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
Reuse of software in distributed embedded automotive systems
Proceedings of the 4th ACM international conference on Embedded software
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Off-line vs. on-line tuning: a study on MAX–MIN ant system for the TSP
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
MO-TRIBES, an adaptive multiobjective particle swarm optimization algorithm
Computational Optimization and Applications
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Modern vehicles possess an increasing number of softwareand hardware components that are integrated in electroniccontrol units (ECUs). Finding an optimal allocation forall components is a multi-objective optimisation problem,since every valid allocation can be rated according to multipleobjectives like costs, busload, weight, etc. Additionally,several constraints mainly regarding the availability of resourceshave to be considered. This paper introduces a newvariant of the well-known ant colony optimisation, whichhas been applied to the real-world problem described above.Since it concerns a multi-objective optimisation problem,multiple ant colonies are employed. In the course of thiswork, pheromone updating strategies specialised on constrainthandling are developed. To reduce the effort neededto adapt the algorithm to the optimisation problem by tuningstrategic parameters, self-adaptive mechanisms are establishedfor most of them. Besides the reduction of theeffort, this step also improves the algorithm's convergencebehaviour.