Polar Bear Optimization Algorithm: Meta-Heuristic with Fast Population Movement and Dynamic Birth and Death Mechanism
In the proposed article, we present a nature-inspired optimization algorithm, which we called Polar Bear Optimization Algorithm (PBO). The inspiration to develop the algorithm comes from the way polar bears hunt to survive in harsh arctic conditions. These carnivorous mammals are active all year rou...
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ftdoajarticles:oai:doaj.org/article:5fed7e775a834f04ba82386cb9032a85 2023-05-15T15:10:47+02:00 Polar Bear Optimization Algorithm: Meta-Heuristic with Fast Population Movement and Dynamic Birth and Death Mechanism Dawid Połap Marcin Woz´niak 2017-09-01T00:00:00Z https://doi.org/10.3390/sym9100203 https://doaj.org/article/5fed7e775a834f04ba82386cb9032a85 EN eng MDPI AG https://www.mdpi.com/2073-8994/9/10/203 https://doaj.org/toc/2073-8994 2073-8994 doi:10.3390/sym9100203 https://doaj.org/article/5fed7e775a834f04ba82386cb9032a85 Symmetry, Vol 9, Iss 10, p 203 (2017) optimization meta-heuristic constrained optimization Mathematics QA1-939 article 2017 ftdoajarticles https://doi.org/10.3390/sym9100203 2022-12-31T00:17:03Z In the proposed article, we present a nature-inspired optimization algorithm, which we called Polar Bear Optimization Algorithm (PBO). The inspiration to develop the algorithm comes from the way polar bears hunt to survive in harsh arctic conditions. These carnivorous mammals are active all year round. Frosty climate, unfavorable to other animals, has made polar bears adapt to the specific mode of exploration and hunting in large areas, not only over ice but also water. The proposed novel mathematical model of the way polar bears move in the search for food and hunt can be a valuable method of optimization for various theoretical and practical problems. Optimization is very similar to nature, similarly to search for optimal solutions for mathematical models animals search for optimal conditions to develop in their natural environments. In this method. we have used a model of polar bear behaviors as a search engine for optimal solutions. Proposed simulated adaptation to harsh winter conditions is an advantage for local and global search, while birth and death mechanism controls the population. Proposed PBO was evaluated and compared to other meta-heuristic algorithms using sample test functions and some classical engineering problems. Experimental research results were compared to other algorithms and analyzed using various parameters. The analysis allowed us to identify the leading advantages which are rapid recognition of the area by the relevant population and efficient birth and death mechanism to improve global and local search within the solution space. Article in Journal/Newspaper Arctic polar bear Directory of Open Access Journals: DOAJ Articles Arctic Symmetry 9 10 203 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
optimization meta-heuristic constrained optimization Mathematics QA1-939 |
spellingShingle |
optimization meta-heuristic constrained optimization Mathematics QA1-939 Dawid Połap Marcin Woz´niak Polar Bear Optimization Algorithm: Meta-Heuristic with Fast Population Movement and Dynamic Birth and Death Mechanism |
topic_facet |
optimization meta-heuristic constrained optimization Mathematics QA1-939 |
description |
In the proposed article, we present a nature-inspired optimization algorithm, which we called Polar Bear Optimization Algorithm (PBO). The inspiration to develop the algorithm comes from the way polar bears hunt to survive in harsh arctic conditions. These carnivorous mammals are active all year round. Frosty climate, unfavorable to other animals, has made polar bears adapt to the specific mode of exploration and hunting in large areas, not only over ice but also water. The proposed novel mathematical model of the way polar bears move in the search for food and hunt can be a valuable method of optimization for various theoretical and practical problems. Optimization is very similar to nature, similarly to search for optimal solutions for mathematical models animals search for optimal conditions to develop in their natural environments. In this method. we have used a model of polar bear behaviors as a search engine for optimal solutions. Proposed simulated adaptation to harsh winter conditions is an advantage for local and global search, while birth and death mechanism controls the population. Proposed PBO was evaluated and compared to other meta-heuristic algorithms using sample test functions and some classical engineering problems. Experimental research results were compared to other algorithms and analyzed using various parameters. The analysis allowed us to identify the leading advantages which are rapid recognition of the area by the relevant population and efficient birth and death mechanism to improve global and local search within the solution space. |
format |
Article in Journal/Newspaper |
author |
Dawid Połap Marcin Woz´niak |
author_facet |
Dawid Połap Marcin Woz´niak |
author_sort |
Dawid Połap |
title |
Polar Bear Optimization Algorithm: Meta-Heuristic with Fast Population Movement and Dynamic Birth and Death Mechanism |
title_short |
Polar Bear Optimization Algorithm: Meta-Heuristic with Fast Population Movement and Dynamic Birth and Death Mechanism |
title_full |
Polar Bear Optimization Algorithm: Meta-Heuristic with Fast Population Movement and Dynamic Birth and Death Mechanism |
title_fullStr |
Polar Bear Optimization Algorithm: Meta-Heuristic with Fast Population Movement and Dynamic Birth and Death Mechanism |
title_full_unstemmed |
Polar Bear Optimization Algorithm: Meta-Heuristic with Fast Population Movement and Dynamic Birth and Death Mechanism |
title_sort |
polar bear optimization algorithm: meta-heuristic with fast population movement and dynamic birth and death mechanism |
publisher |
MDPI AG |
publishDate |
2017 |
url |
https://doi.org/10.3390/sym9100203 https://doaj.org/article/5fed7e775a834f04ba82386cb9032a85 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic polar bear |
genre_facet |
Arctic polar bear |
op_source |
Symmetry, Vol 9, Iss 10, p 203 (2017) |
op_relation |
https://www.mdpi.com/2073-8994/9/10/203 https://doaj.org/toc/2073-8994 2073-8994 doi:10.3390/sym9100203 https://doaj.org/article/5fed7e775a834f04ba82386cb9032a85 |
op_doi |
https://doi.org/10.3390/sym9100203 |
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Symmetry |
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10 |
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203 |
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1766341740038455296 |