IALA-BPNN-MOEDO
Year: 2025
Type: article
Abstract: This codebase implements a complete multi-objective optimization framework for robotic arm structural design, comprising three core modules: ALA (metaheuristic algorithm comparison and ablation studies), BPNN (neural network surrogate model training), and MOPSO (MOEDO-based multi-objective optimizer). The framework generates training data via finite element analysis, optimizes BPNN parameters using an improved artificial lemming algorithm(IALA), and ultimately achieves Pareto-optimal designs for geometric parameters (mass, stress, deformation). Suitable for structural optimization, algorithmic research, and engineering applications.
Author: Yu, Haiyu
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7077144522
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