2010 2nd International Conference on Information Engineering and Computer Science 2010
DOI: 10.1109/iciecs.2010.5677899
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Integer Encoding Differential Evolution Algorithm for Integer Programming

Abstract: A novel integer encoding Differential Evolution (IEDE) algorithm was proposed for integer optimization problems in this paper. Based on the standard framework of the traditional DE, the population was encoding with integer. The IEDE inherited the crossover operator and selection operator from the traditional DE directly. And a new integer mutation operator was defined to deal with the integer encoding individual. Several initial simulation results show it is effective and efficient in solving the integer optim… Show more

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Cited by 5 publications
(2 citation statements)
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“…Therefore, some modifications of the original code, given by the authors of the DE [39], had to be made to allow for selection of the optimal network configuration and tap position from the set of integers. The idea of utilizing a DE for finding a solution that is not in a continuous space is not a new one, and different approaches to solving binary [44][45][46], or integer and mixed-integer differential evolution [47,48] can be found. The transition from a continuous to discrete search space, utilized for determining optimal network configuration and tap position, is discussed in Sections 2.2 and 2.3.…”
Section: Methodsmentioning
confidence: 99%
“…Therefore, some modifications of the original code, given by the authors of the DE [39], had to be made to allow for selection of the optimal network configuration and tap position from the set of integers. The idea of utilizing a DE for finding a solution that is not in a continuous space is not a new one, and different approaches to solving binary [44][45][46], or integer and mixed-integer differential evolution [47,48] can be found. The transition from a continuous to discrete search space, utilized for determining optimal network configuration and tap position, is discussed in Sections 2.2 and 2.3.…”
Section: Methodsmentioning
confidence: 99%
“…However, the algorithm also has the common problems of intelligent algorithms, namely, shrinkage stagnation and premature convergence. Many scholars have improved DE algorithm from the following four aspects: control parameter setting (Brest et al , 2006; Qin et al , 2009; Teo, 2016), evolutionary strategy selection (Das, 2009; Epitropakis et al , 2011; Tang et al , 2014), population structure (Depolli et al , 2013; Kushida et al , 2013) and mixing with other optimization algorithms (Deng and Liu, 2010; Zhan et al , 2014). These improved DE algorithms effectively balance the search ability and development ability of DE algorithm, and provide more choices for solving engineering problems.…”
Section: Introductionmentioning
confidence: 99%