Application of Memetic Differential Evolution frameworks to PMSM drive design

Caponio, Andrea; Neri, Ferrante; Cascella, Giuseppe Leonardo; Salvatore, N.

doi:10.1109/cec.2008.4631079

Cited by 9 publications

(3 citation statements)

References 24 publications

(34 reference statements)

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“…Memetic Algorithm (MA) represents a subfield of Memetic Computing (MC) (Ong, Lim, and Chen 2010) that is widely established as the synergy of populationbased approaches with separate lifetime learning (a.k.a., local search or individual learning) process. Recent studies on MAs have shown that they can converge to high-quality solutions more efficiently than their conventional counterparts on a wide range of domains covering problems in combinatorial (Lim and Xu 2005;Tang, Lim, Ong, and Er 2006;Lim, Ong, Lim, Chen, and Agarwal 2008;Hasan, Sarker, Essam, and Cornforth 2009;Meuth, Wunsch, Saad, and Vian 2010), continuous (Aranha and Iba 2009;Chiam, Tan, and Mamun 2009;Kramer 2010;Neri and Mininno 2010), dynamic (Lim, Ong, and Lee 2005;Goh and Tan 2007;Caponio, Neri, Cascella, and Salvatore 2008) and multi-objective optimisations (Ishibuchi, Yoshida, and Murata 2002;Tan, Khor, and Lee 2005;Goh, Ong, and Tan 2009) Algorithm 1 outlines the schematic workflow of a canonical MA. The algorithm starts with the initialisation of a population Pop(gen ¼ 0) of candidate solutions.…”

Section: Safety Stock For Managerial Control Over Route Failure In Vrpsdmentioning

confidence: 98%

A self-adaptive memeplexes robust search scheme for solving stochastic demands vehicle routing problem

Chen

Feng

Ong

2012

International Journal of Systems Science

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In this article, we proposed a self-adaptive memeplex robust search (SAMRS) for finding robust and reliable solutions that are less sensitive to stochastic behaviours of customer demands and have low probability of route failures, respectively, in vehicle routing problem with stochastic demands (VRPSD). In particular, the contribution of this article is three-fold. First, the proposed SAMRS employs the robust solution search scheme (RS 3 ) as an approximation of the computationally intensive Monte Carlo simulation, thus reducing the computation cost of fitness evaluation in VRPSD, while directing the search towards robust and reliable solutions. Furthermore, a self-adaptive individual learning based on the conceptual modelling of memeplex is introduced in the SAMRS. Finally, SAMRS incorporates a gene-meme co-evolution model with genetic and memetic representation to effectively manage the search for solutions in VRPSD. Extensive experimental results are then presented for benchmark problems to demonstrate that the proposed SAMRS serves as an efficable means of generating high-quality robust and reliable solutions in VRPSD.

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Section: Safety Stock For Managerial Control Over Route Failure In Vrpsdmentioning

confidence: 98%

A self-adaptive memeplexes robust search scheme for solving stochastic demands vehicle routing problem

Chen

Feng

Ong

2012

International Journal of Systems Science

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“…Mutation probability and other search parameters depend also on the diversity measure. the FAMA algorithm was compared against Tirronen's algorithm and SFMDE obtaining better results for the problem of permanent magnet synchronous motors [13].…”

Section: Preliminariesmentioning

confidence: 99%

Benchmarking a hybrid DE-RHC algorithm on real world problems

LaTorre

Muelas

Peña

2011

2011 IEEE Congress of Evolutionary Computation (CEC)

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Continuous optimization is one of the most active research lines in evolutionary and metaheuristic algorithms. Through CEC 2005 to CEC 2010 competitions, many different algorithms have been proposed to solve continuous problems. The advances on this type of problems are of capital importance as many real-world problems from very different domains (biology, engineering, data mining, etc.) can be formulated as the optimization of a continuous function. For this reason, we have proposed a hybrid DE-RHC algorithm that combines the search strength of Differential Evolution with the explorative ability of a Random Hill Climber, which can help the Differential Evolution algorithm to reach new promising areas in difficult fitness landscapes, such as those than can be found on real-world problems. To evaluate this approach, the benchmark problems proposed in the "Testing Evolutionary Algorithms on Real-world Numerical Optimization Problems" CEC 2011 special session have been considered.

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“…FAMA includes a self-adaptive criterium based on a fitness diversity measure and the iteration number. Mutation probability and other search parameters depend also on the diversity measure, the FAMA algorithm was compared against Tirronen's algorithm and SFMDE obtaining better results for the problem of permanent magnet synchronous motors (Caponio et al 2008). …”

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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Application of Memetic Differential Evolution frameworks to PMSM drive design

Cited by 9 publications

References 24 publications

A self-adaptive memeplexes robust search scheme for solving stochastic demands vehicle routing problem

A self-adaptive memeplexes robust search scheme for solving stochastic demands vehicle routing problem

Benchmarking a hybrid DE-RHC algorithm on real world problems

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

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