Journal ArticleDOI
Ant Algorithms: Theory and Applications
TLDR
This paper reviews the theory and applications of ant algorithms, new methods of discrete optimization based on the simulation of self-organized colony of biologic ants, which are especially efficient for online optimization of processes in distributed nonstationary systems.Abstract:
This paper reviews the theory and applications of ant algorithms, new methods of discrete optimization based on the simulation of self-organized colony of biologic ants. The colony can be regarded as a multi-agent system where each agent is functioning independently by simple rules. Unlike the nearly primitive behavior of the agents, the behavior of the whole system happens to be amazingly reasonable. The ant algorithms have been extensively studied by European researchers from the mid-1990s. These algorithms have successfully been applied to solving many complex combinatorial optimization problems, such as the traveling salesman problem, the vehicle routing problem, the problem of graph coloring, the quadratic assignment problem, the problem of network-traffic optimization, the job-shop scheduling problem, etc. The ant algorithms are especially efficient for online optimization of processes in distributed nonstationary systems (for example, telecommunication network routing).read more
Citations
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Journal ArticleDOI
Integrated process planning and scheduling by an agent-based ant colony optimization
TL;DR: The experimental results indicate that the ACO algorithm can effectively solve the IPPS problems and the agent-based implementation can provide a distributive computation of the algorithm.
Journal ArticleDOI
Genetic algorithm for the one-commodity pickup-and-delivery traveling salesman problem
TL;DR: The proposed genetic algorithm for the one-commodity pickup-and-delivery traveling salesman problem is designed, and the computational results show that it gives a faster and better convergence than existing heuristics.
Journal ArticleDOI
An interactive simulation and analysis software for solving TSP using Ant Colony Optimization algorithms
Aybars Ugur,Doğan Aydın +1 more
TL;DR: A web-based simulation and analysis software (TSPAntSim) is developed for solving TSP using ACO algorithms with local search heuristics to find the shortest route for salesperson which visits each given city precisely once.
Journal ArticleDOI
Unequal area flexible bay facility layout using ant colony optimisation
TL;DR: In this article, an ant colony optimisation approach is proposed to solve the facility layout problem with unequal area departments and flexible bays, which is one of the commonly used layout representations in industry practice.
Journal ArticleDOI
An adapted ant colony optimization algorithm for the minimization of the travel distance of pickers in manual warehouses
TL;DR: The outcomes obtained suggest that the FW–ACO is a promising algorithm generally able to provide better results than the heuristic and metaheuristic algorithms, and often able to find an exact solution.
References
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Journal ArticleDOI
Ant system: optimization by a colony of cooperating agents
TL;DR: It is shown how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling, and the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.
Journal ArticleDOI
Ant colony system: a cooperative learning approach to the traveling salesman problem
TL;DR: The results show that the ACS outperforms other nature-inspired algorithms such as simulated annealing and evolutionary computation, and it is concluded comparing ACS-3-opt, a version of the ACS augmented with a local search procedure, to some of the best performing algorithms for symmetric and asymmetric TSPs.
Book
Ant Colony Optimization
TL;DR: Ant colony optimization (ACO) is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and of other animals as discussed by the authors In particular, ants have inspired a number of methods and techniques among which the most studied and the most successful is the general purpose optimization technique known as ant colony optimization.
BookDOI
Swarm intelligence: from natural to artificial systems
TL;DR: This chapter discusses Ant Foraging Behavior, Combinatorial Optimization, and Routing in Communications Networks, and its application to Data Analysis and Graph Partitioning.