The ant colony optimization meta-heuristic
New ideas in optimization
A Graph-based Ant system and its convergence
Future Generation Computer Systems
Inferential Performance Assessment of Stochastic Optimisers and the Attainment Function
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Ant Colony Optimization
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
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A short convergence proof for a class of ant colony optimizationalgorithms
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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We present a risk-group oriented chronic disease progression model embedded within a metaheuristic-based optimization of the policy variables. Policy-makers are provided with Pareto-optimal screening schedules for risk groups by considering cost and effectiveness outcomes as well as budget constraints. The quality of the screening technology depends on risk group, disease stage, and time. As the metaheuristic solution technique, we use the Pareto ant colony optimization (P-ACO) algorithm for multiobjective combinatorial optimization problems, which is based on the ant colony optimization paradigm. Our approach is illustrated by a numerical example for breast cancer. For a 10-year time horizon, we provide cost-effective screening schedules for selected annual and total budgets. We then discuss policy implications of 16 mammography screening scenarios varying the screening schedule (annual, biennial, triennial, quadrennial) and the rate of women tested (25%, 50%, 75%, 100%). Due to the model's flexible structure, interventions for multiple chronic diseases can be considered simultaneously.