MEMPSODE: A global optimization software based on hybridization of population-based algorithms and local searches

Voglis, C.; Parsopoulos, Konstantinos E.; Papageorgiou, D.G.; Lagaris, Isaac E.; Vrahatis, Michael N.

doi:10.1016/j.cpc.2012.01.010

Cited by 33 publications

(10 citation statements)

References 36 publications

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“…The one above is easy to implement and resulted very good performance in [13] and in [14,30,31]. The general procedure of the hybrid algorithm discussed in our study is given in Algorithm 2.…”

Section: Hybrid Frameworkmentioning

confidence: 99%

“…The algorithm under study falls into the category of memetic algorithms and combines a Particle Swarm Optimization (PSO) variant [11] (global component) with Multidimensional Search (MDS) [12] (local component). The efficiency of this hybridization scheme was demonstrated in [13,14]. We decided to incorporate MDS since it is suitable for discontinuous functions and a perfect candidate for parallelization.…”

Section: Introductionmentioning

confidence: 99%

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A parallel hybrid optimization algorithm for fitting interatomic potentials

Voglis

Hadjidoukas

Papageorgiou

et al. 2013

Applied Soft Computing

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Section: Hybrid Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A parallel hybrid optimization algorithm for fitting interatomic potentials

Voglis

Hadjidoukas

Papageorgiou

et al. 2013

Applied Soft Computing

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“…In this section we will present the adaptive modification to the original MEMPSODE algorithm [17,15]. The original algorithm is extended by having multiple local searches that are stochastically selected following an adaptive scheme.…”

Section: Adaptive Mempsode Algorithmmentioning

confidence: 99%

“…The present work extends MEMPSODE optimization software [17] by allowing adaptive selection of local searches. MEMSPODE software combines Unified Particle Swarm Optimization (UPSO) [14] algorithm with local search algorithms provided by the Merlin optimization environment [13].…”

Section: Introductionmentioning

confidence: 99%

Adapt-MEMPSODE

Voglis

2013

Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation

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MEMPSODE global optimization software tool integrates Particle Swarm Optimization, a prominent population-based stochastic algorithm, with well established efficient local search procedures. In the original description of the algorithm [17] a single local search with specific parameters was applied at selected best position vectors. In this work we present an adaptive variant of MEMPSODE where the local search is selected from a predefined pool of different algorithms. The choice of each local search is based on a probabilistic strategy that uses a simple metric to score the efficiency of the local search. This new version of the algorithm, Adapt-MEMPSODE, is benchmarked against BBOB 2013 test bed. The results show great improvement with respect to the static version that was also benchmarked in earlier workshop.

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“…Usually, two of the unknown variables are first optiBrought to you by | University of Calgary Authenticated Download Date | 5/27/15 6:24 AM mized, and then the remaining ones [4,13]. Various inversion algorithms are used, among them some based on neural networks (PSO [16]), while sampling algorithms (for instance the Monte-Carlo method [14]) are used to identify grid points or points in a continuous space which also minimize mismatch between observations and model predictions based on different norms. At a second step, it is investigated whether the selected solution is stable (i.e.…”

Section: Introductionmentioning

confidence: 99%

Adjustment of highly non-linear redundant systems of equations using a numerical, topology-based approach

Saltogianni

Stiros

2012

Journal of Applied Geodesy

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The adjustment of systems of highly non-linear, redundant equations f j , deriving from observations of certain geophysical processes and geodetic data cannot be based on conventional least-squares techniques, and is based on various numerical inversion techniques. Still these techniques lead to solutions trapped in local minima, to correlated estimates and to solution with poor error control. To overcome these problems, we propose an alternative numerical-topological approach inspired by lighthouse beacon navigation, usually used in 2-D, low-accuracy applications. In our approach, an m-dimensional grid G of points around the real solution (an m-dimensional vector) is at first specified. Then, for each equation f j an uncertainty " j is assigned to the corresponding measurement l j , and the sets A j G of the grid points which satisfy the condition jf j l j j < " j are detected. This process is repeated for all equations, and the common section A of the sets of grid points A j is defined. From this set of grid points, which define a space including the real solution, we compute its center of weight, which corresponds to an estimate of the solution, and its variance-covariance matrix. An optimal solution can be obtained through optimization of the uncertainty " j in each observation. The efficiency of the overall process was assessed in comparison with conventional least squares adjustment.

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MEMPSODE: A global optimization software based on hybridization of population-based algorithms and local searches

Cited by 33 publications

References 36 publications

A parallel hybrid optimization algorithm for fitting interatomic potentials

A parallel hybrid optimization algorithm for fitting interatomic potentials

Adapt-MEMPSODE

Adjustment of highly non-linear redundant systems of equations using a numerical, topology-based approach

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