A Memetic-Neural Approach to Discover Resources in P2P Networks

Neri, Ferrante; Kotilainen, Niko; Vapa, M.

doi:10.1007/978-3-540-70807-0_8

Cited by 12 publications

(4 citation statements)

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“…With respect to employing multiple optimizers, Neri et al [28] studied the problem of optimizing the topology of an MLP neural network, and optimized its weights using an EA coupled with a simulated annealing (SA) and a Hook-Jeeves optimizers. Caponio et al [29] coupled an EA with a Hooke-Jeeves and a simplex optimizer for the problem of optimizing the performance of a motor.…”

Section: Related Studies: Algorithms Employing Multiple Metamodels Ormentioning

confidence: 99%

An Optimization Algorithm Employing Multiple Metamodels and Optimizers

Tenne

2013

Int. J. Autom. Comput.

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Modern engineering design optimization often relies on computer simulations to evaluate candidate designs, a setup which results in expensive black-box optimization problems. Such problems introduce unique challenges, which has motivated the application of metamodel-assisted computational intelligence algorithms to solve them. Such algorithms combine a computational intelligence optimizer which employs a population of candidate solutions, with a metamodel which is a computationally cheaper approximation of the expensive computer simulation. However, although a variety of metamodels and optimizers have been proposed, the optimal types to employ are problem dependant. Therefore, a priori prescribing the type of metamodel and optimizer to be used may degrade its effectiveness. Leveraging on this issue, this study proposes a new computational intelligence algorithm which autonomously adapts the type of the metamodel and optimizer during the search by selecting the most suitable types out of a family of candidates at each stage. Performance analysis using a set of test functions demonstrates the effectiveness of the proposed algorithm, and highlights the merit of the proposed adaptation approach.

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Section: Related Studies: Algorithms Employing Multiple Metamodels Ormentioning

confidence: 99%

An Optimization Algorithm Employing Multiple Metamodels and Optimizers

Tenne

2013

Int. J. Autom. Comput.

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“…The choice of these parameters is an optimization problem, see 23 , 24 , 25 , 26 , and 27 . Since this optimization problem is continuous, usually multivariate, and implicitly noisy, see 28 , algorithms based on Particle Swarm Optimization (PSO) and Differential Evolution (DE) frameworks have been proposed in the literature in several occasions. Some examples of PSO application neural network training is given in 29 and 30 .…”

Section: Introductionmentioning

confidence: 99%

Multi-Strategy Coevolving Aging Particle Optimization

Iacca¹,

Caraffini

Neri

2013

Int. J. Neur. Syst.

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We propose Multi-Strategy Coevolving Aging Particles (MS-CAP), a novel population-based algorithm for black-box optimization. In a memetic fashion, MS-CAP combines two components with complementary algorithm logics. In the first stage, each particle is perturbed independently along each dimension with a progressively shrinking (decaying) radius, and attracted towards the current best solution with an increasing force. In the second phase, the particles are mutated and recombined according to a multi-strategy approach in the fashion of the ensemble of mutation strategies in Differential Evolution. The proposed algorithm is tested, at different dimensionalities, on two complete black-box optimization benchmarks proposed at the Congress on Evolutionary Computation 2010 and 2013. To demonstrate the applicability of the approach, we also test MS-CAP to train a Feedforward Neural Network modeling the kinematics of an 8-link robot manipulator. The numerical results show that MS-CAP, for the setting considered in this study, tends to outperform the state-of-the-art optimization algorithms on a large set of problems, thus resulting in a robust and versatile optimizer.

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“…ventured into stochastic refinement to enhancing search diversity in the neighborhood. In combinatorial optimization domain, some of these include the tabu search for finding low autocorrelation binary sequences [53], simulated annealing to discover the optimal resources in peer-to-peer networks [134], etc. While many of these refinement methods have been designed for single-objective optimization, others specifically for multi-objective optimization have also emerged [75,204,92,83].…”

Section: Types Of Search Methodsmentioning

confidence: 99%

“…nation of multiple refinement procedures, [23] monitored how close the average fitness is compared to the population elite. Others, on the other hand, chose to effect the search adaptation according to the sparseness of individuals [214,134] or the super-fit individual [25] in the population. To prevent loss in diversity, additional populations using completely different fitness criteria have also been considered [161].…”

Section: Diversity Managementmentioning

confidence: 99%

A study on memetic computation, with applications to capacitated vehicle routing problems

Chen¹

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Memetic computation, a paradigm that uses the notion of meme(s) as units of information encoded in computational representations for the purpose of problem-solving, represents one of the frontiers in the computational intelligence research, of today's optimization research. This dissertation takes an explorative attitude in the design of memetic computing frameworks and algorithms by leveraging the co-adaptive nature of meme complexes or memeplexes. The current research presented in this dissertation takes multi-memes and memeplexes as focal point of interest in the context of memetic computation and develop meme-centric computing frameworks for more effective problem-solving in the context of stochastic optimization of Capacitated Vehicle Routing Problems (CVRP) and Vehicle Routing Problem with Stochastic Demands (VRPSD). His sincere encouragement during my indecisive moments and inspirational insights into the many aspects of my research direction have faithfully guided me throughout my research work. Without Associate Professor Ong's guidance, the fruitful achievements of this project would not have been possible. I would also like to thank Associate Professor Lim Meng Hiot for his beneficial comments and support. In addition, I would like to thank my friends for their encouragements and the staffs in Intelligent Systems Centre (IntelliSys) and Center for Computational Intelligence for their technical supports. Last but not least, special thanks to my parents for their constant support, understanding and love.

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A Memetic-Neural Approach to Discover Resources in P2P Networks

Cited by 12 publications

References 0 publications

An Optimization Algorithm Employing Multiple Metamodels and Optimizers

An Optimization Algorithm Employing Multiple Metamodels and Optimizers

Multi-Strategy Coevolving Aging Particle Optimization

A study on memetic computation, with applications to capacitated vehicle routing problems

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