An analytical framework for high-speed hardware particle swarm optimization

Damaj, Issam; Elshafei, Mohammed; El-Abd, Mohammed; Aydin, Mehmet Emin

Abstract

Engineering optimization techniques are computationally intensive and can challenge implementations on tightly-constrained embedded systems. Particle Swarm Optimization (PSO) is a well-known bio-inspired algorithm that is adopted in various applications, such as, transportation, robotics, energy, etc. In this paper, a high-speed PSO hardware processor is developed with focus on outperforming similar state-of-the-art implementations. In addition, the investigation comprises the development of an analytical framework that captures wide characteristics of optimization algorithm implementations, in hardware and software, using key simple and combined heterogeneous indicators. The framework proposes a combined Optimization Fitness Indicator that can classify the performance of PSO implementations when targeting different evaluation functions. The two targeted processing systems are Field Programmable Gate Arrays for hardware implementations and a high-end multi-core computer for software implementations. The investigation confirms the successful development of a PSO processor with appealing performance characteristics that outperforms recently presented implementations. The proposed hardware implementation attains 23,300 improvement ratio of execution times with an elliptic evaluation function. In addition, a speedup of 1777 times is achieved with a Shifted Schwefels function. Indeed, the developed framework successfully classifies PSO implementations according to multiple and heterogeneous properties for a variety of benchmark functions.

Citation

Damaj, I., Elshafei, M., El-Abd, M., & Aydin, M. E. (2020). An analytical framework for high-speed hardware particle swarm optimization. Microprocessors and Microsystems, 72, 102949. https://doi.org/10.1016/j.micpro.2019.102949

• Journal Article Type: Article
• Acceptance Date: Dec 2, 2019
• Online Publication Date: Dec 3, 2019
• Publication Date: Feb 1, 2020
• Deposit Date: Dec 4, 2019
• Publicly Available Date: Dec 4, 2020
• Journal: Microprocessors and Microsystems
• Print ISSN: 0141-9331
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 72
• Pages: 102949
• DOI: https://doi.org/10.1016/j.micpro.2019.102949
• Keywords: Computer Networks and Communications; Hardware and Architecture; Software; Artificial Intelligence
• Public URL: https://uwe-repository.worktribe.com/output/4764351