Simulated annealing: theory and applications
Simulated annealing: theory and applications
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Modern heuristic techniques for combinatorial problems
Modern heuristic techniques for combinatorial problems
Emergent colonization and graph partitioning
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
A genetic algorithm for flowshop sequencing
Computers and Operations Research - Special issue on genetic algorithms
Ant-based load balancing in telecommunications networks
Adaptive Behavior
Ant algorithms for discrete optimization
Artificial Life
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The Ant System Applied to the Quadratic Assignment Problem
IEEE Transactions on Knowledge and Data Engineering
HAS-SOP: Hybrid Ant System for the Sequential Ordering Problem
HAS-SOP: Hybrid Ant System for the Sequential Ordering Problem
AntNet: distributed stigmergetic control for communications networks
Journal of Artificial Intelligence Research
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
An ant colony optimization for single-machine tardiness scheduling with sequence-dependent setups
Computers and Operations Research
A tabu search algorithm for the flowshop scheduling problem with changing neighborhoods
Computers and Industrial Engineering
Ant colony optimization for multi-objective flow shop scheduling problem
Computers and Industrial Engineering
Ant Colony Optimization for the Single Machine Total Earliness Tardiness Scheduling Problem
IEA/AIE '08 Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence
Computers and Operations Research
Robotics and Computer-Integrated Manufacturing
Computers and Industrial Engineering
A multi-objective ant colony system algorithm for flow shop scheduling problem
Expert Systems with Applications: An International Journal
A New Ant Colony Optimization Algorithm with an Escape Mechanism for Scheduling Problems
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
MAS Equipped with Ant Colony Applied into Dynamic Job Shop Scheduling
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
A meta-heuristic approach to solve a JIT scheduling problem in hybrid flow shop
Engineering Applications of Artificial Intelligence
Multi-route railroad blocking problem by improved model and ant colony algorithm in real world
Computers and Industrial Engineering
Ant colony optimization algorithm for a Bi-criteria 2-stage hybrid flowshop scheduling problem
Journal of Intelligent Manufacturing
An ant colony optimization algorithm for setup coordination in a two-stage production system
Applied Soft Computing
An exponential representation in the API algorithm for hidden markov models training
EA'05 Proceedings of the 7th international conference on Artificial Evolution
A modified ant colony system for solving the travelling salesman problem with time windows
Mathematical and Computer Modelling: An International Journal
A new ant colony algorithm for makespan minimization in permutation flow shops
Computers and Industrial Engineering
Multi-objective optimization with fuzzy measures and its application to flow-shop scheduling
Engineering Applications of Artificial Intelligence
Scheduling flow lines with buffers by ant colony digraph
Expert Systems with Applications: An International Journal
Computers and Operations Research
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Ant colony system (ACS) is a novel meta-heuristic inspired by the foraging behavior of real ant. This paper is the first to apply ACS for the n/m/P/Cmax problem, an NP-hard sequencing problem which is used to find a processing order of n different jobs to be processed on m machines in the same sequence with minimizing the makespan. To verify the developed ACS algorithm, computational experiments are concluded on the well-known benchmark problem set of Taillard. The ACS algorithm is compared with other mata-heuristics such as genetic algorithm, simulated annealing, and neighborhood search from the literature. Computational results demonstrate that ACS is a more effective mata-heuristic for the n/m/P/Cmax problem.