The traveling salesman problem (TSP, Travlling Salesman Problem) is a classic combinatorial optimization problems, and is NP-hard problem, too. Technical engineering problems in practice, such as circuit board drilling, urban planning, the postman messenger problems, network layout problems, can be seen as the traveling salesman problem model, and solved. Finding a fast and high quality
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Combinatorial optimization problem is a NP-hard problem, many problems canbe solved by being transformed into a combinatorial optimization problem inscientific research and project technology. In order to find a rational solution to thisproblem, many excellent algorithms were proposed by many scholars and researchers.However, these algorithms can’t gain the ideal results because of their inherentlimitations and
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Researchers have been highly interested in heuristic algorithms since the 1960s. And these algorithms used to solve complicated optimization problems are inspired from natural evolution. Genetic algorithms, which have strong abilities of optimization and stochastic searching, are widely used in industry and engineering and have great impact on these practical fields.Traveling salesman problem(TSP) which is
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Researchers have been highly interested in heuristic algorithms since the 1960s. And these algorithms used to solve complicated optimization problems are inspired from natural evolution. Genetic algorithms, which have strong abilities of optimization and stochastic searching, are widely used in industry and engineering and have great impact on these practical fields.Traveling salesman problem(TSP) which is
(Read More…)
TSP (Traveling Salesman Problem) is a problem of combination optimization with simple definition but difficult to be solved, which attracts many researchers in various fields including mathematics, physics, biology and artificial intelligence (AI). It has become and will continue to become a standard problem to test a new algorithm of combination optimization. Theoretically speaking, the
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Ant Colony Algorithm is a novel heuristic algorithm to solve complicated optimization problems. It gets inspiration from nature behavior of ants in finding food. Because of its properties of robustness, global optimization, universality and distributed computation, the theoretical research is involved increasingly deep and the application becomes increasingly large. The theory part of ACO focuses
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Ant colony algorithm was proposed first by Italy scholar M.Dorigo. It is another heuristic search algorithm applied in combinational optimized problem followed by simulated annealing algorithm, heredity algorithm, taboo search algorithm, ANN algorithm and so on. The experiment indicates the algorithm has good capability of finding the solution. But ant colony algorithm has some disadvantages
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Ant colony algorithm was proposed first by Italy scholar M.Dorigo. It is another heuristic search algorithm applied in combinational optimized problem followed by simulated annealing algorithm, heredity algorithm, taboo search algorithm, ANN algorithm and so on. The experiment indicates the algorithm has good capability of finding the solution. But ant colony algorithm has some disadvantages
(Read More…)
Genetic Algorithm (GA) is a simulation of natural biological evolution of the search algorithm and Particle Swarm Optimization (PSO) is the embodiment of swarm intelligence which also imitates the searching behavior of fishes and birds for food. Both of them are evolutionary algorithms. There are a lot of similarities between the two theories which are
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Researchers have been highly interested in heuristic algorithms since the 1960s. And these algorithms used to solve complicated optimization problems are inspired from natural evolution. Genetic algorithms, which have strong abilities of optimization and stochastic searching, are widely used in industry and engineering and have great impact on these practical fields.Traveling salesman problem(TSP) which is
(Read More…)
