Application of Bat Algorithm for Transport Network Design Problem

Expert Systems with Applications, 2013

The transit network design problem is one of the most significant problems faced by transit operators and city authorities in the world. This transportation planning problem belongs to the class of difficult combinatorial optimization problem, whose optimal solution is difficult to discover. The paper develops a Swarm Intelligence (SI) based model for the transit network design problem. When designing the transit network, we try to maximize the number of satisfied passengers, to minimize the total number of transfers, and to minimize the total travel time of all served passengers. Our approach to the transit network design problem is based on the Bee Colony Optimization (BCO) metaheuristics. The BCO algorithm is a stochastic, random-search technique that belongs to the class of population-based algorithms. This technique uses a similarity among the way in which bees in nature look for food, and the way in which optimization algorithms search for an optimum of a combinatorial optimization problem. The numerical experiments are performed on known benchmark problems. We clearly show that our approach, based on the BCO algorithm, is competitive with other approaches in the literature, and it can generate highquality solutions.

14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings., 2002

Artificial Life (ALife) uses biological knowledge and techniques to help solve different engineering, management, control and computational problems. Natural systems teach us that very simple individual organisms can form systems capable of performing highly complex tasks by dynamically interacting with each other. The main goal of this paper is to show how we can use ALife concepts (inspired by some principles of natural swarm intelligence) when solving complex problems in traffic and transportation. The Bee System that represents the new approach in the field of Swarm Intelligence is described. It is also shown in the paper that ALife approach can be successful to "attack" transportation problems characterized by uncertainty. The Fuzzy Ant System (FAS) described in the paper represents an attempt to handle the uncertainty that sometimes exists in some complex transportation problems. The potential applications of the Bee System and the Fuzzy Ant System in the field of Traffic and Transportation Engineering are discussed.

Computer Science & Engineering: An International Journal, 2012

Searching for an efficient optimization method to increase the degree of speed optimization and thereby increasing the traffic Flow in an unplanned zone is a widely concerning issue. However, there has been a limited research effort on the optimization of the lane usage with speed optimization.

Proceedings of the 2020 Federated Conference on Computer Science and Information Systems, 2020

Public transport plays an important role in our live. It is very important to have a reliable service. Up to 1000 km, trains and buses play the main role in the public transport. The number of the people and which kind of transport they prefer is important information for transport operators. In this paper is proposed algorithm for transport modeling and passenger flow, based on Ant Colony Optimization method. The problem is described as multi-objective optimization problem. There are two optimization purposes: minimal transportation time and minimal price. Some fuzzy element is included. When the price is in a predefined interval it is considered the same. Similar for the starting traveling time. The aim is to show how many passengers will prefer train and how many will prefer buses according their preferences, the price or the time.

In this paper we present an optimization model for bus routing. The School Bus Routing Problem (SBRP) covers the issue of establishing plans to efficiently transport students distributed across a designated area to the relevant schools using defined resources. The model can be applied to a real network, and results are presented. In this work we propose to improve the routing efficiency of bus routing problem using Genetic Algorithm based AI technique. The results will be compared with the existing algorithms in order to improve the overall system efficiency and reduce the delay needed for routing. We plan to test the system in a real time bus environment for result evaluations.

Archives of Computational Methods in Engineering

Bat algorithm (BA) is one of the promising metaheuristic algorithms. It proved its efficiency in dealing with various optimization problems in diverse fields, such as power and energy systems, economic load dispatch problems, engineering design, image processing and medical applications. Thus, this review introduces a comprehensive and exhaustive review of the BA, as well as evaluates its main characteristics by comparing it with other optimization algorithms. The review paper highlights the performance of BA in different applications and the modifications that have been conducted by researchers (i.e., variants of BA). At the end, the conclusions focus on the current work on BA, highlighting its weaknesses, and suggest possible future research directions. The review paper will be helpful for the researchers and practitioners of BA belonging to a wide range of audiences from the domains of optimization, engineering, medical, data mining and clustering. As well, it is wealthy in research on health, environment and public safety. Also, it will aid those who are interested by providing them with potential future research.

HAL (Le Centre pour la Communication Scientifique Directe), 2020

Applied Mechanics and Materials, 2011

The shortest path between the start node and end node plays an important role in city’s road traffic network analysis system. The basic ant colony system algorithm which is a novel simulated evolutionary algorithm is studied to solve the shortest path problem. But the basic ant colony system algorithm is easy to run into the local optimum solution for shortest path. In order to solve the problem, the improved ant colony system algorithm is proposed. The improvement methods for selection strategy, local search, and information quantity modification of basic ant colony system are discussed in detail. The experiments are done in Beijing road network in China. The results of experiments show that comparing with the basic ant colony algorithm, the improved algorithm can easily converge at the global optimum for the shortest path.

Advances in Evolutionary Algorithms, 2008

Vehicle traffic congestion problem in urban areas due to increased number of vehicles has received increased attention from industries and universities researchers. This problem not also affects the human life in economic matters such as time and fuel consumption, but also affects it in health issues by increasing CO2 and greenhouse gases emissions. In this paper, a novel cellular ant-based algorithm combined with intelligent traffic lights based on streets traffic load condition has been proposed. In the proposed method road network will be divided into different cells and each vehicle will guide through the less traffic path to its destination using Ant Colony Optimization (ACO) in each cell. Moreover, a new method for traffic lights optimization is proposed in order to mitigate the traffic congestion at intersections. Two different scenarios have been performed through NS2 in order to evaluate our traffic lights optimization method. Based on obtained results, vehicles average speed, their waiting time and number of stopped vehicles at intersections are improved using our method instead of using usual traffic lights.