Hybrid Metaheuristic Algorithm (Sagac) Used in Optimization of Vacuum Cooling Treatment of Postharvest Broccoli / Algoritmo Metaheurístico Híbrido (Sagac) Usado na Otimização do Tratamento de Resfriamento a Vácuo do Broccolis Pós-Colheita
Abstract
This research aims to analyze the application of the hybrid metaheuristic algorithm SAGAC, which is composed of the Simulated Annealing (SA) and Genetic Algorithm (GA) techniques with the inclusion of a convergence acceleration (AC) mechanism. SAGAC was used to optimize the postharvest broccoli vacuum cooling process. Another concern included in the algorithm is population diversity, and, for this situation, a high mutation rate (40%) and a low elitism rate (10%) were used. The objective of maintaining population diversity is to avoid premature and undue convergence of the results curve. The SAGAC algorithm's performance was compared with another type of approach in optimizing this process, which used the Response Surface (RSM) methodology combined with the Genetic Algorithm (GA), here, called RSMGA in this present study. The results obtained showed that the SAGAC algorithm obtained better results concerning RSMGA in optimizing this process.
Key-words: Algorithms. Optimization. SAGAC. Metaheurístics.
RESUMO
Esta pesquisa tem o objetivo de analisar a aplicação do algoritmo metaheurístico híbrido SAGAC, o qual é composto dos algoritmos, Simulated Annealing (SA) e o Algoritmo Genético (GA) com a inclusão de um mecanismo de aceleração de convergência de resultados (AC). O SAGAC foi usado para otimizar o processo de resfriamento à vácuo do brócolis após a colheita. Outra preocupação incluída no algoritmo é a diversidade da população, e, para essa situação, foram utilizadas uma alta taxa de mutação (40%) e uma baixa taxa de elitismo (10%). O objetivo de manter a diversidade populacional é evitar a convergência prematura e indevida da curva de resultados. O desempenho do algoritmo SAGAC foi comparado com outro tipo de abordagem na otimização deste processo, que utilizou a metodologia de Superfície de Resposta (RSM) combinada com o Algoritmo Genético (GA), aqui, denominado RSMGA no presente estudo. Os resultados obtidos mostraram que o algoritmo SAGAC obteve melhores resultados em relação ao RSMGA na otimização deste processo.
Palavras-chave: Algoritmos. Otimização. SAGAC. Metaheurística.
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DOI: http://dx.doi.org/10.12819/2021.18.7.10
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ISSN 1806-6356 (Print) and 2317-2983 (Electronic)