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Optimization of traveling salesman problem using affinity propagation clustering and genetic algorithm

El-Samak A. F., Ashour W.

Journal of Artificial Intelligence and Soft Computing Research

2015

Vol. 5, No. 4

239--245

Combinatorial optimization problems, such as travel salesman problem, are usually NPhard and the solution space of this problem is very large. Therefore the set of feasible solutions cannot be evaluated one by one. The simple genetic algorithm is one of the most used evolutionary computation algorithms, that give a good solution for TSP, however, it takes much computational time. In this paper, Affinity Propagation Clustering Technique (AP) is used to optimize the performance of the Genetic Algorithm (GA) for solving TSP. The core idea, which is clustering cities into smaller clusters and solving each cluster using GA separately, thus the access to the optimal solution will be in less computational time. Numerical experiments show that the proposed algorithm can give a good results for TSP problem more than the simple GA.

Heuristic algorithms for the problem of task scheduling with moving executors

Józefczyk J.

Systems Science

2004

Vol. 30, no 3

95-103

New heuristic algorithms for solving the task scheduling problem with moving executors to minimize the sum of completion times are considered. The corresponding combinatorial optimization problem is formulated for single executor. A hybrid solution algorithm is introduced and investigated, where evolutionary as well as simulated annealing procedures are applied. A simulated annealing algorithm assists the evolutionary algorithm in three different ways. It is used for the generation of the initial set of solutions of the evolutionary algorithm. Moreover, this algorithm attempts to enhance the best solutions at current iterations of the evolutionary procedure. The results of the evaluation of the solution algorithms, which have been performed during the computer simulation experiments, are presented. The influence of the parameters of the solution algorithm as well as the task scheduling problem on the quality of results and on the time of computation is investigated.

Optimization using chaotic neural network and its application to lighting design

Nanba R., Hasegawa M., Nishita T., Aihara K.

Control and Cybernetics

2002

Vol. 31, no 2

249-269

We have developed a chaotic neurodynamical searching method for solving the lighting design problems. The goal of this method is to design interior lighting that satisfies required illuminance distribution. We can obtain accurate illuminance distribution by using the radiosity method to calculate interreflection of lights. We formulate the lighting design problem that considers the interreflection of lights as a combinatorial optimization problem, and construct a chaotic neural network which searches the optimum solution of the lighting design problem. The calculated illuminance distribution is visualized using computer graphics. We compare this optimization method with the conventional neural network with gradient dynamics, simulated annealing, and the genetic algorithm, and clarify the effectiveness of the proposed method based on the chaotic neural network.