The Coral Reefs Optimization Algorithm: A Novel Metaheuristic for Efficiently Solving Optimization Problems

Salcedo‐Sanz, Sancho; Ser, Javier Del; Landa-Torres, Itziar; Gil-López, Sergio; Portilla-Figueras, Antonio

doi:10.1155/2014/739768

Cited by 146 publications

(88 citation statements)

References 51 publications

(60 reference statements)

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“…A multi-method ensemble CRO-SL is the algorithm used to optimized the proposed antenna. This algorithm is an advanced version of a basic original version of the CRO [18]. The CRO is an evolutionary-type algorithm in which the search operators are based on the processes occurring in a coral reefs, including reproduction, fight for space, or depredation [19].…”

Section: Antenna Optimization: the Cro-sl Algorithmmentioning

confidence: 99%

Design of a Multi-Band Microstrip Textile Patch Antenna for LTE and 5G Services with the CRO-SL Ensemble

et al. 2020

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Featured Application: A novel textile U-shaped with concentric annular slot antenna prototype for LTE and 5G services has been described. In the ground plane, a meander slot has been introduced to reduce the antenna dimensions. A new multi-method metaheuristic algorithm, the Coral Reefs Optimization with Substrate Layer CRO-SL, has been used to optimize the antenna parameters and improve its performance in the frequency bands of interest.Abstract: A textile multi-band antenna for LTE and 5G communication services, composed by a rectangular microstrip patch, two concentric annular slots and a U-Shaped slot, is considered in this paper. In the ground plane, three sleeved meanders have been introduced to modify the surface current distribution, leading to a bandwidth improvement. The U-Shaped slot, the dual circular slots, and the meanders shape have been optimized by means of the Coral Reefs Optimization with Substrate Layer algorithm (CRO-SL). This population-based meta-heuristic approach is a kind of ensemble algorithm for optimization (multi-method), in which different search operators are considered within the algorithm. We show that the CRO-SL is able to obtain a robust multi-band textile antenna, including LTE and 5G frequency bands. For the optimization process, the CRO-SL is guided by means of a fitness function obtained after the antenna simulation by a specific simulation software for electromagnetic analysis in the high frequency range. Keywords: antenna design; constrained optimization problems; coral reefs optimization algorithm; meta-heuristics Recently, the implementation of different kind of antennas in wearables has been massive [6][7][8][9]. Microstrip patch antennas are frequently used in textile materials because of their many advantages, such as low profile, light weight, and conformity. However, these kind of antennas suffer from important issues in their design process (precise value and model of fabric dielectric constant for simulations, difficulty to glue metallic parts to textile materials, bending and moisture influence in antenna performance, etc.), causing severe limitations in their practical applications. This fact is even more dramatic in the frequency bands of modern communication systems based on LTE and 5G technologies. The number of parameters to be tuned in order to make the antenna feasible for working in LTE and 5G applications is usually very high. In these cases, classical optimization methods are no longer suitable, and the employment of advanced optimization algorithms (mainly meta-heuristic approaches, among others) has been shown to be very useful for antenna design [10][11][12][13][14].In this paper, we propose a new model of microstrip textile patch antenna, a multi-band device that can be tuned for LTE and 5G services, among others. Specifically, the proposed antenna is composed by a rectangular microstrip patch with two concentric annular slots and a U-Shaped slot, with sleeved meanders introduced in the ground plane to modify the surface current distribution, leadi...

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Section: Antenna Optimization: the Cro-sl Algorithmmentioning

confidence: 99%

Design of a Multi-Band Microstrip Textile Patch Antenna for LTE and 5G Services with the CRO-SL Ensemble

et al. 2020

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“…The Coral Reefs Optimization (CRO) Algorithm [49] is an evolutionary algorithm based on the behavior of a coral reef that has been applied to a number of energy applications [50,51]. Let R be the reef represented by an R 1 Â R 2 grid, where each position ði; jÞ of R is able to allocate a coral or a colony of corals, C i;j .…”

Section: The Basic Coral Reefs Optimization Algorithmmentioning

confidence: 99%

A CRO-species optimization scheme for robust global solar radiation statistical downscaling

et al. 2017

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a b s t r a c tThis paper tackles the prediction of the global solar radiation (GSR) at a given point, using as predictive variables the outputs of a numerical weather model (the WRF meso-scale model) obtained at a different grid points. Prediction is obtained in this work using a Multilayer Perceptron (MLP) trained with Extreme Learning Machines (ELMs). Provided that the number of WRF outputs is vast, we propose the use of a Coral Reefs Optimization algorithm with species (CRO-SP) to obtain a reduced number of significant predictive variables, therefore improving the global solar radiation prediction attained without feature selection. The proposed system has been tested on real data from a radiometric station located at Toledo (Spain) and average best results of RMSE of 69.19 W=m 2 have been achieved, resulting in a 21.62% improvement over the average prediction without considering the CRO-SP for the feature selection.

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“…Furthermore, other heuristic solvers with innovative operators will be also investigated (e.g. Coral Reefs Optimization [25]). …”

Section: Conclusion and Future Research Linesmentioning

confidence: 99%

Nature-inspired heuristics for the multiple-vehicle selective pickup and delivery problem under maximum profit and incentive fairness criteria

Ser

Torre-Bastida

Laña

et al. 2017

2017 IEEE Congress on Evolutionary Computation (CEC)

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Abstract-This work focuses on wide-scale freight transportation logistics motivated by the sharp increase of on-line shopping stores and the upsurge of Internet as the most frequently utilized selling channel during the last decade. This huge ecosystem of one-click-away catalogs has ultimately unleashed the need for efficient algorithms aimed at properly scheduling the underlying transportation resources in an efficient fashion, especially over the so-called last mile of the distribution chain. In this context the selective pickup and delivery problem focuses on determining the optimal subset of packets that should be picked from its origin city and delivered to their corresponding destination within a given time frame, often driven by the maximization of the total profit of the courier service company. This manuscript tackles a realistic variant of this problem where the transportation fleet is composed by more than one vehicle, which further complicates the selection of packets due to the subsequent need for coordinating the delivery service from the command center. In particular the addressed problem includes a second optimization metric aimed at reflecting a fair share of the net benefit among the company staff based on their driven distance. To efficiently solve this optimization problem, several nature-inspired metaheuristic solvers are analyzed and statistically compared to each other under different parameters of the problem setup. Finally, results obtained over a realistic scenario over the province of Bizkaia (Spain) using emulated data will be explored so as to shed light on the practical applicability of the analyzed heuristics.

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The Coral Reefs Optimization Algorithm: A Novel Metaheuristic for Efficiently Solving Optimization Problems

Cited by 146 publications

References 51 publications

Design of a Multi-Band Microstrip Textile Patch Antenna for LTE and 5G Services with the CRO-SL Ensemble

Design of a Multi-Band Microstrip Textile Patch Antenna for LTE and 5G Services with the CRO-SL Ensemble

A CRO-species optimization scheme for robust global solar radiation statistical downscaling

Nature-inspired heuristics for the multiple-vehicle selective pickup and delivery problem under maximum profit and incentive fairness criteria

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