In dynamic and information-rich contexts, systems must be capable of making instantaneous, context-aware decisions. Such scenarios require optimization methods that are both fast and flexible. This paper introduces an innovative hardware-based intelligent optimization framework, deployed on FPGAs, designed to support autonomous decisions in real-time systems. In contrast to conventional methods based on a single chaotic map, our scheme brings together six separate chaotic generators in simultaneous operation, orchestrated by an adaptive voting system based on past results. The system, in conjunction with the Secretary Bird Optimization Algorithm (SBOA), constantly adjusts its optimization approach according to the changing profile of the objective function. This delivers first-rate, timely solutions with improved convergence, resistance to local minima, and a high degree of adaptability to a variety of decision-making contexts. Simulations carried out on reference standards and engineering problems have demonstrated the scalability, responsiveness, and efficiency of the proposed model. These characteristics make it particularly suitable for use in embedded intelligence applications in sectors such as intelligent production, robotics, and IoT-based infrastructures. The suggested solution was tested using post-synthesis simulations on Vivado 2022.2 and experimented on three concrete engineering challenges: welded beam design, pressure equipment design, and tension/compression spring refinement. In each situation, the adaptive selection process dynamically determined the most suitable chaotic map, such as the logistics map for the Welded Beam Design Problem (WBDP) and the Tent map for the Pressure Vessel Design Problem (PVDP). This led to ideal results that exceed both conventional static methods and recent references in the literature. The post-synthesis results on the Nexys 4 DDR (Artix-7 XC7A100T, Digilent Inc., Pullman, WA, USA) show that the initial Q16.16 implementation exceeded the device resources (128% LUTs and 100% DSPs), whereas the optimized Q4.8 representation achieved feasible deployment with 80% LUT utilization, 72% DSP usage, and 3% FF occupancy. This adjustment reduced resource consumption by more than 25% while maintaining sufficient computational accuracy.

Satellite image transmission systems face a dual challenge: efficient compression and robust encryption. Existing approaches treat these requirements sequentially, resulting in doubled latency and energy overhead, while no FPGA implementation of orthogonal moments exists in the literature. This work presents the first unified FPGA framework transforming adaptive orthogonal moments into dual compression-encryption vectors. The system implements four moment families (Charlier, Hahn, Meixner, Krawtchouk) on Xilinx ZedBoard Zynq-7000, with real-time adaptive selection (93.8% accuracy) and confusion-diffusion chaotic encryption applied directly to coefficients. Three major contributions are demonstrated: (i) first FPGA hardware architecture for orthogonal moments via hybrid PC/FPGA strategy, (ii) adaptive multi-family framework optimized for satellite energy constraints, (iii) unified pipeline eliminating intermediate stages. Implemented at 125 MHz on ZedBoard, the system achieves PSNR of 27–36 dB for Sentinel-2 images of \(64 \times 64\) pixels (4.6:1 compression), 728 ns latency, and robust security (7.99 bits entropy, correlations \(< 0.021\)).

Automated brain tumor classification from MRI scans requires optimized CNN architectures deployable on embedded FPGA platforms. This paper presents an integrated approach combining the Multi-Chaotic Enhanced HEOA (MC-HEOA) for automatic CNN architecture discovery with deployment validation on a Xilinx Zynq-7000 FPGA. A CEC2023 benchmark across 10 test functions evaluates 6 chaotic maps and selects the Tent map as the optimal diversity generator. The NAS search space spans a massive combinatorial space of $1.31 \times {10}^{16}$ configurations encoding architectural choices (layers, convolutions, channels, pooling) under a strict constraint of fewer than one million parameters for FPGA compatibility. The optimal discovered architecture, trained and evaluated using single-channel grayscale input (224 × 224 × 1)—the natural representation for intrinsically monochromatic MRI data— achieves 91.33% test accuracy and 92.44% validation accuracy with 724,200 parameters on the 4-class Brain Tumor MRI dataset (glioma, meningioma, pituitary, no tumor). HLS synthesis on the Zynq-7000 (xc7z020clg484-1) validates embedded deployment feasibility, with DSP utilization of 16%, LUT utilization of 57%, FF utilization of 28%, and an inference latency of 374 ms at 100 MHz. This study demonstrates the effectiveness of MC-HEOA for discovering compact, high-performing CNN architectures compatible with FPGA deployment, opening new perspectives for real-time embedded medical diagnosis.