A Fast Adaptive Memetic Algorithm for Online and Offline Control Design of PMSM Drives

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

doi:10.1109/tsmcb.2006.883271

Cited by 212 publications

(135 citation statements)

References 21 publications

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“…[16]- [18]. Whenever derivatives cannot be calculated, or due to other reasons such as a high problem dimensionality, the application of a metaheuristic approach is the only viable option.…”

Section: A Metaheuristic Approach: a Tailored Evolutionary Algorithmmentioning

confidence: 99%

Scalable Hierarchical Clustering: Twister Tries with a Posteriori Trie Elimination

Cochez

Neri

2015

2015 IEEE Symposium Series on Computational Intelligence

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Abstract-Exact methods for Agglomerative Hierarchical Clustering (AHC) with average linkage do not scale well when the number of items to be clustered is large. The best known algorithms are characterized by quadratic complexity. This is a generally accepted fact and cannot be improved without using specifics of certain metric spaces. Twister tries is an algorithm that produces a dendrogram (i.e, outcome of a hierarchical clustering) which resembles the one produced by AHC, while only needing linear space and time. However, twister tries are sensitive to rare, but still possible, hash evaluations. These might have a disastrous effect on the final outcome. We propose the use of a metaheuristic algorithm to overcome this sensitivity and show how approximate computations of dendrogram quality can help to evaluate the heuristic within reasonable time. The proposed metaheuristic is based on an evolutionary framework and integrates a surrogate model of the fitness within it to enhance the algorithmic performance in terms of computational time.

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Section: A Metaheuristic Approach: a Tailored Evolutionary Algorithmmentioning

confidence: 99%

Scalable Hierarchical Clustering: Twister Tries with a Posteriori Trie Elimination

Cochez

Neri

2015

2015 IEEE Symposium Series on Computational Intelligence

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“…The term MA is now widely used as a synergy of evolutionary or any population-based approach with separate individual learning or local improvement procedures for problem search. So far, MAs are extensively used for solving many optimization problems, such as scheduling problems [20], [1], travel salesman problems [27], combinatorial optimization problems [13], quadratic assignment problems [24] and other applications [6], [16].…”

Section: Behaviors Of Agentsmentioning

confidence: 99%

Agent Based Evolutionary Dynamic Optimization

Yang

Wang

et al. 2010

Agent-Based Evolutionary Search

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Summary Agent-based Evolutionary Search (AES) has attracted a growing interest from the evolutionary computation community in recent years due to its robust ability in solving large scale problems, ranging from online trading, disaster response, modeling social structures to financial investment planning. In order to solve these problems, a great variety of intelligent techniques have been developed to improve the framework and efficiency of AES. This chapter investigates an agent based evolutionary search algorithm in which the agents are updated and co-evolve to track dynamic optimum by imitating the exhibited feature of living organism. In the proposed algorithm, all agents live in a lattice like environment, where each agent is fixed on a lattice point. In order to increase the predefined energy function, individual agent can compete with its neighbors and also can acquire knowledge through cumulative information. For the purpose of maintaining the diversity of the population, random immigrants and adaptive primal dual mapping schemes are incorporated. Simulation experiments on a set of dynamic benchmark problems show the proposed AES algorithm can yield a better performance on dynamic optimization problems (DOPs) in comparison with several peer algorithms.

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“…Afterwards, cellular memetic algorithm was enhanced by an implementation of an adaptive mechanism. (Krasnogor and Smith [73]), Neri et al [74], Caponio et al [75], and Quang et al [76]). …”

Section: Hybrid and Parallel Search-based Evolutionary Algorithmmentioning

confidence: 99%

A New Genetic Algorithm Methodology for Design Optimization of Truss Structures: Bipopulation-Based Genetic Algorithm with Enhanced Interval Search

Talaslioglu

2009

Modelling and Simulation in Engineering

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A new genetic algorithm (GA) methodology, Bipopulation-Based Genetic Algorithm with Enhanced Interval Search (BGAwEIS), is introduced and used to optimize the design of truss structures with various complexities. The results of BGAwEIS are compared with those obtained by the sequential genetic algorithm (SGA) utilizing a single population, a multipopulation-based genetic algorithm (MPGA) proposed for this study and other existing approaches presented in literature. This study has two goals: outlining BGAwEIS's fundamentals and evaluating the performances of BGAwEIS and MPGA. Consequently, it is demonstrated that MPGA shows a better performance than SGA taking advantage of multiple populations, but BGAwEIS explores promising solution regions more efficiently than MPGA by exploiting the feasible solutions. The performance of BGAwEIS is confirmed by better quality degree of its optimal designations compared to algorithms proposed here and described in literature.

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A Fast Adaptive Memetic Algorithm for Online and Offline Control Design of PMSM Drives

Cited by 212 publications

References 21 publications

Scalable Hierarchical Clustering: Twister Tries with a Posteriori Trie Elimination

Scalable Hierarchical Clustering: Twister Tries with a Posteriori Trie Elimination

Agent Based Evolutionary Dynamic Optimization

A New Genetic Algorithm Methodology for Design Optimization of Truss Structures: Bipopulation-Based Genetic Algorithm with Enhanced Interval Search

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