A Memetic Differential Evolution in Filter Design for Defect Detection in Paper Production

Tirronen, Ville; Neri, Ferrante; Kärkkäinen, Tommi; Majava, K.; Rossi, Tuomo

doi:10.1007/978-3-540-71805-5_35

Cited by 32 publications

(25 citation statements)

References 14 publications

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“…The comparison in Fig. 14b shows that the SFMDE has a similar performance in comparison to the DE and MDE proposed in Tirronen et al (2007). Nevertheless, the SFMDE seems more promising, since it converged to solutions having a slightly better performance.…”

Section: Application 1: Design Of a DC Motor Speed Controllermentioning

confidence: 78%

“…(22), the SFMDE has been applied. Performance of the SFMDE has been compared with a plain DE, an ES, SA, the comprehensive learning particle swarm optimizer (CLPSO) proposed in Liang et al (2006), the DE with the self-adaptive control parameter (SACPDE) proposed in Brest et al (2006), and the memetic differential evolution (MDE) proposed in Tirronen et al (2007) for solving the same problem.…”

Section: Application 2: Digital Filter Design For Defect Detection Inmentioning

confidence: 99%

“…In Kaelo and Ali (2007), a hybrid DE with a mutation local searcher based on the attractrepulsion logic, is proposed. Tirronen et al (2007) proposes a hybridization of a DE framework with two local searchers adaptively coordinated by means of a rule based on fitness diversity. Moreover, Tirronen et al (2007) applies the resulting algorithm to the design of a Finite Impulse Response (FIR) filter for defect detections in paper production.…”

Section: Introductionmentioning

confidence: 99%

“…Tirronen et al (2007) proposes a hybridization of a DE framework with two local searchers adaptively coordinated by means of a rule based on fitness diversity. Moreover, Tirronen et al (2007) applies the resulting algorithm to the design of a Finite Impulse Response (FIR) filter for defect detections in paper production. This paper proposes a novel implementation of an adaptive memetic algorithm (MA) (Ong et al 2006), employing the DE as an evolutionary framework.…”

Section: Introductionmentioning

confidence: 99%

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Super-fit control adaptation in memetic differential evolution frameworks

2008

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This paper proposes the super-fit memetic differential evolution (SFMDE). This algorithm employs a differential evolution (DE) framework hybridized with three meta-heuristics, each having different roles and features. Particle Swarm Optimization assists the DE in the beginning of the optimization process by helping to generate a super-fit individual. The two other meta-heuristics are local searchers adaptively coordinated by means of an index measuring quality of the super-fit individual with respect to the rest of the population. The choice of the local searcher and its application is then executed by means of a probabilistic scheme which makes use of the generalized beta distribution. These two local searchers are the Nelder mead algorithm and the Rosenbrock Algorithm. The SFMDE has been tested on two engineering problems; the first application is the optimal control drive design for a direct current (DC) motor, the second is the design of a digital filter for image processing purposes. Numerical results show that the SFMDE is a flexible and promising approach which has a high performance standard in terms of both final solutions detected and convergence speed.A. Caponio

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Section: Application 1: Design Of a DC Motor Speed Controllermentioning

confidence: 78%

Section: Application 2: Digital Filter Design For Defect Detection Inmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Super-fit control adaptation in memetic differential evolution frameworks

2008

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“…CSDE1 applies the local search only to the best individual in the population at each generation. Tirronen et al (2007) designed a hybrid DE algorithm that combined the Hooke-Jeeves Algorithm (HJA) and the stochastic local search (SLS), coordinated by an adaptive rule that estimates fitness diversity using the ratio between the standard deviation and the average fitness of the population. This algorithm was compared against a regular DE and an evolution strategy (ES) on the problem of weighting coefficients to detect defects in paper production.…”

Section: Related Workmentioning

confidence: 99%

A MOS-based dynamic memetic differential evolution algorithm for continuous optimization: a scalability test

2010

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Continuous optimization is one of the areas with more activity in the field of heuristic optimization. Many algorithms have been proposed and compared on several benchmarks of functions, with different performance depending on the problems. For this reason, the combination of different search strategies seems desirable to obtain the best performance of each of these approaches. This contribution explores the use of a hybrid memetic algorithm based on the multiple offspring framework. The proposed algorithm combines the explorative/exploitative strength of two heuristic search methods that separately obtain very competitive results. This algorithm has been tested with the benchmark problems and conditions defined for the special issue of the Soft Computing Journal on Scalability of Evolutionary Algorithms and other Metaheuristics for Large Scale Continuous Optimization Problems. The proposed algorithm obtained the best results compared with both its composing algorithms and a set of reference algorithms that were proposed for the special issue.

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A Modern Introduction to Memetic Algorithms

Moscato

Cotta

2010

International Series in Operations Research &Amp; Management Science

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A Memetic Differential Evolution in Filter Design for Defect Detection in Paper Production

Cited by 32 publications

References 14 publications

Super-fit control adaptation in memetic differential evolution frameworks

Super-fit control adaptation in memetic differential evolution frameworks

A MOS-based dynamic memetic differential evolution algorithm for continuous optimization: a scalability test

A Modern Introduction to Memetic Algorithms

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