A Hybrid of Fully Informed Particle Swarm and Self-Adaptive Differential Evolution for Global Optimization Article Swipe
Shir Li Wang
,
Sarah Hazwani Adnan
,
Haidi Ibrahim
,
Theam Foo Ng
,
Parvathy Rajendran
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/app122211367
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/app122211367
Evolutionary computation algorithms (EC) and swarm intelligence have been widely used to solve global optimization problems. The optimal solution for an optimization problem is called by different terms in EC and swarm intelligence. It is called individual in EC and particle in swarm intelligence. Self-adaptive differential evolution (SaDE) is one of the promising variants of EC for solving global optimization problems. Adapting self-manipulating parameter values into SaDE can overcome the burden of choosing suitable parameter values to create the next best generation’s individuals to achieve optimal convergence. In this paper, a fully informed particle swarm (FIPS) is hybridized with SaDE to enhance SaDE’s exploitation capability while maintaining its exploration power so that it is not trapped in stagnation. The proposed hybrid is called FIPSaDE. FIPS, a variant of particle swarm optimization (PSO), aims to help solutions jump out of stagnation by gathering knowledge about its neighborhood’s solutions. Each solution in the FIPS swarm is influenced by a group of solutions in its neighborhood, rather than by the best position it has visited. Indirectly, FIPS increases the diversity of the swarm. The proposed algorithm is tested on benchmark test functions from “CEC 2005 Special Session on Real-Parameter Optimization” with various properties. Experimental results show that the FIPSaDE is more effective and reasonably competent than its standalone variants, FIPS and SaDE, in solving the test functions, considering the solutions’ quality.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app122211367
- https://www.mdpi.com/2076-3417/12/22/11367/pdf?version=1668651122
- OA Status
- gold
- Cited By
- 2
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4308869723
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4308869723Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/app122211367Digital Object Identifier
- Title
-
A Hybrid of Fully Informed Particle Swarm and Self-Adaptive Differential Evolution for Global OptimizationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-11-09Full publication date if available
- Authors
-
Shir Li Wang, Sarah Hazwani Adnan, Haidi Ibrahim, Theam Foo Ng, Parvathy RajendranList of authors in order
- Landing page
-
https://doi.org/10.3390/app122211367Publisher landing page
- PDF URL
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https://www.mdpi.com/2076-3417/12/22/11367/pdf?version=1668651122Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.mdpi.com/2076-3417/12/22/11367/pdf?version=1668651122Direct OA link when available
- Concepts
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Particle swarm optimization, Differential evolution, Multi-swarm optimization, Benchmark (surveying), Mathematical optimization, Convergence (economics), Swarm intelligence, Computer science, Swarm behaviour, Optimization problem, Metaheuristic, Mathematics, Geodesy, Geography, Economic growth, EconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2025: 1, 2023: 1Per-year citation counts (last 5 years)
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- Related works (count)
-
10Other works algorithmically related by OpenAlex
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