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Entropy–Based Diversification Approach for Bio–Computing Methods

Nature–inspired computing is a promising field of artificial intelligence. This area is mainly devoted to designing computational models based on natural phenomena to address complex problems. Nature provides a rich source of inspiration for designing smart procedures capable of becoming powerful al...

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Autores principales: Olivares, Rodrigo, Soto, Ricardo, Crawford, Broderick, Riquelme, Fabián, Munoz, Roberto, Ríos, Víctor, Cabrera, Rodrigo, Castro, Carlos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9498173/
https://www.ncbi.nlm.nih.gov/pubmed/36141179
http://dx.doi.org/10.3390/e24091293
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author Olivares, Rodrigo
Soto, Ricardo
Crawford, Broderick
Riquelme, Fabián
Munoz, Roberto
Ríos, Víctor
Cabrera, Rodrigo
Castro, Carlos
author_facet Olivares, Rodrigo
Soto, Ricardo
Crawford, Broderick
Riquelme, Fabián
Munoz, Roberto
Ríos, Víctor
Cabrera, Rodrigo
Castro, Carlos
author_sort Olivares, Rodrigo
collection PubMed
description Nature–inspired computing is a promising field of artificial intelligence. This area is mainly devoted to designing computational models based on natural phenomena to address complex problems. Nature provides a rich source of inspiration for designing smart procedures capable of becoming powerful algorithms. Many of these procedures have been successfully developed to treat optimization problems, with impressive results. Nonetheless, for these algorithms to reach their maximum performance, a proper balance between the intensification and the diversification phases is required. The intensification generates a local solution around the best solution by exploiting a promising region. Diversification is responsible for finding new solutions when the main procedure is trapped in a local region. This procedure is usually carryout by non-deterministic fundamentals that do not necessarily provide the expected results. Here, we encounter the stagnation problem, which describes a scenario where the search for the optimum solution stalls before discovering a globally optimal solution. In this work, we propose an efficient technique for detecting and leaving local optimum regions based on Shannon entropy. This component can measure the uncertainty level of the observations taken from random variables. We employ this principle on three well–known population–based bio–inspired optimization algorithms: particle swarm optimization, bat optimization, and black hole algorithm. The proposal’s performance is evidenced by solving twenty of the most challenging instances of the multidimensional knapsack problem. Computational results show that the proposed exploration approach is a legitimate alternative to manage the diversification of solutions since the improved techniques can generate a better distribution of the optimal values found. The best results are with the bat method, where in all instances, the enhanced solver with the Shannon exploration strategy works better than its native version. For the other two bio-inspired algorithms, the proposal operates significantly better in over 70% of instances.
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spelling pubmed-94981732022-09-23 Entropy–Based Diversification Approach for Bio–Computing Methods Olivares, Rodrigo Soto, Ricardo Crawford, Broderick Riquelme, Fabián Munoz, Roberto Ríos, Víctor Cabrera, Rodrigo Castro, Carlos Entropy (Basel) Article Nature–inspired computing is a promising field of artificial intelligence. This area is mainly devoted to designing computational models based on natural phenomena to address complex problems. Nature provides a rich source of inspiration for designing smart procedures capable of becoming powerful algorithms. Many of these procedures have been successfully developed to treat optimization problems, with impressive results. Nonetheless, for these algorithms to reach their maximum performance, a proper balance between the intensification and the diversification phases is required. The intensification generates a local solution around the best solution by exploiting a promising region. Diversification is responsible for finding new solutions when the main procedure is trapped in a local region. This procedure is usually carryout by non-deterministic fundamentals that do not necessarily provide the expected results. Here, we encounter the stagnation problem, which describes a scenario where the search for the optimum solution stalls before discovering a globally optimal solution. In this work, we propose an efficient technique for detecting and leaving local optimum regions based on Shannon entropy. This component can measure the uncertainty level of the observations taken from random variables. We employ this principle on three well–known population–based bio–inspired optimization algorithms: particle swarm optimization, bat optimization, and black hole algorithm. The proposal’s performance is evidenced by solving twenty of the most challenging instances of the multidimensional knapsack problem. Computational results show that the proposed exploration approach is a legitimate alternative to manage the diversification of solutions since the improved techniques can generate a better distribution of the optimal values found. The best results are with the bat method, where in all instances, the enhanced solver with the Shannon exploration strategy works better than its native version. For the other two bio-inspired algorithms, the proposal operates significantly better in over 70% of instances. MDPI 2022-09-14 /pmc/articles/PMC9498173/ /pubmed/36141179 http://dx.doi.org/10.3390/e24091293 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Olivares, Rodrigo
Soto, Ricardo
Crawford, Broderick
Riquelme, Fabián
Munoz, Roberto
Ríos, Víctor
Cabrera, Rodrigo
Castro, Carlos
Entropy–Based Diversification Approach for Bio–Computing Methods
title Entropy–Based Diversification Approach for Bio–Computing Methods
title_full Entropy–Based Diversification Approach for Bio–Computing Methods
title_fullStr Entropy–Based Diversification Approach for Bio–Computing Methods
title_full_unstemmed Entropy–Based Diversification Approach for Bio–Computing Methods
title_short Entropy–Based Diversification Approach for Bio–Computing Methods
title_sort entropy–based diversification approach for bio–computing methods
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9498173/
https://www.ncbi.nlm.nih.gov/pubmed/36141179
http://dx.doi.org/10.3390/e24091293
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