A hybrid intelligent algorithm is proposed. The algorithm utilizes a particle swarm and a Tabu search algorithm. Swarm based algorithms and single agent based algorithms each, have distinct advantages and disadvantages. The goal of the presented work is to combine the strengths of the two different algorithms in order to achieve a more effective optimization routine. The developed hybrid algorithm is tailored such that it has the capability to adapt to the given cost function during the optimization process. The proposed algorithm is tested on a set of different benchmark problems. In addition, the hybrid algorithm is utilized for solving the estimation problem encountered for estimating the finger force output given a surface electromyogram (sEMG) signal at the input. This estimation problem is commonly encountered while developing a control system for a prosthetic hand.
- Dynamic Systems and Control Division
Hybrid Particle Swarm: Tabu Search Optimization Algorithm for Parameter Estimation
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Sebastian, A, & Schoen, MP. "Hybrid Particle Swarm: Tabu Search Optimization Algorithm for Parameter Estimation." Proceedings of the ASME 2013 Dynamic Systems and Control Conference. Volume 2: Control, Monitoring, and Energy Harvesting of Vibratory Systems; Cooperative and Networked Control; Delay Systems; Dynamical Modeling and Diagnostics in Biomedical Systems; Estimation and Id of Energy Systems; Fault Detection; Flow and Thermal Systems; Haptics and Hand Motion; Human Assistive Systems and Wearable Robots; Instrumentation and Characterization in Bio-Systems; Intelligent Transportation Systems; Linear Systems and Robust Control; Marine Vehicles; Nonholonomic Systems. Palo Alto, California, USA. October 21–23, 2013. V002T20A006. ASME. https://doi.org/10.1115/DSCC2013-4041
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