A Novel, Low Computational Complexity, Parallel Swarm Algorithm for Application in Low-Energy Devices.

Sensors (Basel)

Faculty of Telecommunication, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, 85-796 Bydgoszcz, Poland.

Published: December 2021

AI Article Synopsis

  • This work presents a new metaheuristic algorithm based on traditional particle swarm optimization (PSO) designed for low-energy applications in miniaturized devices.
  • Key modifications were made to reduce computational complexity, improving energy efficiency and making it suitable for devices like wireless sensor networks that have limited power sources.
  • The algorithm demonstrated comparable or superior performance to conventional PSO across various fitness functions, with a focus on simplifying hardware implementation while maintaining speed and minimizing energy use.

Article Abstract

In this work, we propose a novel metaheuristic algorithm that evolved from a conventional particle swarm optimization (PSO) algorithm for application in miniaturized devices and systems that require low energy consumption. The modifications allowed us to substantially reduce the computational complexity of the PSO algorithm, translating to reduced energy consumption in hardware implementation. This is a paramount feature in the devices used, for example, in wireless sensor networks (WSNs) or wireless body area sensors (WBANs), in which particular devices have limited access to a power source. Various swarm algorithms are widely used in solving problems that require searching for an optimal solution, with simultaneous occurrence of a different number of sub-optimal solutions. This makes the hardware implementation worthy of consideration. However, hardware implementation of the conventional PSO algorithm is challenging task. One of the issues is an efficient implementation of the randomization function. In this work, we propose novel methods to work around this problem. In the proposed approach, we replaced the block responsible for generating random values using deterministic methods, which differentiate the trajectories of particular particles in the swarm. Comprehensive investigations in the software model of the modified algorithm have shown that its performance is comparable with or even surpasses the conventional PSO algorithm in a multitude of scenarios. The proposed algorithm was tested with numerous fitness functions to verify its flexibility and adaptiveness to different problems. The paper also presents the hardware implementation of the selected blocks that modify the algorithm. In particular, we focused on reducing the hardware complexity, achieving high-speed operation, while reducing energy consumption.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8703726PMC
http://dx.doi.org/10.3390/s21248449DOI Listing

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