Acta mathematica scientia, Series B >
AN ADAPTIVE MEMBRANE ALGORITHM FOR SOLVING COMBINATORIAL OPTIMIZATION PROBLEMS
Received date: 2013-10-23
Revised date: 2014-01-06
Online published: 2014-09-20
Supported by
This work was supported by the National Natural Science Foundation of China (60903105, 61373066, 61309015, 61033003 and 61320106005).
Membrane algorithms (MAs), which inherit from P systems, constitute a new parallel and distribute framework for approximate computation. In the paper, a membrane algorithm is proposed with the improvement that the involved parameters can be adaptively chosen. In the algorithm, some membranes can evolve dynamically during the computing
process to specify the values of the requested parameters. The new algorithm is tested on a well-known combinatorial optimization problem, the travelling salesman problem. The em-pirical evidence suggests that the proposed approach is efficient and reliable when dealing with 11 benchmark instances, particularly obtaining the best of the known solutions in eight instances. Compared with the genetic algorithm, simulated annealing algorithm, neural net-work and a fine-tuned non-adaptive membrane algorithm, our algorithm performs better than them. In practice, to design the airline network that minimize the total routing cost on the CAB data with twenty-five US cities, we can quickly obtain high quality solutions using our algorithm.
Key words： membrane algorithm; adaptation; travelling salesman problem
HE Juan-Juan , XIAO Jian-Hua , SHAO Ze-Hui . AN ADAPTIVE MEMBRANE ALGORITHM FOR SOLVING COMBINATORIAL OPTIMIZATION PROBLEMS[J]. Acta mathematica scientia, Series B, 2014 , 34(5) : 1377 -1394 . DOI: 10.1016/S0252-9602(14)60090-4
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