A coral reefs optimization algorithm with substrate layers and local search for large scale global optimization

Salcedo‐Sanz, Sancho; Camacho-Gómez, Carlos; Molina, Daniel; Herrera, Francisco

doi:10.1109/cec.2016.7744242

Cited by 35 publications

(30 citation statements)

References 18 publications

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“…In this algorithm, each substrate layer may represent different processes (different models, operators, parameters, constraints, repairing functions, etc.). Specifically, in [42] a version of the CRO-SL algorithm was proposed, in which each substrate layer represents a different search procedure, leading to a co-evolution competitive algorithm. This version of the CRO-SL has been successfully tested in different applications and problems such as micro-grid design [43], vibration cancellation in buildings, both with passive models [44], and active models [45], or in the evaluation of novel non-linear search procedures [46].…”

Section: Optimization Method: the Cro-sl Algorithmmentioning

confidence: 99%

Wind power field reconstruction from a reduced set of representative measuring points

et al. 2018

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In this paper we deal with a problem of representative measuring points selection for long-term wind power analysis. It has direct applications such as wind farm prospective location or long-term power generation prediction in wind-based energy facilities. The problem's objective is to select the best set of N measuring points (i.e. N representative points), in such a way that a wind power error reconstruction measure is minimized, considering a monthly average wind power field. In order to solve this problem, we use a novel meta-heuristic algorithm, the Coral Reefs Optimization with Substrate Layer, which is an evolutionary-type method able to combine different search procedures within a single population. The CRO-SL is hybridized with the Analogue Method as wind power reconstruction method, to identify the most representative points for the wind field. The proposed approach has been tested in the reconstruction of monthly average wind power fields in Europe, from reanalysis data (ERA-Interim reanalysis). The method exhibits strong performance as evidenced from the experiments carried out. The solutions obtained show that the more significant measuring points are mainly located over the Atlantic ocean, which is consistent with the wind speed climatology of the Northern hemisphere midlatitudes. We have also analyzed the set of least representative points to reconstruct the wind power field (less informative points for whole reconstruction of

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Section: Optimization Method: the Cro-sl Algorithmmentioning

confidence: 99%

Wind power field reconstruction from a reduced set of representative measuring points

et al. 2018

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“…The performance of CBCC was evaluated using only partially separable functions of the CEC2013. In the same year, Coral Reefs Optimization (CRO) was extended using different substrate layers and local search (CRO-LS) to solve LSGO [37].…”

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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“…This new version of the CRO was named CRO-SL (Coral Reefs Optimization algorithm with Substrate Layer). In [42], a new version of the CRO-SL was presented, where several substrate layers which provide a different search procedure each, were defined in the CRO. Following this version of the algorithm, the CRO-SL general approach for competitive co-evolution, where each substrate layer represents different processes (different models, operators, parameters, constraints, repairing functions, etc.).…”

Section: Asexual Reproduction Corals Reproduce Asexually By Budding Ormentioning

confidence: 99%

“…In this paper, the recently developed co-evolution meta-heuristic, the Coral Reefs Optimization algorithm with Substrate Layer (CRO-SL) [42], is applied to design MIMO-AVC for structures subjected to human induced vibration. This optimization algorithm is particularly interested when vibrations on complex floor structures with several closelyfrequency spaced vibration modes have to be cancelled.…”

Section: Introductionmentioning

confidence: 99%

Active vibration control design using the Coral Reefs Optimization with Substrate Layer algorithm

Camacho-Gómez

Wang

Pereira

et al. 2018

Engineering Structures

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Active vibration control (AVC) via inertial-mass actuators is a viable technique to mitigate human-induced vibrations in civil structures. A multi-input multi-output (MIMO) AVC has been previously proposed in the literature to simultaneously find the sensor/actuator pairs' optimal placements and tune the control gains. However, the method involved local gradient-based methods, which is not affordable when the number of possible locations of actuators is large. In this case, the computation time to obtain a local solution may be huge and unaffordable, which limits the number of test points and/or actuators/sensors considered. This paper proposes an alternative approach based on a recently proposed meta-heuristic, the Coral Reefs Optimization (CRO) algorithm. More concretely, an enhanced version of the CRO is considered, the Coral Reefs Optimization with Substrate Layer (CRO-SL). The CRO-SL is a competitive co-evolution algorithm in which different exploration procedures are jointly evolved within a single population of potential solutions to the problem. The proposed algorithm is thus able to promote competition among different search methods to solve hard optimization problems. In terms of structural design, this work provides an important step to improve the applicability of AVC systems to real complex structures (with a large number of vibration modes and/or with a large number of test points) by achieving global optimum designs with affordable computation time. A finite element model of a real complex floor structure is used to illustrate the contributions of this paper.

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A coral reefs optimization algorithm with substrate layers and local search for large scale global optimization

Cited by 35 publications

References 18 publications

Wind power field reconstruction from a reduced set of representative measuring points

Wind power field reconstruction from a reduced set of representative measuring points

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

Active vibration control design using the Coral Reefs Optimization with Substrate Layer algorithm

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