Abstract
The Traveling Salesman Problem (TSP) stands as one of the earliest and
most pervasive optimization challenges, aiming to streamline the
salesman\'s travel route, ensuring efficiency and avoiding redundancy.
With an extensive number of cities to visit and the requirement to find
the shortest route, solving the TSP becomes computationally daunting.
To tackle this, various approaches are explored, including the genetic
algorithm. This study employs a modified genetic algorithm, inspired by
biological processes such as population dynamics, crossover, mutation,
and natural selection. Introducing a novel intersection approach
amalgamating sliding flipping and swapping methods, the study
investigates towns with populations ranging from (10, 50) to (150, 200)
across two scenarios. In the first scenario, experiments were conducted
with a fixed iterations (10000) and a population is (150), resulting in
execution times of 20.00 s, 55.14 s, 37.11 s, and 120.14 s when choose
the cities (100, 200, 10, 20), respectively. In the second scenario, the
population sizes of 50 or 100, and iteration numbers of 1000 or 500 were
explored. The times needed for solution determination were 01.20 s,
02.30 s, 02.74 s, and 17.15 s for cities (10, 20, 100, 200), in accordance.
Notably, the study’s outcomes indicated that there is a proportional
relationship between iteration, population, and the number of cities.
When integrating the three techniques, a shorter and faster path is
achieved compared to the standard genetic algorithm outcomes
most pervasive optimization challenges, aiming to streamline the
salesman\'s travel route, ensuring efficiency and avoiding redundancy.
With an extensive number of cities to visit and the requirement to find
the shortest route, solving the TSP becomes computationally daunting.
To tackle this, various approaches are explored, including the genetic
algorithm. This study employs a modified genetic algorithm, inspired by
biological processes such as population dynamics, crossover, mutation,
and natural selection. Introducing a novel intersection approach
amalgamating sliding flipping and swapping methods, the study
investigates towns with populations ranging from (10, 50) to (150, 200)
across two scenarios. In the first scenario, experiments were conducted
with a fixed iterations (10000) and a population is (150), resulting in
execution times of 20.00 s, 55.14 s, 37.11 s, and 120.14 s when choose
the cities (100, 200, 10, 20), respectively. In the second scenario, the
population sizes of 50 or 100, and iteration numbers of 1000 or 500 were
explored. The times needed for solution determination were 01.20 s,
02.30 s, 02.74 s, and 17.15 s for cities (10, 20, 100, 200), in accordance.
Notably, the study’s outcomes indicated that there is a proportional
relationship between iteration, population, and the number of cities.
When integrating the three techniques, a shorter and faster path is
achieved compared to the standard genetic algorithm outcomes
Keywords
Genetic Algorithm.
Modify
Travelling Salesman Problem