ArticleBiomimetics (Basel, Switzerland)2022
Bio-inspired Machine Learning for Distributed Confidential Multi-Portfolio Selection Problem.
Article in Biomimetics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 25 citations in OpenAlex.
- Advances in Zeroing Neural Networks: Bio-Inspired Structures, Performance Enhancements, and Applications.Biomimetics (Basel, Switzerland) · 2025Review
- An adapted Black Widow Optimization Algorithm for Financial Portfolio Optimization Problem with cardinalty and budget constraints.Scientific reports · 2024Article
- A Survey on Biomimetic and Intelligent Algorithms with Applications.Biomimetics (Basel, Switzerland) · 2024Article
- Named Entity Recognition of Diabetes Online Health Community Data Using Multiple Machine Learning Models.Bioengineering (Basel, Switzerland) · 2023Article
- Biology-Informed Recurrent Neural Network for Pandemic Prediction Using Multimodal Data.Biomimetics (Basel, Switzerland) · 2023Article
- Bionic Path Planning Fusing Episodic Memory Based on RatSLAM.Biomimetics (Basel, Switzerland) · 2023Article
- Advances on intelligent algorithms for scientific computing: an overview.Frontiers in neurorobotics · 2023Review
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Authors and funding
4 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The recently emerging multi-portfolio selection problem lacks a proper framework to ensure that client privacy and database secrecy remain intact. Since privacy is of major concern these days, in this paper, we propose a variant of Beetle Antennae Search (BAS) known as Distributed Beetle Antennae Search (DBAS) to optimize multi-portfolio selection problems without violating the privacy of individual portfolios. DBAS is a swarm-based optimization algorithm that solely shares the gradients of portfolios among the swarm without sharing private data or portfolio stock information. DBAS is a hybrid framework, and it inherits the swarm-like nature of the Particle Swarm Optimization (PSO) algorithm with the BAS updating criteria. It ensures a robust and fast optimization of the multi-portfolio selection problem whilst keeping the privacy and secrecy of each portfolio intact. Since multi-portfolio selection problems are a recent direction for the field, no work has been done concerning the privacy of the database nor the privacy of stock information of individual portfolios. To test the robustness of DBAS, simulations were conducted consisting of
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