The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Opposition-Based Differential Evolution
Abstract
Evolutionary algorithms (EAs) are well-known optimization approaches to deal with nonlinear and complex problems. However, these population-based algorithms are computationally expensive due to the slow nature of the evolutionary process. This paper presents a novel algorithm to accelerate the differential evolution (DE). The proposed opposition-based DE (ODE) employs opposition-based learning (OBL) for population initialization and also for generation jumping. In this work, opposite numbers have been utilized to improve the convergence rate of DE. A comprehensive set of 58 complex benchmark functions including a wide range of dimensions is employed for experimental verification. The influence of dimensionality, population size, jumping rate, and various mutation strategies are also investigated. Additionally, the contribution of opposite numbers is empirically verified. We also provide a comparison of ODE to fuzzy adaptive DE (FADE). Experimental results confirm that the ODE outperforms the original DE and FADE in terms of convergence speed and solution accuracy.
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Cites
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Opposition-Based Learning: A New Scheme for Machine Intelligence
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Self-citationParameter control in evolutionary algorithms
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Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
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Differential Evolution: A Survey of the State-of-the-Art
Das, S., & Suganthan, P. N. (2011) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2010.2059031 · Cited by 5,307
Exploration and exploitation in evolutionary algorithms: A survey
Črepinšek, M., Liu, S.-H., & Mernik, M. (2013) · ACM Computing Surveys · doi:10.1145/2480741.2480752 · Cited by 1,298
Differential evolution with multi-population based ensemble of mutation strategies
Wu, G., Mallipeddi, R., Suganthan, P. N., Wang, R., & Chen, H. (2016) · Information Sciences · doi:10.1016/j.ins.2015.09.009 · Cited by 569
Differential Evolution: A survey of theoretical analyses
Opara, K. R., & Arabas, J. (2019) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2018.06.010 · Cited by 473
Metaheuristics in large-scale global continues optimization: A survey
Mahdavi, S., Shiri, M. E., & Rahnamayan, S. (2015) · Information Sciences · doi:10.1016/j.ins.2014.10.042 · Cited by 465
Self-citation
Metadata
- Authors
- Rahnamayan, S., Tizhoosh, H. R., & Salama, M. M. A.
- Venue
- IEEE Transactions on Evolutionary Computation
- Published
- 2008-02
- Volume
- 12
- Issue
- 1
- Pages
- 64-79
- ISSN
- 1089-778X (ISSN-L)
- 1941-0026
- OpenAlex
- W2123682012
- Cited by
- 1,630
- References
- 29
- FWCI
- 112.89
- Type
- journal article
- Language
- en
- Open access
- closed
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