Optimized Multi-Layer Perceptron Model

Updated 29 November 2025

Optimized multi-layer perceptron models are enhanced neural networks that integrate algorithmic innovations, automated architectural design, and hardware-specific adjustments to improve efficiency and accuracy.
They employ advanced techniques such as hybrid quasi-Newton methods, metaheuristic optimizers, and GA-based feature selection to systematically enhance model performance.
These optimizations enable robust applications in classification, regression, and signal modeling, effectively balancing accuracy with computational and resource constraints.

An optimized multi-layer perceptron (MLP) model refers to a class of neural architectures and methodologies in which the structure, parameters, and training procedures of standard MLPs are systematically enhanced for improved task performance, computational efficiency, or resource efficiency. Techniques span algorithmic innovations (e.g., hybrid and second-order optimization, hardware-aware training), architectural search, regularization, feature selection, and hardware-specific considerations. This broad paradigm has produced models with empirical or provable gains over classic feedforward MLPs across multiple domains, including classification, regression, signal modeling, and resource-constrained deployment.

1. Architectural Optimization and Automated Design

Optimizing the architecture—depth, width, activation types, and regularization—has become a core strategy. AutoML and neural architecture search frameworks enable automatic exploration of parameterized design spaces:

The search space Θ is typically specified by the number of layers (L), neurons per layer ({n_i}), activation functions ({a_i}), dropout rates ({d_i}), learning rate (η), batch size (B), and regularization (λ), sometimes including hardware knobs (H) such as resource limits and throughput targets.
Evolutionary algorithms (EAs) or other population-based methods (e.g., steady-state mutation/crossover loops) are common search engines, with evaluation metrics including validation accuracy (A), hardware throughput (T), and resource usage (R) (Colangelo et al., 2020).

Such methods systematically uncover Pareto-optimal trade-offs between performance and efficiency. For instance, widening a hidden layer by 50% may increase accuracy by 0.3–0.5% but double DSP usage on FPGAs. Shallow, wide architectures (L ≤ 3) are typically favored for tabular data with significant hardware constraints, whereas drops of only 0.1–0.2% in accuracy can enable 5–10× throughput boosts by moving away from the memory-bandwidth roofline (Colangelo et al., 2020).

2. Algorithmic and Training Enhancements

Advanced optimization techniques replace or augment gradient descent for improved convergence and global search:

Hybrid quasi-Newton backpropagation integrates BFGS Hessian approximations and Wolfe-condition line searches, yielding robust, self-tuning step-sizes and improved curvature adaptation over plain stochastic gradient descent. This approach avoids the tuning burden of fixed learning rates and converges robustly even in ill-conditioned regions (Chakraborty et al., 2012).
Metaheuristic optimizers, such as Whale Optimization Algorithm (WOA) and Fitness Dependent Optimizer (FDO), treat MLP parameter vectors as candidate solutions within a population. These methods rely on population position updates based on swarm intelligence, random walks, or fitness-driven steps, inherently enabling exploration and exploitation without the need for close supervision or differentiability. The FDO, for instance, demonstrated test accuracy improvements from 74.7% (classic BP-MLP) to up to 97% (FDO-MLP) on student achievement data (Abbas et al., 2022); analogously, MLP-WOA achieves ≈10% average RMSE reduction in wind speed forecasting (Samadianfard et al., 2020).

3. Feature Selection, Dimensionality Reduction, and Data Efficiency

Feature engineering remains a high-leverage axis for MLP optimization:

Genetic Algorithm (GA)-based feature selection encodes subsets as binary chromosomes and optimizes for validation-set accuracy after MLP retraining, consistently outperforming both unpruned and principal component analysis (PCA)-reduced alternatives in high-dimensional, noisy datasets (e.g., TinyFace, heart disease) (Al-Batah et al., 11 Jun 2025).
PCA projects input features onto the principal components that preserve high-variance directions; this is effective only when the underlying features are linearly correlated and not overly noisy, as evidenced by a marked accuracy drop for PCA-MLPs compared to GA-MLPs on more challenging tasks (Al-Batah et al., 11 Jun 2025).
Data mining and cleansing techniques—such as pruning samples by deviation from class mean or PCA-reconstruction error—yield significant memory (up to 67%) and computational savings with negligible accuracy loss for resource-constrained deployments (Pricope, 2021).

4. Specialized and Heterogeneous Architectures

Extensions to the standard MLP architecture further boost efficiency and interpretability:

Generalized Operational Perceptron (GOP) models replace fixed linear–activation neurons with a heterogeneous set of nodal, pooling, and activation operators from rich libraries. The progressive HeMLGOP algorithm greedily adds neurons and layers based on relative improvement in loss, selecting operator sets via random-network evaluation with closed-form linear solves (ridge-regularized pseudo-inverse), and fine-tunes only as needed. This often yields 2–40× more compact models than progressive homogeneous MLPs, with superior or equivalent accuracy (Tran et al., 2018).
Tailed Multi-Layer Perceptron (T-MLP) attaches explicit output branches (“tails”) to each hidden layer, enabling supervision at multiple depths and supporting level-of-detail (LoD) signal representation. Residual-additive and multiplicative tail designs regularize training, yield improved coarse representations, and reduce vanishing-scale errors. T-MLP matches or surpasses state-of-the-art methods in 3D shape and image fitting with tiny parameter and computational overheads (≈3–10% over base MLP) (Yang et al., 26 Aug 2025).
Feedforward MLP (FF-MLP) architectures grounded in linear discriminant analysis map class-conditional Gaussian mixture distributions to explicit partitions, region-isolation, and class-mergence stages, producing all weights and architecture without iterative training, and often matching or surpassing back-propagation MLP accuracy in a single analytical pass (Lin et al., 2020).

5. Resource-Aware and Hardware-Optimized MLPs

Practical deployments in resource-constrained environments—such as FPGAs or ASICs—demand joint algorithmic and circuit optimization:

Mapping MLP layer matrix multiplies to FPGA systolic arrays via tiling parameters (e.g., (P_r×P_c) grids, vector width) enables optimal throughput under hardware constraints. Design rules quantify how network size, batch size, and tiling impact accuracy, throughput, and memory use (Colangelo et al., 2020).
Circuit-level approximate multipliers with dynamically configurable error allow “dialing in” power–accuracy trade-offs. Empirical results show power savings of 13.33% (entire network, MNIST) with only a 0.92% accuracy loss; each 1% in energy reduction costs approximately 0.07% in accuracy, with much of the gain achieved by truncating low-order partial products in multipliers (Ghaderi et al., 14 Oct 2024).
Input feature compression and resource sharing (e.g., time-multiplexed neurons over cycles) further compound savings, making single-layer designs with reduced bitwidths and streamlined control logic highly effective in real-world deployments where memory and power budgets are constraining factors (Ghaderi et al., 14 Oct 2024, Pricope, 2021).

6. Optimized MLPs for Global Optimization and Signal Modeling

Some recent methodology generalizes MLP structure and optimization to broader mathematical landscapes:

The “MLPf” algorithm reformulates global optimization objectives as functionals over nested continuous mappings, derives gradients via functional derivatives (as opposed to classic backprop), and shapes the loss landscape with convex proxies and Kullback–Leibler divergence terms (Koh, 2023). This transforms parameter updates into ax+b neuron forms, yields more robust convergence on classic hard global optimization benchmarks (e.g., Lennard-Jones clusters), and avoids local traps endemic to standard gradient descent.
T-MLP’s explicit, trainable multi-depth outputs enable progressive, resource-efficient signal representations for 3D shapes and high-res images, with strong evidence that depth-supervised residual supervision enhances coarse and fine detail fidelity (Yang et al., 26 Aug 2025).

7. Empirical Outcomes and Best Practices

Consistent empirical results from these studies demonstrate substantial practical and theoretical gains:

Metaheuristic, hybrid, or second-order optimizers reliably outperform plain gradient descent on ill-conditioned or non-convex problems, particularly in limited data or resource-limited settings (Chakraborty et al., 2012, Abbas et al., 2022).
Data-driven feature/structure selection (GA or progressive growth/pruning) not only yields smaller and faster MLPs but also avoids the manual hyperparameter heuristics of classic architectures (Tran et al., 2018, Al-Batah et al., 11 Jun 2025).
Multi-objective optimization balancing accuracy, latency/throughput, and resource usage is central to deploying MLPs in hardware, with evolutionary or co-design search pipelines offering direct control over these trade-offs (Colangelo et al., 2020, Ghaderi et al., 14 Oct 2024).

In sum, the optimized multi-layer perceptron model is the result of coordinated improvements spanning algorithm design, architecture, feature engineering, and hardware adaptation. These advances have led to compact, efficient, and high-performing MLPs, with application-specific pipelines dictating the most effective combinations of structural and algorithmic optimizations.