A Novel Parameter Optimization Metaheuristic: Human Habitation Behavior Based Optimization

Parameters have great impact on the performance of an optimization algorithm. This paper concerns parameter tuning for metaheuristics which are stochastic optimization algorithms. A novel, fast, and very simple population-based metaheuristic namely Human Habitation Behavior-Based optimization (HHBO)...

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Bibliographic Details
Published in2022 5th International Conference on Contemporary Computing and Informatics (IC3I) pp. 921 - 924
Main Authors Jain, Divya, Arya, Mithlesh, Malik, Varun, Singh, S Vikram
Format Conference Proceeding
LanguageEnglish
Published IEEE 14.12.2022
Subjects
Feed-forward neural networks
Metaheuristics
Parameter tuning
Weight and bias optimization
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DOI10.1109/IC3I56241.2022.10072699

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Summary:Parameters have great impact on the performance of an optimization algorithm. This paper concerns parameter tuning for metaheuristics which are stochastic optimization algorithms. A novel, fast, and very simple population-based metaheuristic namely Human Habitation Behavior-Based optimization (HHBO) is proposed in order to tune parameters of the metaheuristics used for the weight and bias optimization problem in feed-forward neural networks. The proposed algorithm is compared with other state-of-art algorithms, and results and analysis are presented. The results show the merits of HHBO for parameter tuning in comparison of other state-of-art algorithms.
DOI:10.1109/IC3I56241.2022.10072699