CZhang BWang HFeng S(2024)Detection of false data injection in electric energy metering platforms using gradient lifting decision trees and MLP neural networksDiscover Applied Sciences10.1007/s42452-024-06450-87:1Online publication date: 26-Dec-2024
https://doi.org/10.1007/s42452-024-06450-8

Index Terms

A hybrid Aquila Optimizer sine cosine Algorithm for Numerical Optimization
1. Computing methodologies
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization

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CACML '23: Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning

March 2023

598 pages

ISBN:9781450399449

DOI:10.1145/3590003

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 May 2023

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CACML 2023

CACML 2023: 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning

March 17 - 19, 2023

Shanghai, China

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CACML '23 Paper Acceptance Rate 93 of 241 submissions, 39%;

Overall Acceptance Rate 93 of 241 submissions, 39%

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Cited By

Zhu YZhang YZhang CZhang BWang HFeng S(2024)Detection of false data injection in electric energy metering platforms using gradient lifting decision trees and MLP neural networksDiscover Applied Sciences10.1007/s42452-024-06450-87:1Online publication date: 26-Dec-2024
https://doi.org/10.1007/s42452-024-06450-8

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