PCOBL: A Novel Opposition-Based Learning Strategy to Improve Metaheuristics Exploration and Exploitation for Solving Global Optimization Problems-Reference-Cited by-同舟云学术

PCOBL: A Novel Opposition-Based Learning Strategy to Improve Metaheuristics Exploration and Exploitation for Solving Global Optimization Problems

Published:2023 Issue: Volume:11 Page:46413-46440
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Si Tapas¹^ORCID,Bhattacharya Debolina²^ORCID,Nayak Somen³^ORCID,Miranda Péricles B. C.⁴^ORCID,Nandi Utpal⁵^ORCID,Mallik Saurav⁶^ORCID,Maulik Ujjwal⁷^ORCID,Qin Hong⁸

Affiliation:

1. Department of Computer Science & Engineering, University of Engineering & Management, Jaipur, Rajasthan, India

2. Department of Computer Science & Engineering, Sarala Birla University, Ranchi, Jharkhand, India

3. Department of Computer Application, University of Engineering & Management, Jaipur, Rajasthan, India

4. Departamento de Computação (DC), Universidade Federal Rural de Pernambuco (UFRPE), Recife, Brazil

5. Department of Computer Science, Vidyasagar University, Midnapore, Paschim Medinipur, West Bengal, India

6. Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA, USA

7. Department of Computer Science and Engineering, Jadavpur University, Kolkata, India

8. Department of Computer Science and Engineering, The University of Tennessee at Chattanooga, Chattanooga, TN, USA

Funder

USA NSF Award

USA National Academy of Medicine under the Catalyst Award

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

https://ieeexplore.ieee.org/ielam/6287639/10005208/10119133-aam.pdf

Reference47 articles.

1. Particle swarm optimization with chaotic opposition-based population initialization and stochastic search technique

2. A novel hybrid sine cosine algorithm for global optimization and its application to train multilayer perceptrons

3. Enhancing particle swarm optimization using generalized opposition-based learning

4. A memory guided sine cosine algorithm for global optimization

5. Dynamic regional harmony search with opposition and local learning

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