Robotic Arm Motion Trajectory Optimization

This paper presents a MATLAB implementation for scheduling algorithm simulation, specifically focusing on Simulated Annealing (SA) optimization. SA is a heuristic optimization algorithm particularly effective for solving complex combinatorial optimization problems. Our implementation includes MATLAB code that demonstrates SA's core components: temperature scheduling, neighbor solution generation, and probability-based acceptance criteria. The code architecture features modular functions for: - Solution initialization using random or heuristic-based approaches - Energy calculation (objective function evaluation) - Neighborhood search operations with customizable mutation strategies - Exponential cooling schedules with adjustable parameters - Convergence monitoring and performance metrics tracking Through systematic parameter tuning (initial temperature, cooling rate, iteration counts) and multiple initial condition tests, we optimize algorithm performance for scheduling applications. The implementation also includes visualization tools for tracking solution improvement and convergence patterns. Furthermore, we discuss SA's adaptability to other optimization domains like path planning and resource allocation, while examining its practical limitations regarding convergence speed and solution quality in large-scale problems. The code provides a framework for extending SA to multi-objective optimization problems with minimal modifications.