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Sarkar T, Salauddin M, Mukherjee A, Shariati MA, Rebezov M, Tretyak L, Pateiro M, Lorenzo JM. Application of bio-inspired optimization algorithms in food processing. Curr Res Food Sci 2022; 5:432-450. [PMID: 35243356 PMCID: PMC8866069 DOI: 10.1016/j.crfs.2022.02.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 02/08/2022] [Accepted: 02/11/2022] [Indexed: 12/23/2022] Open
Abstract
Bio-inspired optimization techniques (BOT) are part of intelligent computing techniques. There are several BOTs available and many new BOTs are evolving in this era of industrial revolution 4.0. Genetic algorithm, particle swarm optimization, artificial bee colony, and grey wolf optimization are the techniques explored by researchers in the field of food processing technology. Although, there are other potential methods that may efficiently solve the optimum related problem in food industries. In this review, the mathematical background of the techniques, their application and the potential microbial-based optimization methods with higher precision has been surveyed for a complete and comprehensive understanding of BOTs along with their mechanism of functioning. These techniques can simulate the process efficiently and able to find the near-to-optimal value expeditiously.
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Affiliation(s)
- Tanmay Sarkar
- Department of Food Processing Technology, Malda Polytechnic, West Bengal State Council of Technical Education, Malda, 732102, West Bengal, India
| | - Molla Salauddin
- Department of Food Processing Technology, Mir Madan Mohanlal Govt. Polytechnic, West Bengal State Council of Technical Education, Nadia 741156, West Bengal, India
| | - Alok Mukherjee
- Government College of Engineering and Ceramic Technology, Kolkata, India
| | - Mohammad Ali Shariati
- Department of Scientific Research, K.G. Razumovsky Moscow State University of Technologies and Management (The First Cossack University), 109004, Moscow, Russian Federation
| | - Maksim Rebezov
- Department of Scientific Research, K.G. Razumovsky Moscow State University of Technologies and Management (The First Cossack University), 109004, Moscow, Russian Federation
- Biophotonics Center, Prokhorov General Physics Institute of the Russian Academy of Science, 119991, Moscow, Russian Federation
- Department of Scientific Research, V. M. Gorbatov Federal Research Center for Food Systems, 109316, Moscow, Russian Federation
| | - Lyudmila Tretyak
- Department of Metrology, Standardization and Certification, Orenburg State University, 460018, Orenburg, Russian Federation
| | - Mirian Pateiro
- Centro Tecnológico de La Carne de Galicia, Rúa Galicia Nº 4, Parque Tecnológico de Galicia, San Cibrao das Viñas, 32900, Ourense, Spain
| | - José M. Lorenzo
- Centro Tecnológico de La Carne de Galicia, Rúa Galicia Nº 4, Parque Tecnológico de Galicia, San Cibrao das Viñas, 32900, Ourense, Spain
- Universidade de Vigo, Área de Tecnoloxía dos Alimentos, Facultade de Ciencias, 32004 Ourense, Spain
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Information Literacy Assessment with a Modified Hybrid Differential Evolution with Model-Based Reinitialization. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2018; 2018:9745639. [PMID: 30425734 PMCID: PMC6217889 DOI: 10.1155/2018/9745639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2018] [Revised: 07/30/2018] [Accepted: 09/03/2018] [Indexed: 12/04/2022]
Abstract
Information literacy assessment is extremely important for the evaluation of the information literacy skills of college students. Intelligent optimization technique is an effective strategy to optimize the weight parameters of the information literacy assessment index system (ILAIS). In this paper, a new version of differential evolution algorithm (DE), named hybrid differential evolution with model-based reinitialization (HDEMR), is proposed to accurately fit the weight parameters of ILAIS. The main contributions of this paper are as follows: firstly, an improved contraction criterion which is based on the population entropy in objective space and the maximum distance in decision space is employed to decide when the local search starts. Secondly, a modified model-based population reinitialization strategy is designed to enhance the global search ability of HDEMR to handle complex problems. Two types of experiments are designed to assess the performance of HDEMR. In the first type of experiments, HDEMR is tested and compared with seven well-known DE variants on CEC2005 and CEC2014 benchmark functions. In the second type of experiments, HDEMR is compared with the well-known and widely used deterministic algorithm DIRECT on GKLS test classes. The experimental results demonstrate the effectiveness of HDEMR for global numerical optimization and show better performance. Furthermore, HDEMR is applied to optimize the weight parameters of ILAIS at China University of Geosciences (CUG), and satisfactory results are obtained.
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