Extending Selection Learning toward Fixed-Length d-Ary Strings
Selected Papers from the 5th European Conference on Artificial Evolution
A new hybrid heuristic approach for solving large traveling salesman problem
Information Sciences—Informatics and Computer Science: An International Journal
Information Sciences: an International Journal
Genetic algorithm for asymmetric traveling salesman problem with imprecise travel times
Journal of Computational and Applied Mathematics
A novel ant colony system based on minimum 1-tree and hybrid mutation for TSP
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Solving the traveling salesman problem using cooperative genetic ant systems
Expert Systems with Applications: An International Journal
A cooperative ant colony system and genetic algorithm for TSPs
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
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Simulated annealing can be viewed as a process that generates a sequence of Markov chains, i.e., it keeps no memory about the states visited in the past of the process. This property makes simulated annealing time-consuming in exploring needless states and difficult in controlling the temperature and transition number. In this paper, we propose a new annealing model with memory that records important information about the states visited in the past. After mapping applications onto a physical system containing particles with discrete states, the new annealing method systematically explores the configuration space, learns the energy information of it, and converges to a well-optimized state. Such energy information is encoded in a learning scheme. The scheme generates states distributed in Boltzmann-style probability according to the energy information recorded in it. Moreover, with the assistance of the learning scheme, controlling over the annealing process become simple and deterministic. From qualitative and quantitative analyses in this paper, we can see that this convenient framework provides an efficient technique for combinatorial optimization problems and good confidence in the solution quality