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This paper presents an agent-based evolutionary search algorithm (AES) for solving dynamic travelling salesman problem (DTSP). The proposed algorithm uses the principal of collaborative endeavor learning mechanism in which all the agents within the current population co-evolve to track dynamic optima. Moreover, a local updating rule which is much the same of permutation enforcement learning scheme...
Recently, the research on quantum-inspired evolutionary algorithms (QEA) has attracted some attention in the area of evolutionary computation. QEA use a probabilistic representation, called Q-bit, to encode individuals in population. Unlike standard evolutionary algorithms, each Q-bit individual is a probability model, which can represent multiple solutions. Since probability models store global statistical...
The traveling salesman problem (TSP) is widely used in many real world problems. It is very important to design efficient algorithms for this problem. The key issue in TSP is that the computation cost will increase rapidly with the increasing of the size of the problem. To overcome the shortcoming, in this paper a novel evolutionary algorithm based on a clustering algorithm is proposed for TSP. The...
In this paper a new idea to solve the traveling salesman problem is introduced. The idea is categorized within meta-heuristic algorithms and is based on a normal wise human-being thinking method. Starting from an arbitrary starting point, three factors are considered to generate a score vector by which the next position is selected. Distance from the non-visited points, successful previous experiments,...
An improved particle swarm optimization (IPSO) algorithm was proposed. In the basic particle swarm optimization (PSO) algorithm, the tentative behavior of individuals and the mutation of velocity have been introduced, according to the law of evolutionary process. Using the single node adjustment algorithm, each particle searches the neighbor area by itself at every generation after general steps....
Traveling salesman problem (TSP) is one of the most difficult problems in the combinatorial optimization area. The goal of TSP is to find one path that can travel between all the nodes (instances) of the graph just once (Hamiltonian tour) in the smallest tour, that is, smallest Euclidian distance. In this context, many other meta-heuristics techniques based on evolutionary algorithms have been propose...
The relevant parameters in the ant colony algorithm have great impact on algorithm performance and various parameters are closely linked, and a good parameter combination will increase the overall search capability and convergence of algorithm. At present, the parameter settings of the ant colony algorithm are determined relying on experience and experiments which have heavy workload and it is difficult...
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