The ant colony optimization meta-heuristic
New ideas in optimization
MACS-VRPTW: a multiple ant colony system for vehicle routing problems with time windows
New ideas in optimization
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
A Population Based Approach for ACO
Proceedings of the Applications of Evolutionary Computing on EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN
Cooperative Ant Colonies for Optimizing Resource Allocation in Transportation
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
Applying Population Based ACO to Dynamic Optimization Problems
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Local Search Heuristics for the Single Machine Total Weighted Tardiness Scheduling Problem
INFORMS Journal on Computing
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Population-based ant colony optimisation for multi-objective function optimisation
ACAL'07 Proceedings of the 3rd Australian conference on Progress in artificial life
Sensor placement in water networks using a population-based ant colony optimization algorithm
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part III
Population-ACO for the automotive deployment problem
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Instruction selection for ARM/Thumb processors based on a multi-objective ant algorithm
CSR'06 Proceedings of the First international computer science conference on Theory and Applications
Solving a bi-objective flowshop scheduling problem by pareto-ant colony optimization
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
Refined ranking relations for multi objective optimization andapplication to P-ACO
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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In this paper a Population-based Ant Colony Optimization approach is proposed to solve multi-criteria optimization problems where the population of solutions is chosen from the set of all nondominated solutions found so far. We investigate different maximum sizes for this population. The algorithm employs one pheromone matrix for each type of optimization criterion. The matrices are derived from the chosen population of solutions, and can cope with an arbitrary number of criteria. As a test problem, Single Machine Total Tardiness with changeover costs is used.