Solving Traveling Salesman Problem Using Genetic Algorithm with MATLAB Implementation
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Resource Overview
Various optimization problems including Traveling Salesman Problem (TSP), postal route planning, nut assembly line sequencing, and production scheduling can be formulated as TSP instances. This MATLAB implementation utilizes genetic algorithm to solve TSP, featuring complete sub-functions for fitness calculation, selection operators, crossover operations, and mutation mechanisms with detailed code-level descriptions.
Detailed Documentation
The Traveling Salesman Problem (TSP) serves as a fundamental model for numerous optimization challenges including postal route optimization, assembly line nut sequencing, and production scheduling arrangements. To address TSP effectively, we developed a MATLAB-based solution employing genetic algorithms. The implementation comprises complete modular functions: fitness evaluation calculates route distances using Euclidean metrics, selection operators employ tournament or roulette wheel methods, crossover operations implement ordered or cycle crossover techniques, and mutation mechanisms include swap or inversion mutations. Each sub-function is meticulously coded to ensure algorithmic efficiency and solution quality. This comprehensive approach enables robust TSP resolution with enhanced computational performance and solution accuracy through iterative population evolution and convergence mechanisms.
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