Carlos Camacho-Gómez

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

This paper presents a new version of the Coral Reefs Optimization (CRO) algorithm to improve its performance in large scale global optimization problems. Specifically, we propose to extend the original CRO with different substrate layers, where several exploration operators are defined. This definition allows establishing a competitive co-evolution process within the CRO, which improves the search for optimal solution in large scale optimization problems. The new CRO with substrate layers (CRO-SL) is used in combination with a local search, and the final memetic algorithm obtained has been tested by using a test suite for large scale continuous optimization, showing a robust behavior.

Optimal Design of a Planar Textile Antenna for Industrial Scientific Medical (ISM) 2.4 GHz Wireless Body Area Networks (WBAN) with the CRO-SL Algorithm

This paper proposes a low-profile textile-modified meander line Inverted-F Antenna (IFA) with variable width and spacing meanders, for Industrial Scientific Medical (ISM) 2.4-GHz Wireless Body Area Networks (WBAN), optimized with a novel metaheuristic algorithm. Specifically, a metaheuristic known as Coral Reefs Optimization with Substrate Layer (CRO-SL) is used to obtain an optimal antenna for sensor systems, which allows covering properly and resiliently the 2.4–2.45-GHz industrial scientific medical bandwidth. Flexible pad foam has been used to make the designed prototype with a 1.1-mm thickness. We have used a version of the algorithm that is able to combine different searching operators within a single population of solutions. This approach is ideal to deal with hard optimization problems, such as the design of the proposed meander line IFA. During the optimization phase with the CRO-SL, the proposed antenna has been simulated using CST Microwave Studio software, linked to the CRO-SL by means of MATLAB implementation and Visual Basic Applications (VBA) code. We fully describe the antenna design process, the adaptation of the CRO-SL approach to this problem and several practical aspects of the optimization and details on the algorithm’s performance. To validate the simulation results, we have constructed and measured two prototypes of the antenna, designed with the proposed algorithm. Several practical aspects such as sensitivity during the antenna manufacturing or the agreement between the simulated and constructed antenna are also detailed in the paper.

A Hybrid Neuro-Evolutionary Algorithm for Wind Power Ramp Events Detection

In this work, a hybrid system for wind power ramps events prediction in wind farms is proposed. The system is based on modelling the prediction problem as a binary classification problem from atmospheric reanalysis data inputs. On the other hand, a hybrid neuro-evolutive algorithm is proposed, which combines Artificial Neuronal Networks such as Extreme Learning Machines, with evolutionary algorithms to optimize the trained models. The phenomenon under study occurs with a very low probability, for this reason the problem is so unbalanced, and it is necessary to resort to techniques focused on obtain good results by means of a reduction of the samples from the majority class, as the SMOTE approach. A feature selection is performed by the evolutionary algorithm in order to choose the best trained model. Finally, this model is evaluated by a test set and its accuracy performance is given. The accuracy obtained in the results is quite good in terms of classification performance.

Optimal placement of distributed generation in micro-grids with binary and integer-encoding evolutionary algorithms

This paper discuses the performance of two different Evolutionary Algorithms (EAs) in a problem of Optimal Placement of Distributed Power Generation (OPDPG) in Micro-Grids (MGs). Specifically, the problem consists of choosing the node/nodes to locate a number of different distributed generators with different technologies (such as micro wind turbines, photovoltaic panels, etc.), in such a way that the electrical power losses along a given time period (T) in the MG are minimized. We consider a situation where the network topology is already defined and where each node can have a load with different profiles allocated. The consumption profiles are real measurements of different types (residential, industrial, etc.) and will be hourly evaluated. The generations profiles are also real measurement data from different generation technologies. We consider two different encodings the EAs: first a binary-encoding approach, where each wind generator is represented by 2 bits and each solar generator by N bits, where N is the number of nodes that form the MG; and second, an integer-encoding approach, where both wind and PV generators are represented by 1 and 4 integer elements, respectively. Experiments are performed by considering three different MG topologies, with different number of nodes, in order to test the behavior of the algorithms with search spaces of increasing size. In these experimental scenarios we show how the binary approach attains better solutions than the integer-encoding approach, tough the computational time of the former is higher.

Feature Selection with a Grouping Genetic Algorithm – Extreme Learning Machine Approach for Wind Power Prediction

This paper proposes a hybrid algorithm for feature selection in a Wind Power prediction problem, based on a Grouping Genetic Algorithm-Extreme Learning Machine (GGA-ELM) approach. The proposed approach follows the classical wrapper method where a global search algorithm looks for the best set of features which minimize the output of a given predictors. In this case a GGA searches for several subsets of features and the ELM provides the fitness of the algorithm. Moreover, we propose to use variables from atmospheric reanalysis data as predictive inputs for the system, which opens the possibility of hybridizing numerical weather models with Machine Learning (ML) techniques for wind power prediction in real systems. The ERA-Interim reanalysis from the European Center for Medium-Range Weather Forecasts has been the one used in this paper. Specifically, after the process of feature selection, we have tested the ELM and Gaussian Processes (GPR) to solve the problem. Experimental evaluation of the prediction system in real data from three wind farms in Spain has been carried out, obtaining excellent prediction results when the ELM is applied after the feature selection but not enough in the case of the GPR algorithm.