Genetic Algorithm MATLAB Implementation for Traveling Salesman Problem with Greedy Operator Protection
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Resource Overview
This paper utilizes the global search capability of genetic algorithms to solve the Traveling Salesman Problem (TSP), designing a genetic algorithm with greedy operator individual protection. The MATLAB implementation efficiently computes satisfactory solutions through population initialization, fitness evaluation, selection, crossover with greedy enhancements, mutation operations, and elitism preservation.
Detailed Documentation
This paper employs the global search capability of genetic algorithms to solve the Traveling Salesman Problem (TSP). The Traveling Salesman Problem represents a classic combinatorial optimization challenge where the objective is to find the shortest possible route enabling a salesman to visit a series of cities exactly once and return to the origin city. To address this NP-hard problem, we designed a genetic algorithm incorporating greedy operator individual protection. The implementation utilizes MATLAB's matrix operations and optimization functions to efficiently handle population management, fitness calculation using path distance summation, tournament selection, ordered crossover with greedy local optimization, and mutation with swap operations. The algorithm successfully achieves satisfactory results through iterative evolution while maintaining solution diversity through protective mechanisms for high-quality individuals.
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