Two local search components that move along the axes for memetic computing frameworks

Neri, Ferrante; Khan, Noel

doi:10.1109/foci.2014.7007808

Cited by 4 publications

(1 citation statement)

References 28 publications

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“…CLPSO followed its original implementation in [40]. An adaptive version of differential evolution called jDE [41] used rand/1 mutation and an exponential crossover, while a memetic form of jDE improved each trial solution using the S3 algorithm proposed in [42]. It must be remarked that in this study we have chosen to use some examples of classical metaheuristic and some relatively modern implementations (CLPSO and jDE) which are these days renown as simple and efficient algorithmic frameworks.…”

Section: B Metaheuristic Algorithmsmentioning

confidence: 98%

Adaptive Differential Evolution Applied to Point Matching 2D GIS Data

Khan

Neri

Ahmadi

2015

2015 IEEE Symposium Series on Computational Intelligence

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The impetus behind data analytics and integration is the need for greater insight and data visibility, but since a growing share of our data is multimedia, there is a parallel need for methods that can align multimedia data. This paper explores georeferencing, which is used to combine spatial datasets and used here to align map images to 2D GIS models. This paper surveys various approaches for building the key components of a georeferencing solution, notes their strengths and weaknesses, and comments on their trajectory to help orient future work. The implementation presented here uses Hough transforms for feature detection, nearest neighbor correspondences with simplistic similarity measures, and a population based optimizer. The comparison among metaheuristics has shown that Differential Evolution (DE) frameworks appear especially suited for this problem. In particular, the controlled randomization of DE parameters appears to display the best performance in terms of execution time and competitive performance in terms of function evaluations even with respect to more complex memetic implementations.

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Section: B Metaheuristic Algorithmsmentioning

confidence: 98%