Multiple Offspring Sampling in Large Scale Global Optimization

LaTorre, Antonio; Muelas, Santiago; Peña, José-María

doi:10.1109/cec.2012.6256611

Cited by 56 publications

(31 citation statements)

References 10 publications

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“…This characteristic makes it, in our opinion, a perfect candidate to be the optimization technique of choice for the SoftFEM method, as it leverages the effort of choosing a particular optimization method from the users. In many cases, 23,62,63,[67][68][69][70][71][72] the framework is able to exploit the existing synergies among the different methods under consideration and obtain better results than any of the individual algorithms. In other cases, 63,73,74 those synergies do not exist, but still the framework is able to identify the best performing method and adjust the participation ratios accordingly, with a minimum overhead, thus simplifying the overall process.…”

Section: Resultsmentioning

confidence: 99%

SoftFEM: The Soft Finite Element Method

Peña

LaTorre

Jérusalem

2019

Numerical Meth Engineering

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Summary While the finite element method (FEM) has now reached full maturity both in academy and industry, its use in optimization pipelines remains either computationally intensive or cumbersome. In particular, currently used optimization schemes leveraging FEM still require the choice of dedicated optimization algorithms for a specific design problem, and a “black box” approach to FEM‐based optimization remains elusive. To this end, we propose here an integrated finite element‐soft computing method, ie, the soft FEM (SoftFEM), which integrates a finite element solver within a metaheuristic search wrapper. To illustrate this general method, we focus here on solid mechanics problems. For these problems, SoftFEM is able to optimize geometry changes and mechanistic measures based on geometry constraints and material properties inputs. From the optimization perspective, the use of a fitness function based on finite element calculation imposes a series of challenges. To bypass the limitations in search capabilities of the usual optimization techniques (local search and gradient‐based methods), we propose, instead a hybrid self adaptive search technique, the multiple offspring sampling (MOS), combining two metaheuristics methods: one population‐based differential evolution method and a local search optimizer. The formulation coupling FEM to the optimization wrapper is presented in detail and its flexibility is illustrated with three representative solid mechanics problems. More particularly, we propose here the MOS as the most versatile search algorithm for SoftFEM. A new method for the identification of nonfully determined parameters is also proposed.

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Section: Resultsmentioning

confidence: 99%

SoftFEM: The Soft Finite Element Method

Peña

LaTorre

Jérusalem

2019

Numerical Meth Engineering

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“…Actually, as documented in the summary reports of the competitions on large-scale global optimization at CEC'2010, CEC'2012, CEC'2013 and CEC'2015, the winner has been a non-CC metaheuristic algorithm. For this reason, we also compare MMO-CC with six metaheuristic non-CC algorithms: 2S-Ensemble [25], GaDE [61], jDElscop [62], MA-SW-Chains [31], MOS-CEC2012 [63] and MOS-CEC2013 [22]. The descriptions of these compared algorithms are summarized in Table S-IV.…”

Section: B Comparison With State-of-the-art Metaheuristic Non-cc Algmentioning

confidence: 99%

Multimodal Optimization Enhanced Cooperative Coevolution for Large-Scale Optimization

Peng

Jin

Wang

2019

IEEE Trans. Cybern.

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Cooperative coevolutionary (CC) algorithms decompose a problem into several subcomponents and optimize them separately. Such a divide-and-conquer strategy makes CC algorithms potentially well suited for large-scale optimization. However, decomposition may be inaccurate, resulting in a wrong division of the interacting decision variables into different subcomponents and thereby a loss of important information about the topology of the overall fitness landscape. In this paper, we suggest an idea that concurrently searches for multiple optima and uses them as informative representatives to be exchanged among subcomponents for compensation. To this end, we incorporate a multimodal optimization procedure into each subcomponent, which is adaptively triggered by the status of subcomponent optimizers. In addition, a nondominance-based selection scheme is proposed to adaptively select one complete solution for evaluation from the ones that are constructed by combining informative representatives from each subcomponent with a given solution. The performance of the proposed algorithm has been demonstrated by comparing five popular CC algorithms on a set of selected problems that are recognized to be hard for traditional CC algorithms. The superior performance of the proposed algorithm is further confirmed by a comprehensive study that compares 17 state-of-the-art CC algorithms and other metaheuristic algorithms on 20 1000-dimensional benchmark functions.

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“…CEC2010 benchmark was also used in CEC2012 competition. Improved multiple offspring sampling (MOS) [17] was the winner of CEC2012. MOS combines Solis and Wets [9] and MTS-LS1 [4] as two local searches.…”

Section: Related Workmentioning

confidence: 99%

LSHADE-SPA memetic framework for solving large-scale optimization problems

Hadi

Mohamed

Jambi

2018

Complex Intell. Syst.

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During the last decade, large-scale global optimization has been one of the active research fields. Optimization algorithms are affected by the curse of dimensionality associated with this kind of complex problems. To solve this problem, a new memetic framework for solving large-scale global optimization problems is proposed in this paper. In the proposed framework, success history-based differential evolution with linear population size reduction and semi-parameter adaptation (LSHADE-SPA) is used for global exploration, while a modified version of multiple trajectory search is used for local exploitation. The framework introduced in this paper is further enhanced by the concept of divide and conquer, where the dimensions are randomly divided into groups, and each group is solved separately. The proposed framework is evaluated using IEEE CEC2010 and the IEEE CEC2013 benchmarks designed for large-scale global optimization. The comparison results between our framework and other state-of-the-art algorithms indicate that our proposed framework is competitive in solving large-scale global optimization problems.

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Multiple Offspring Sampling in Large Scale Global Optimization

Cited by 56 publications

References 10 publications

SoftFEM: The Soft Finite Element Method

SoftFEM: The Soft Finite Element Method

Multimodal Optimization Enhanced Cooperative Coevolution for Large-Scale Optimization

LSHADE-SPA memetic framework for solving large-scale optimization problems

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