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Intelligent Decision Making Through Bio-Inspired Optimization: Ant Colony Optimization for Real-World Scenarios

Intelligent Decision Making Through Bio-Inspired Optimization: Ant Colony Optimization for Real-World Scenarios
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Author(s): J. Angel Ida Chellam (Sri Ramakrishna Engineering College, India), Preethi Muthukumar (Sri Ramakrishna Engineering College, India)and M. Senthamil Selvi (Sri Ramakrishna Engineering College, India)
Copyright: 2024
Pages: 13
Source title: Intelligent Decision Making Through Bio-Inspired Optimization
Source Author(s)/Editor(s): Ramkumar Jaganathan (Sri Krishna Arts and Science College, India), Shilpa Mehta (Auckland University of Technology, New Zealand)and Ram Krishan (Mata Sundri University Girls College, Mansa, India)
DOI: 10.4018/979-8-3693-2073-0.ch005

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Abstract

Ant colony optimization is a popular metaheuristic algorithm inspired by the foraging behavior of ants. It's often used to solve complex optimization problems. Train scheduling is a challenging problem involving the allocation of train resources, routes, and timetables to optimize various objectives like minimizing travel time, energy consumption. In train scheduling, this usually involves defining nodes and arcs to represent railway stations, tracks, and possible connections to optimize, well-defined, such as minimizing the total travel time or energy consumption. The process of ant movement and pheromone update is repeated for a predefined number of iterations or until a termination condition is met. Vehicle routing problem with time frames, the time dependent VRP, the VRP with pickup and delivery, and the dynamic VRP were developed to bring the VRP closer to the types of situations found in the real world. The optimum route serving all clients utilizing a driver's maximum working hours and minimizing total transportation cost is one solution to this problem.

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