Parallel design of SFO optimization algorithm based on FPGA

• Published in: The Journal of supercomputing Vol. 80; no. 8; pp. 10796 - 10817
• Main Authors: Naji, Hamid Reza, Shadravan, Soodeh, Jafarabadi, Hossien Mousa, Momeni, Hossien
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.05.2024; Springer Nature B.V
• Subjects: Algorithms; Central processing units; Compilers; Computer Science; Computing time; Convergence; CPUs; Design optimization; Field programmable gate arrays; Graphics processing units; Heuristic methods; Interpreters; Optimization algorithms; Parallel processing; Physics; Processor Architectures; Programming Languages; Reconfigurable hardware; Simulation; Field programmable gate array; Parallel programming; SFO algorithm; Metaheuristic algorithm; Finite state machine
• ISSN: 0920-8542; 1573-0484
• DOI: 10.1007/s11227-023-05851-7

Taking a lot of time to solve optimization problems has become a challenge for metaheuristic algorithms. Due to independence of the metaheuristics components, parallel processing is a good option to reduce the computational time and to find high quality solutions that are close to the optimum with an acceptable cost. One of these metaheuristic algorithms is the Sailfish Optimizer (SFO) which is inspired by a group of hunting sailfish. The SFO algorithm uses a simple method to provide a dynamic balance between exploration and exploitation phases, creates a swarm diversity, avoids local optima, and guarantees high convergence speed. It has been shown that the SFO algorithm outperforms various state-of-art metaheuristic algorithms for multimodal and high dimensional benchmark functions and complicated real-world optimization problems in terms of accuracy and speed by CPU and GPU implementation. In this paper, to speedup this algorithm and increase its performance we propose a reconfigurable hardware version of SFO implemented on Field Programmable Gate Array (FPGA). The FPGA-based SFO can be a very good option in many applications with massive calculations. Due to the inherent parallelism and high computing capabilities of FPGA, the SFO algorithm gains optimum computational time despite the complexity of optimization problems. We have compared the performance of the proposed FPGA-based SFO with its CPU and GPU implementation and some other metaheuristic algorithms. The results show the FPGA implementation is considerably faster than the CPU and GPU implementation. Also, it outperforms other compared FPGA-based metaheuristic algorithms in terms of execution time and convergence speed.