Traveling salesman problem(TSP)is a classic non-deterministic polynomial-hard optimization prob-lem.Based on the characteristics of self-organizing mapping(SOM)network,this paper proposes an improved SOM network from ...Traveling salesman problem(TSP)is a classic non-deterministic polynomial-hard optimization prob-lem.Based on the characteristics of self-organizing mapping(SOM)network,this paper proposes an improved SOM network from the perspectives of network update strategy,initialization method,and parameter selection.This paper compares the performance of the proposed algorithms with the performance of existing SOM network algorithms on the TSP and compares them with several heuristic algorithms.Simulations show that compared with existing SOM networks,the improved SOM network proposed in this paper improves the convergence rate and algorithm accuracy.Compared with iterated local search and heuristic algorithms,the improved SOM net-work algorithms proposed in this paper have the advantage of fast calculation speed on medium-scale TSP.展开更多
In this paper the population based incremental learning method is extended to a form of multiple traits for one gene to reflect pleiotropic and polygenic characters in natural evolved systems and the entropy of a pro...In this paper the population based incremental learning method is extended to a form of multiple traits for one gene to reflect pleiotropic and polygenic characters in natural evolved systems and the entropy of a probability distribution is used to decide the evolvability of the system. This method is used to solve a typical combinatorial optimization problem ─ the symmetric traveling salesman problem. Some results are better than the best existing algorithm of evolutionary algorithms for the problem.展开更多
基金the National Natural Science Foundation of China (No.61627810)the National Science and Technology Major Program of China (No.2018YFB1305003)the National Defense Science and Technology Outstanding Youth Science Foundation (No.2017-JCJQ-ZQ-031)。
文摘Traveling salesman problem(TSP)is a classic non-deterministic polynomial-hard optimization prob-lem.Based on the characteristics of self-organizing mapping(SOM)network,this paper proposes an improved SOM network from the perspectives of network update strategy,initialization method,and parameter selection.This paper compares the performance of the proposed algorithms with the performance of existing SOM network algorithms on the TSP and compares them with several heuristic algorithms.Simulations show that compared with existing SOM networks,the improved SOM network proposed in this paper improves the convergence rate and algorithm accuracy.Compared with iterated local search and heuristic algorithms,the improved SOM net-work algorithms proposed in this paper have the advantage of fast calculation speed on medium-scale TSP.
文摘In this paper the population based incremental learning method is extended to a form of multiple traits for one gene to reflect pleiotropic and polygenic characters in natural evolved systems and the entropy of a probability distribution is used to decide the evolvability of the system. This method is used to solve a typical combinatorial optimization problem ─ the symmetric traveling salesman problem. Some results are better than the best existing algorithm of evolutionary algorithms for the problem.
