TSP Problem Solutions using Genetic Algorithm with Four Implementation Scenarios

Genetic Algorithm implementations for solving the Traveling Salesman Problem (TSP) can be categorized into four distinct scenarios. The first scenario involves Multiple Traveling Salesmen with a Single Starting Point, where multiple salesmen commence their routes from a common origin point. Code implementation typically includes designing chromosome structures that encode multiple routes while ensuring they all share the same starting node, with fitness functions evaluating collective route efficiency.

The second scenario covers Multiple Traveling Salesmen with Multiple Starting Points, where each salesman begins from different designated locations. Algorithm implementation requires specialized initialization procedures that assign unique starting points to each salesman's route, along with crossover and mutation operations that maintain starting point constraints throughout evolutionary iterations.

The third scenario addresses Single Traveling Salesman Using Point Coordinates Calculation, where optimal paths are determined by computing Euclidean distances between geographical coordinates. Implementation involves distance calculation functions using coordinate data, with path optimization focusing on minimizing total Euclidean distance traveled through genetic operators like ordered crossover and inversion mutation.

The fourth scenario involves Single Traveling Salesman Using Distance Matrix Calculation, where optimal routes are derived from predefined distance matrices between all node pairs. This approach utilizes matrix lookup operations within fitness evaluation functions, allowing for asymmetric distance considerations and custom distance metrics that aren't limited to Euclidean space constraints.

Each scenario employs genetic algorithm techniques with specific chromosome encoding schemes, selection mechanisms (such as tournament selection or roulette wheel), and specialized genetic operators tailored to the problem constraints. The implementations demonstrate how genetic algorithms can adapt to various TSP configurations while maintaining solution quality and computational efficiency.