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Journals / International Journal of 3D Printing Technologies and Digital Industry / 2026 / Cilt: 10 Sayı: 1

IMPLEMENTATION OF GENETIC ALGORITHM ACCELERATOR ON FPGA FOR TRAVELING SALESMAN PROBLEM AND PERFORMING COMPARATIVE PERFORMANCE ANALYSIS

Cem Deniz Kumral(ISPARTA UYGULAMALI BİLİMLER ÜNİVERSİTESİ)0000-0002-1326-4537
Pages
176–191
Publication Date
2026-04-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This comparative analysis demonstrates that the proposed hardware-based approach offers a superior, cost-effective, and deterministic alternative to software-based solutions for real-time optimization and Edge AI applications.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The primary objective of this study is to perform a comprehensive comparative performance analysis between traditional processor-based systems and hardware-oriented architectures for solving the Traveling Salesman Problem (TSP).
📊
Key Findings & Data:On this way, a high-performance and scalable Genetic Algorithm (GA) accelerator was designed and implemented on a Xilinx Artix-7 Field-Programmable Gate Array (FPGA) using VHSIC Hardware Description Language (VHDL).
💡
Conclusion & Impact:This comparative analysis demonstrates that the proposed hardware-based approach offers a superior, cost-effective, and deterministic alternative to software-based solutions for real-time optimization and Edge AI applications.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Genetic Algorithm · FPGA · VHDL

Abstract

The primary objective of this study is to perform a comprehensive comparative performance analysis between traditional processor-based systems and hardware-oriented architectures for solving the Traveling Salesman Problem (TSP). On this way, a high-performance and scalable Genetic Algorithm (GA) accelerator was designed and implemented on a Xilinx Artix-7 Field-Programmable Gate Array (FPGA) using VHSIC Hardware Description Language (VHDL). The proposed architecture employs a Dual-Port Block RAM (BRAM) structure and a 4-stage parallel pipeline to overcome the computational bottlenecks and memory access latencies inherent in serial Central Processing Units (CPU) execution. The performance of the developed system was benchmarked against a Python-based CPU implementation using TSPLIB datasets. Experimental results reveal that the FPGA-based accelerator provides a big advantage, achieving a speedup ratio of up to 121.24x on used datasets. This comparative analysis demonstrates that the proposed hardware-based approach offers a superior, cost-effective, and deterministic alternative to software-based solutions for real-time optimization and Edge AI applications.

Özet

The primary objective of this study is to perform a comprehensive comparative performance analysis between traditional processor-based systems and hardware-oriented architectures for solving the Traveling Salesman Problem (TSP). On this way, a high-performance and scalable Genetic Algorithm (GA) accelerator was designed and implemented on a Xilinx Artix-7 Field-Programmable Gate Array (FPGA) using VHSIC Hardware Description Language (VHDL). The proposed architecture employs a Dual-Port Block RAM (BRAM) structure and a 4-stage parallel pipeline to overcome the computational bottlenecks and memory access latencies inherent in serial Central Processing Units (CPU) execution. The performance of the developed system was benchmarked against a Python-based CPU implementation using TSPLIB datasets. Experimental results reveal that the FPGA-based accelerator provides a big advantage, achieving a speedup ratio of up to 121.24x on used datasets. This comparative analysis demonstrates that the proposed hardware-based approach offers a superior, cost-effective, and deterministic alternative to software-based solutions for real-time optimization and Edge AI applications.

🔖

Cite This Article

Kumral, C. D. (2026). IMPLEMENTATION OF GENETIC ALGORITHM ACCELERATOR ON FPGA FOR TRAVELING SALESMAN PROBLEM AND PERFORMING COMPARATIVE PERFORMANCE ANALYSIS. International Journal of 3D Printing Technologies and Digital Industry, Cilt: 10 Sayı: 1, 176–191. https://doi.org/10.46519/ij3dptdi.1856787

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