Abstract
In order to improve the exploration ability of Quantum Evolutionary Algorithm (QEA) and helping the algorithm to escape from local optima, this paper proposes a novel operator which uses the history of search process during the previous iterations to lead the q-individuals toward better parts of the search space. In the proposed method, in each iteration the history of the solutions is stored in a set called the history set. The history of solutions contains some information about the fitness landscape and the structure of better and worse solutions. This paper proposes a new operator which exploits this information to make a figure about the backbone structure of the fitness landscape and lead the q-individuals to search better parts of the search space. The proposed algorithm is tested on Knapsack Problem, Trap Problem, Max-3-Sat Problem and 13 Numerical Benchmark functions. Experimental results show better performance for the proposed algorithm than the original version of QEA.
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Tayarani N., M.H., Beheshti, M., Sabet, J., Mobasher, M., Joneid, H. (2011). Improvement of the Performance of QEA Using the History of Search Process and Backbone Structure of Landscape. In: Pichappan, P., Ahmadi, H., Ariwa, E. (eds) Innovative Computing Technology. INCT 2011. Communications in Computer and Information Science, vol 241. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27337-7_37
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DOI: https://doi.org/10.1007/978-3-642-27337-7_37
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