AI ANALYSIS
An enhanced blood-sucking leech optimization for training feedforward neural networks
This paper proposes an enhanced Blood-Sucking Leech Optimization (SBSLO) with the simplex method to train Feedforward Neural Networks (FNNs). SBSLO aims to quantify the discrepancy between anticipated and realistic outputs, assess training efficacy and classification accuracy, and establish optimal connection weights and bias thresholds. The simplex method strengthens directional exploration, bolsters population diversity, mitigates premature convergence, facilitates escape from local optima, and improves constraint processing. Experimental results across 17 datasets demonstrate SBSLO's superior stability, robustness, convergence speed, and solution quality compared to KOA, NRBO, HLOA, IAO, WO, PKO, EGO, HEOA, APO, FLO, PO, and BSLO.
Executive Impact & Core Metrics
SBSLO demonstrates significant advancements in optimizing neural network training, delivering superior performance across diverse datasets.
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Average Classification Accuracy
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Algorithm Comparison Superiority
Deep Analysis & Enterprise Applications
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BSLO Overview
The Blood-Sucking Leech Optimization (BSLO) is predicated on the foraging patterns of blood-sucking leeches in rice paddies, incorporating exploration, exploitation, switching mechanisms for directional leeches, recherche mechanisms for directionless leeches, and a re-tracking mechanism to achieve global discovery and local extraction for optimal solutions.
Simplex Method Integration
The Simplex method is a reliable and flexible linear programming method for systematically exploring solution vertices. It enhances global search efficiency, strengthens directional search, and improves constraint processing. Its integration with BSLO bolsters population diversity and mitigates premature convergence.
SBSLO Synergy
The SBSLO (BSLO with Simplex Method) achieves conflict-free coordination between global exploration and local exploitation. It utilizes deterministic operations of reflection, expansion, and contraction to finely explore high-quality solution domains, significantly reducing error. Its memoryless characteristic enables rapid adaptation to dynamic optimization problems.
Key Performance Indicator
0 Average Classification Accuracy Across 17 DatasetsSBSLO-based FNN Training Flow
Initialize BSLO population (Xall) & FNN structure
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Calculate fitness (MSE) & obtain optimal solution (Prey)
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Loop while t ≤ T
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Apply Re-tracking strategy (if t > t1)
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Refresh N1, N2, s, LV(i,j), k, k2
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Execute Directional Leech Strategies (Exploration/Exploitation)
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Execute Directionless Leech Strategies
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Confirm & alter solutions outside search area
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Recalculate fitness & refresh Prey
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Increment t and loop
| Comparative Dimension | SBSLO-simplex method | BSLO-GA | BSLO-PSO | BSLO-DE |
|---|---|---|---|---|
| Core search mechanism | Global exploration of BSLO, deterministic local refinement of simplex method | Random crossover/mutation operation, simulate biological evolution, heuristic search | Particle velocity update, simulate birds flock foraging, direction correction | Differential mutation, driven by individual differences, and the selection operation |
| Operational logic | Space-time separation, early exploration, later refinement, without conflict | Parallel overlapping, simultaneously perform crossover/mutation and BSLO, prone to conflict | Rely on historical optimality, guided by individual/global optimality, prone to lag | Rely on differential vectors, driven by inter-individual differences, without global perception |
| Robustness to noise | Strong, simplex method smooths noise through multi-solution comparison | Weak, random operation amplifies noise impact | Medium, particle velocity is susceptible to noise interference | Medium, differential variation is sensitive to noise |
| Adaptation scenario | Black box, non-smooth, noise, high-dimensional problem | Low-dimensional, smooth, noise-free problem | Low-to-medium dimensionality, continuous and smooth, noise-free problem | Low-to-medium dimensionality, continuous and smooth, noise-free problem |
Real-world Engineering Design: Cantilever Beam Optimization
The SBSLO was applied to optimize the cumulative weight of a cantilever beam, demonstrating strong adaptability and versatility. It achieved superior extraction fitness and assessment metrics compared to other algorithms. The optimal weight identified was 1.33997.
Key Result: Optimal Weight: 1.33997
Real-world Engineering Design: Three-bar Truss Optimization
The SBSLO effectively optimized the cumulative weight of a three-bar truss, highlighting its practicality and reliability. It enhanced constraint processing, emphasized robustness, and fostered convergence. The optimum weight found was 263.895723.
Key Result: Optimal Weight: 263.895723
Real-world Engineering Design: Tubular Column Optimization
SBSLO was used to ameliorate the cumulative cost of a tubular column, demonstrating increased population diversity, improved constraint processing efficiency, and enhanced solution quality. It identified an optimum cost of 26.499495.
Key Result: Optimal Cost: 26.499495
Real-world Engineering Design: Piston Lever Optimization
The SBSLO successfully minimized the cumulative oil volume for a piston lever, achieving global exploration coverage and local fine-tuning exploitation. It surpassed other algorithms in extraction fitness and assessment metrics. The optimum weight identified was 8.409875.
Key Result: Optimal Oil Volume: 8.409875
Quantify Your Potential ROI with SBSLO
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Estimated Annual Savings
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Your Strategic AI Implementation Roadmap
A phased approach to integrate SBSLO into your existing AI infrastructure, ensuring a smooth transition and measurable impact.
Phase 01: Discovery & Assessment
Comprehensive review of existing FNN models, data infrastructure, and current optimization challenges. Identify specific pain points and performance bottlenecks.
Phase 02: SBSLO Integration & Customization
Design and implement SBSLO-based training modules, customizing parameters for optimal performance on your unique datasets and network architectures. Initial testing on benchmark data.
Phase 03: Pilot Deployment & Validation
Deploy SBSLO-enhanced FNNs in a controlled pilot environment. Rigorous testing and validation against current systems, focusing on accuracy, convergence speed, and resource efficiency.
Phase 04: Full-Scale Rollout & Monitoring
Gradual rollout of SBSLO across relevant enterprise applications. Continuous monitoring of performance, automated adjustments, and ongoing support to ensure sustained benefits.
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