NDLI: Erratum to Dominance-Based Multiobjective Simulated Annealing

Content Provider	IEEE Xplore Digital Library
Author	Zhenguo Tu Yong Lu
Copyright Year	1997
Abstract	For original paper see Z. Tu et al., ibid., vol. 8, no.5, p.456-70, (2004). We have recently discovered an error in the programming of the stochastic genetic algorithm (StGA). The main program was written in C++ except for a subroutine which was coded in MATLAB. This particular subroutine was used to generate the NS number of stochastic children for a chromosome. The NS stochastic children were stored in an array S (NS x N), where N is the dimension of a function. The array S was called into the main program in the form of a vector s with entries being taken column-wise from S (i.e., s[(j - 1) x NS + i] = S[i][j]). In the implementation of the local selection, the vector was supposed to be converted back to S in exactly the reverse manner. But unfortunately the statement was mistakenly written as S[i][j] = s[(i - 1) x N + j]. This error causes a distortion in the variable arrangement such that a typical stochastic child tends to have segments of similar values for different dimensions. Incidentally, for most of the 20 test functions, which have also been used by other researchers in a previous publication, the global optimum is at a position where all variables are equal. As a result, the StGA exhibited a false accelerated convergence speed in a surprisingly consistent manner in all the test cases. After the correction of the above programming error, the StGA as presented in the paper exhibits very different performance and in many cases could not achieve as satisfactory results.
Starting Page	781
Ending Page	781
Page Count	1
File Size	40255
File Format	PDF
ISSN	1089778X
Volume Number	12
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Simulated annealing Computational modeling Vectors Robustness Stochastic processes Genetic algorithms
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Computational Theory and Mathematics Software

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