Time dependent traveling salesman problem(TDTSP)is an extension of the traveling salesman problem(TSP),in which the travel time or cost between two nodes depends on not only the distance between the nodes,but also the time of day or the node position in the Hamilton cycle.

Improved particle swarm optimization based on k-center and its application in traveling salesman problem;

Modified particle swarm optimization algorithm for traveling salesman problem;

Hybrid approach based on Ant Colony System for solving traveling salesman problem;

Predatory search algorithm based on tabu list and its experimental research on travelling salesman problems;

The problem of path planning for coordinated mine clearance operation of multiple autonomous underwater vehicles was formulated as a kind of multiple travelling salesman problem and two models of multiple travelling salesman problem were developed from the mission background information.

This paper introduces six different random sampling methods for cycling permutations and carries out a further study of solving the travelling salesman problem by simulated annealing.

Application Research for TSP Based on Neural Network-Ant Colony Algorithm;

Improved Genetic Algorithms for TSP;

An improved Simulated Annealing Algorithm to TSP;

Aimed at many fields combinational optimization problems being transformed into travelling salesman problem(TSP),a complex particle swarm optimization(CPSO) algorithm was put forward to solve TSP.

The operation of the genetic algorithm of Travelling Salesman Problem(TSP) needs lots of time and it is easy to fall into the local optimal solution.

When the method of antibody distilling and injecting used in Travelling Salesman Problem(TSP) were presented,the convergence of AIGA was proven theoretically.

On this basis,a new network was proposed to solve traveling salesman problem(TSP) by decaying self-feedback.

To increase the convergence speed of the genetic algorithm in solving the traveling salesman problem(TSP),combined with adaptive operators and competitive strategy between parents and their children,an adaptive genetic algorithm based on the regional search is proposed.

This paper proposed a full set of evolutionary strategies that include new method of encoding of chromosomes,invert,rotate and swap operations,for solving traveling salesman problem(TSP)by genetic algorithm(GA)The strategies are different from those commonly used in GA except the invert operation.