Bee colony optimisation of the travelling salesman problem in light rail systems

Abstract

The travelling salesman problem in transit planning is an extremely complex and non-deterministic polynomial problem. Many optimisation algorithms have been tested but failed to facilitate passengers getting to multiple stations by way of a complex rail transit network. Although various algorithms based on social insect species have successfully solved complex problems, bee behaviour in nature has inspired more significant solutions. The study reported in this paper aimed to identify the most efficient algorithm and develop a mathematical model based on artificial bee colony optimisation to solve this problem in light rail transit systems. For validation, the developed model was contrasted with empirical routes generated by human experience. The model was able to generate the optimal route in all cases without any error or miscalculation and could thus be used in any rail transit network in the world to determine the optimal route for multiple stations.

Prioritizing Road Network Restorative Interventions Using a Discrete Particle Swarm Optimization

“…In these systems, in absence of a centralized control structure to dictate the individual behavior of the agents, the local and to some degree random interactions among the agents lead to an intelligent global behavior that is unknown to the individual agents. Examples of optimization algorithms using swarm intelligence include PSO (Clerc 2004;Gao et al 2012;Wang et al 2003), ACS (Dorigo and Gambardella 1997;Nilsson 2003;Yan and Shih 2012), and artificial bee colony (ABC) (Pandey and Kumar 2013;Wang et al 2019). According to the literature, PSO is more widely used in continuous optimization problems; ACS is used to deal with discrete optimization problems, and ABC is mostly implemented in optimizing multivariable objective functions with few parameters (Gao et al 2012).…”

Minimizing Cost of Highway Maintenance Considering the Impact of Vehicle Emissions using an Artificial Bee Colony Approach

“…The performance of ABC is inspired by the intelligent foraging behavior of honeybee swarms, and employs three types of artificial bees—employed bees, onlooker bees, and scout bees—to search for the optimal solution. In the transportation field, Wang and Leong found ABC the most efficient optimization algorithm for finding the optimal routes in transit systems ( 18 ). Panda and Swamy proposed ABC for pavement resurfacing optimization to find the best resurfacing frequency intensity efficiently ( 19 ).…”