Coral Reefs Optimization Research Articles

TensorCRO: A TensorFlow‐based implementation of a multi‐method ensemble for optimization

AbstractThis paper presents a novel implementation of the Coral Reef Optimization with Substrate Layers (CRO‐SL) algorithm. Our approach, which we call TensorCRO, takes advantage of the TensorFlow framework to represent CRO‐SL as a series of tensor operations, allowing it to run on GPU and search for solutions in a faster and more efficient way. We evaluate the performance of the proposed implementation across a wide range of benchmark functions commonly used in optimization research (such as the Rastrigin, Rosenbrock, Ackley, and Griewank functions), and we show that GPU execution leads to considerable speedups when compared to its CPU counterpart. Then, when comparing TensorCRO to other state‐of‐the‐art optimization algorithms (such as the Genetic Algorithm, Simulated Annealing, and Particle Swarm Optimization), the results show that TensorCRO can achieve better convergence rates and solutions than other algorithms within a fixed execution time, given that the fitness functions are also implemented on TensorFlow. Furthermore, we also evaluate the proposed approach in a real‐world problem of optimizing power production in wind farms by selecting the locations of turbines; in every evaluated scenario, TensorCRO outperformed the other meta‐heuristics and achieved solutions close to the best known in the literature. Overall, our implementation of the CRO‐SL algorithm in TensorFlow GPU provides a new, fast, and efficient approach to solving optimization problems, and we believe that the proposed implementation has significant potential to be applied in various domains, such as engineering, finance, and machine learning, where optimization is often used to solve complex problems. Furthermore, we propose that this implementation can be used to optimize models that cannot propagate an error gradient, which is an excellent choice for non‐gradient‐based optimizers.

Advances in Structural Health Monitoring: Bio-Inspired Optimization Techniques and Vision-Based Monitoring System for Damage Detection Using Natural Frequency

This paper introduces the improvements in natural-frequency-based SHM by applying bio-inspired optimization methods and a vision-based monitoring system for effective damage detection. This paper proposes a natural frequency extraction method using a motion magnification-based vision monitoring system with bio-inspired optimization techniques to estimate the damage location and depth in a cantilever beam. The proposed optimization techniques are inspired by natural processes and biological evolution including genetic algorithms, particle swarm optimization, sea lion optimization, and coral reefs optimization. To verify the performance of each bio-inspired optimization method, the eigenvalues of a two-bay truss structure are used for estimating the damaged elements. Then, using the proposed video motion magnification method, the natural frequency for each undamaged and damaged cantilever beam is extracted and compared with the LDV sensor to verify the proposed vision-based monitoring system. The performance of each bio-inspired optimizer in damage detection is compared. As a result, coral reefs optimization shows the lowest average error, around 1%, in damage detection using the natural frequency.

Enhancing intelligent transport systems: A cutting-edge framework for context-aware service management with hybrid deep learning

This study presents a comprehensive framework for optimizing intelligent transport systems (ITS) by integrating advanced communication and information technologies into vehicles, roads, and infrastructure. The primary goal is to enhance transportation efficiency, safety, and environmental sustainability while improving overall mobility for people and goods. Leveraging contextual information, the framework offers personalized, proactive services such as real-time traffic updates, route recommendations, and parking availability. Additionally, it enhances safety and security by providing early hazard warnings and adapting to changing road conditions. Our proposed framework utilizes the enhanced coral reef optimization (ECRO) algorithm to efficiently group vehicles for energy-saving data collection, maximizing information gathering efficiency. Collected data is then transmitted to a central data gathering center via a sink node optimized through the modified pelican optimization (MPO) algorithm, considering various vehicle node design constraints. An incident detection module accurately classifies and detects road incidents, enabling timely emergency service requests and alternate route recommendations. To facilitate incident detection, we introduce the deep Rigdelet neural network (DRNN), a novel deep learning technique tailored for decision-making in incident classification. We validate our framework's performance through NS-2 simulations using the SUMO traffic generator, demonstrating its effectiveness in meeting quality of service (QoS) metrics. Through comparative analysis with existing frameworks, our proposed approach stands out for its superior performance and ability to optimize ITS operations.

Predicting the friction angle of clays using a multi-layer perceptron neural network enhanced by yeo-johnson transformation and coral reefs optimization

The accurate prediction of the friction angle of clays is crucial for assessing slope stability in engineering applications. This study addresses the importance of estimating the friction angle and presents the development of four soft computing models: YJ-FPA-MLPnet, YJ-CRO-MLPnet, YJ-ACOC-MLPnet, and YJ-CSA-MLPnet. First of all, the Yeo-Johnson (YJ) transformation technique was used to stabilize the variance of data and make it more suitable for parametric statistical models that assume normality and equal variances. This technique is expected to improve the accuracy of friction angle prediction models. The friction angle prediction models then utilized multi-layer perceptron neural networks (MLPnet) and metaheuristic optimization algorithms to further enhance performance, including flower pollination algorithm (FPA), coral reefs optimization (CRO), ant colony optimization continuous (ACOC), and cuckoo search algorithm (CSA). The prediction models without the YJ technique, i.e. FPA-MLPnet, CRO-MLPnet, ACOC-MLPnet, and CSA-MLPnet, were then compared to those with the YJ technique, i.e. YJ-FPA-MLPnet, YJ-CRO-MLPnet, YJ-ACOC-MLPnet, and YJ-CSA-MLPnet. Among these, the YJ-CRO-MLPnet model demonstrated superior reliability, achieving an accuracy of up to 83% in predicting the friction angle of clay in practical engineering scenarios. This improvement is significant, as it represents an increase from 1.3% to approximately 20% compared to the models that did not utilize the YJ transformation technique.

CROA-based feature selection with BERT model for detecting the offensive speech in Twitter data

<p>Online hate speech has flourished on social networking sites due to the widespread availability of mobile computers and other Web knowledge. Extensive research has shown that online exposure to hate speech has real-world effects on marginalized communities. Research into methods of automatically identifying hate speech has garnered significant attention. Hate speech can affect any demographic, while some populations are more vulnerable than others. Relying solely on progressive learning is insufficient for achieving the goal of automatic hate speech identification. It need access to large amounts of labelled data to train a model. Inaccurate statistics on hate speech and preconceived notions have been the biggest obstacles in the field of hate speech research for a long time. This research provides a novel strategy for meeting these needs by combining a transfer-learning attitude-based BERT (Bidirectional Encoder Representations from Transformers) with a coral reef optimization-based approach (CROA). A feature selection (FC) optimization strategy for coral reefs, a coral reefs optimization method mimics coral behaviours for reef location and development. We might think of each potential answer to the problem as a coral trying to establish itself in the reefs. The results are refined at each stage by applying specialized operators from the coral reefs optimization algorithm. When everything is said and done, the optimal solution is chosen. We also use a cutting-edge fine-tuning method based on transfer learning to assess BERT’s ability to recognize hostile contexts in social media communications. The paper evaluates the proposed approach using Twitter datasets tagged for racist, sexist, homophobic, or otherwise offensive content. The numbers show that our strategy achieves 5%–10% higher precision and recall compared to other approaches.</p>

Efficient evolution of decision trees via fully matrix-based fitness evaluation

Decision Trees (DTs) are a class of supervised learning models that are widely used for both classification and regression applications. They are well-known for their interpretability and robustness, which have led them to remain popular even 60 years after they were first proposed. However, because traditional tree algorithms use greedy methods that are prone to suboptimality, several works have explored the usage of evolutionary algorithms instead. Although these algorithms are often reported to outperform the traditional greedy approach, their computational cost is much higher, since the evolutionary component requires a large number (millions or billions) of function evaluations in order to produce a single tree. Aiming to reduce this computational cost, in this work we propose an encoding that allows the training and evaluation of DTs using only matrix operations. The proposed procedure is shown to be much faster than the traditional tree implementation for complete trees with depths ranging from 2 to 6, and for datasets ranging in size from 100 to 100,000 observations. In particular, the results show speedups of nearly up to 20 times, especially when the dataset is large and the desired tree is small enough to be interpretable. The proposed procedure also benefits from GPU parallelization, although it is still highly performing without it. Furthermore, we propose an evolutionary algorithm, called Coral Reef Optimization for Decision Trees (CRO-DT), that integrates this encoding with a pre-existing ensemble algorithm to evolve better univariate trees. The results obtained show that the proposed CRO-DT is competitive with traditional and modern tree algorithms, consistently producing models of good quality across 14 tested UCI Datasets. We conclude that for most relevant situations, the proposed matrix encoding provides significant speedups over the traditional implementation, and also may serve as a basis for high quality evolutionary DT algorithms.

A Literature Review and Critical Analysis of Metaheuristics Recently Developed

Metaheuristic algorithms have applicability in various fields where it is necessary to solve optimization problems. It has been a common practice in this field for several years to propose new algorithms that take inspiration from various natural and physical processes. The exponential increase of new algorithms is a controversial issue that several researchers have criticized. However, their efforts to point out multiple issues involved in these practices have been insufficient since the number of existing metaheuristics continues to increase yearly. To know the current state of this problem, this paper analyzes a sample of 111 recent studies where so-called new, hybrid, or improved optimization algorithms are proposed. Throughout the document, the topics reviewed will be addressed from a general perspective to their specific aspects. Among the study’s findings, it is observed that only 43% of the analyzed papers make some mention of the No Free Lunch (NFL) theorem, being this significant result ignored by most of the studies where new algorithms are presented. Of the analyzed studies, 65% present an improved version of some established algorithm, which reveals that the trend is no longer to propose metaheuristics based on new analogies. Additionally, a compilation of solutions found in engineering problems commonly used to verify the performance of state-of-the-art algorithms is presented. To demonstrate that algorithms with a low level of innovation can be erroneously considered as new frameworks for years, the metaheuristics known as Black Widow Optimization and Coral Reef Optimization are analyzed. The study of its components reveals that they do not have any innovation. Instead, they are just deficient mixtures of different evolutionary operators. This result applies by extension to their recently proposed improved versions.

An improvised coral reef optimization-based image registration using modified mutual information method

Computer visualisation and medical applications require image registration. It includes transforming collective picture data into the familiar coordinate scheme. Metaheuristic-based methods were developed to explain the issue and improve the efficiency and accuracy of conventional image registration techniques due to their limitations. We describe a hybrid medical image registration technique using bio-inspired meta-heuristic algorithms: Betteroffspring and multi-crossover strategies increase convergent time and solution quality with an improvised coral reef optimization with modified mutual information (ICOR-MMI) algorithm. This new optimization method (ICORMMI) proposes that coral reefs expand, compete for space, and reproduce. A linear weighted sum of image intensity and contour flow model intensity is added to the mutual information calculation. Including statistical and spatial image data improves image registration. The established technique has been tested and verified using multiple medical image data sets, some of which contain single-modality and mixed-modality images (CT, MRI). The registration validates the proposed model's accuracy and efficiency and shows posture's contribution by incorporating statistical and geographical image data. This strategy is adapted to the real-coding problem and tested for realtime issues. The ICOR-MMI algorithm-based hybrid approach outperforms current results in time efficiency and toughness.

New Probabilistic, Dynamic Multi-Method Ensembles for Optimization Based on the CRO-SL

In this paper, new probabilistic and dynamic (adaptive) strategies for creating multi-method ensembles based on the coral reef optimization with substrate layers (CRO-SL) algorithm are proposed. CRO-SL is an evolutionary-based ensemble approach that is able to combine different search procedures for a single population. In this work, two different probabilistic strategies to improve the algorithm are analyzed. First, the probabilistic CRO-SL (PCRO-SL) is presented, which substitutes the substrates in the CRO-SL population with tags associated with each individual. Each tag represents a different operator which will modify the individual in the reproduction phase. In each generation of the algorithm, the tags are randomly assigned to the individuals with similar probabilities, obtaining this way an ensemble that sees more intense changes with the application of different operators to a given individual than CRO-SL. Second, the dynamic probabilistic CRO-SL (DPCRO-SL) is presented, in which the probability of tag assignment is modified during the evolution of the algorithm, depending on the quality of the solutions generated in each substrate. Thus, the best substrates in the search process will be assigned higher probabilities than those which showed worse performance during the search. The performances of the proposed probabilistic and dynamic ensembles were tested for different optimization problems, including benchmark functions and a real application of wind-turbine-layout optimization, comparing the results obtained with those of existing algorithms in the literature.

Cross-entropy boosted CRO-SL for optimal power flow in smart grids

Optimal power flow (OPF) is a complex, highly nonlinear, NP-hard optimization problem, in which the goal is to determine the optimal operational parameters of a power-related system (in many cases a type of smart or micro grid) which guarantee an economic and effective power dispatch. In recent years, a number of approaches based on metaheuristics algorithms have been proposed to solve OPF problems. In this paper, we propose the use of the Cross-Entropy (CE) method as a first step depth search operator to assist population-based evolutionary methods in the framework of an OPF problem. Specifically, a new variant of the Coral Reefs Optimization with Substrate Layers algorithm boosted with CE method (CE+CRO-SL) is presented in this work. We have adopted the IEEE 57-Bus System as a test scenario which, by default, has seven thermal generators for power production for the grid. We have modified this system by replacing three thermal generators with renewable source generators, in order to consider a smart grid approach with renewable energy production. The performance of CE+CRO-SL in this particular case study scenario has been compared with that of well-known techniques such as population’s methods CMA-ES and EPSO (both boosted with CE). The results obtained indicate that CE+CRO-SL showed a superior performance than the alternative techniques in terms of efficiency and accuracy. This is justified by its greater exploration capacity, since it has internally operations coming from different heuristics, thus surpassing the performance of classic methods. Moreover, in a projection analysis, the CE+CRO-SL provides a profit of millions of dollars per month in all cases tested considering the modified version of the IEEE 57-Bus smart grid system.

Modified Coral Reef Optimization Methods for Job Shop Scheduling Problems

The job shop scheduling problem (JSSP) is a fundamental operational research topic with numerous applications in the real world. Since the JSSP is an NP-hard (nondeterministic polynomial time) problem, approximation approaches are frequently used to rectify it. This study proposes a novel biologically-inspired metaheuristic method named Coral Reef Optimization in conjunction with two local search techniques, Simulated Annealing (SA) and Variable Neighborhood Search (VNS), with significant performance and finding-solutions speed enhancement. The two-hybrid algorithms’ performance is evaluated by solving JSSP of various sizes. The findings demonstrate that local search strategies significantly enhance the search efficiency of the two hybrid algorithms compared to the original algorithm. Furthermore, the comparison results with two other metaheuristic algorithms that also use the local search feature and five state-of-the-art algorithms found in the literature reveal the superior search capability of the two proposed hybrid algorithms.

Hybridizing machine learning with metaheuristics for preventing convergence failures in mechanical models based on compression field theories

Some materials, such as reinforced concrete (RC), introduce non-linear constitutive relationships into structural models. The characterization of their structural load–strain response is usually approached by using iterative strategies, such as Newton-type methods. However, these iterative strategies may not predict the non-linear structural response of RC elements (beams) due to convergence problems (non-consistent elements). In this work we propose a novel solution to overcome the convergence failures of RC structural elements subjected to shear force modeled by Compression Field Theories (CFTs). First, a Coral Reef Optimization with Substrate Layers (CRO-SL) metaheuristic is used to solve a fitting problem between numerical and experimental results on a set of real beams tested in shear, for which the system is solvable. Then, the outputs of the CRO-SL, plus the mechanical and geometric parameters of the RC beams, are used to train different Machine Learning (ML) regression methods. These regressors are then able to predict the tensile strains of the non-consistent RC elements. The characterization of such strains allows modifying the parameterization of the system of equations, overcoming thus the convergence problem of Newton-type methods in these cases. Experiments over a set of 72 real RC beams have shown the good performance of our approach. In them we have carried out a comparison among different ML regressors including Multi-Layer perceptrons, Support Vector Regression and Random Forest, plus feature reduction techniques such as PCA.

Multiobjective Edge Server Placement in Mobile-Edge Computing Using a Combination of Multiagent Deep Q-Network and Coral Reefs Optimization

The growth of telecommunication technologies, especially 5G, the growing popularity of smart mobile devices, the emergence of smart cities and Internet of Things (IoT), and the easy use of these equipments have led the cloud users to utilize their various services. Real-time applications and the use of big data have caused cloud service providers (CSPs) to move their servers to the edge of the network and in the vicinity of users to maintain the quality of their services. For this purpose, the concept of mobile-edge computing (MEC) was formed. Applications often have heavy computing complexity on mobile devices or require a lot of data to process. Moreover, in order to save energy consumption of the batteries of this equipment, offloading them on the network resources can transfer the computational complexity from the users’ equipment to the network resources. The resource placement (RP) is one of the major challenges in this area. Improper resource topology upsets their load balancing and increases access latency. In the proposed method of this article, the cellular mobile network is divided into smaller areas and using the coral reefs optimization (CRO) algorithm, the optimal placement of resources in each of these areas will be locally performed. The deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -network (DQN) and Markov game (MG) are used to optimize global RP to reduce global latency and to improve resource load balancing as its two objectives. The results of the experiments show that the proposed method has significantly improved its objectives and server’s energy efficiency, compared to some similar works in this area.

Evolutionary Optimization for Prioritized Materialized View Selection

Selecting appropriate views that provide faster query response time is a critical decision in data warehouse design. Top-level users expect quick results from a data warehouse for faster decision-making to gain a competitive edge in business. Prioritizing a view can distinguish views required to answer top-level users' queries from regular users and provide a better selection chance. The prioritized materialized view selection (PMVS) problem addresses how to utilize the given space to materialize prioritized views more relevant to users. Particle swarm optimization algorithm has been used to achieve minimized query processing costs. Evolutionary algorithms are widely known to solve complex optimization problems quickly by reaching a semi-optimal solution. This paper explores the performance of six evolutionary algorithms: particle swarm optimization, coral reef optimization, cuckoo search, ant colony optimization, grey wolf optimization, and artificial bee colony. The results of empirical and statistical analysis show that PSO, CRO, and GWO algorithms are best suited to solve PMVS.

A centroid opposition-based coral reefs algorithm for solving an automated guided vehicle routing problem with a recharging constraint

Automated guided vehicles (AGVs) have been utilized extensively in material handling systems. In this paper, a synchronization problem of task scheduling and AGV routing is studied on a shop floor with reconfigurable machine tools. The objective is to minimize the maximum completion time (i.e., makespan) of all products. Studying scheduling and routing problems simultaneously, we propose a centroid opposition-based coral reefs optimization (COCRO) algorithm combined with a heuristic. The developed heuristic uses the Dijkstra algorithm to extract collision-free paths. To test the performance of the proposed algorithm, its results are compared to the results of the algorithm used in the literature recently. The results indicate the superiority of the COCRO algorithm. Finally, the performance of the system under different circumstances is analyzed. Based on the results, it can be claimed that reconfigurable machines reduce production time by up to 35%. Furthermore, the capacity of AGVs has a more significant role than the number of AGVs in the production time. Also, comparing four widely used types of batteries (i.e., sealed GEL, AGM pure-lead, Lithium, and flooded lead-acid), the result confirms the superiority of Lithium batteries.

Cluster Head Selection and Optimal Multipath detection using Coral Reef Optimization in MANET Environment

Mobile Ad-hoc Network (MANET) data transfer between nodes in a multi-hop way offers a wide variety of applications. The dynamic feature of ad hoc network mobile nodes is primarily influenced by safety issues, which limit data forwarding rate in multipath routing. As a supplementary method to improve safe data delivery in a MANET, this paper propose and analyse the cluster head (CH) selection and optimum multipath scheme. The CHs are chosen based on the possibility values of each node in MANET, which are considered from the residual energy of each node. During the present phase, the total remaining node energy is used to calculate the mean energy of the entire network. The most likely nodes are picked as the CH, which gathers packets from the cluster members through multi-hop communication. The fundamental idea is to partition a top-secret communication into several shares and then forward the shares via numerous routes to the destination. The Coral Reef Optimization method is used in this work to perform optimum multipath routing. The thorough simulation findings validate the feasibility and efficacy of the suggested strategy in comparison to Butterfly optimization algorithm (BA), Whale Optimization algorithm (WOA) and BAT algorithm techniques.

A two-stage container management in the cloud for optimizing the load balancing and migration cost

Container technology is driving the transformation of Container as a Service (CaaS), which calls for an optimal solution of container management. In this paper, we present a two-stage framework of container management in the cloud, which includes container placement and container migration. Specifically, we investigate the container placement strategy of cloud servers for load balancing in a long time scale. In a short time scale, we investigate the container migration strategy among the cloud servers for minimizing the migration cost while maintaining the degree of load balance. Both of the strategies are formulated as constrained optimization problems. We propose Balance Aware Container Placement (BACP) algorithm and Adaptive Threshold Container Migration (ATCM) algorithm to solve the optimization problems in different time scales. Furthermore, workload prediction of the containers is proposed to enhance the solution. We show by experiments that we can achieve the load balance while reducing the migration cost. Moreover, the proposed algorithm outperforms the meta-heuristic algorithms, i.e., PSO (Particle Swarm Optimization) and CRO (Coral Reef Optimization).

A versatile multi-method ensemble for wind farm layout optimization

In this paper, the performance of a multi-method ensemble for optimization in a turbine layout problem for wind farms is discussed. Specifically, the Coral Reefs Optimization algorithm with Substrate Layer (CRO-SL) is evaluated. The CRO-SL is an evolutionary-type meta-heuristic, able to combine different search procedures or methods within a single population, leading to a multi-method ensemble. The CRO-SL has been adapted to the wind turbines layout problem by including a discrete encoding, based on sparse matrices (though it can also work over continuous encodings) and a convex geometric procedure to speed-up the wake effect calculation for each turbine. The performance of the proposed CRO-SL algorithm in the turbine layout problem is evaluated by means of different experiments, considering simulated and real data from a wind farm in Spain. The performance of the CRO-SL is also evaluated in a different wind turbine layout problem from a NREL-IEA case competition. The contribution of each substrate of the CRO-SL algorithm (search procedure) is discussed in terms of the global search ability of the algorithm in the different problems considered. The global performance of the proposed approach has been finally compared to that of existing approaches in the literature, obtaining excellent results in all scenarios considered.

Novel Real Valued Improved Coral-Reef Optimization Algorithm for Optimal Integration of Classified Distributed Generators

In recent times, Distributed Generation (DG) penetration, especially, through Renewable Energy Sources (RES) has been growing immensely due to marginal carbon footprints. Furthermore, they make system more reliable by minimizing the voltage deviation of the distribution network. Nevertheless, to exploit the DGs in the best manner, they need to be sited and rated optimally. Diverse solutions exist for the problem mainly classified as: analytical approaches, classical non-linear optimization algorithms and meta-heuristic methods. With the objective of minimizing power loss, this manuscript proposes a novel hybrid meta-heuristics approach: Particle Swarm Optimization-Coral Reef Optimization (PSO-CRO) for identifying an optimum positioning and rating of Type-1, Type-2 &#x0026; Type-3 DGs (at 0.82 optimal power factor) in IEEE 33, 69 &#x0026; 118 bus Radial Distributed System (RDS). Furthermore, the results from the proposed hypothesis are compared with its prevalent peers, namely, PSO, CRO, Gravitational Search Algorithm (GSA), PSO-GSA and PSO-Grey Wolf Optimization (PSO-GWO) etc. The results of the proposed algorithm are also compared with the results of GAMS/CONOPT commercial solver. The simulation results prove the robustness, higher efficacy and faster convergence of the proposed method when applied to larger distribution systems.

Memetic coral reefs optimization algorithms for optimal geometrical design of submerged arches

This paper deals with the geometrically nonlinear analysis of submerged arches by means of memetic Coral Reefs Optimization algorithms. The classic design of submerged arches is only focused on calculating the bending stress-less shape (funicular shape) of the structure. Nevertheless, recent works show that this funicular shape can be approached by using a parametric family curve, which also allows a multi-variable optimization of the arch’s geometry. Using this novel parametric set of curves, we propose a new Coral Reefs Optimization (CRO) algorithm based on a memetic approach to tackle the geometrically nonlinear design of submerged arches. Specifically, the proposed CRO approaches have been tested with different search procedures as exploration operators, and we also test a multi-method version of the algorithm, the Coral Reefs Optimization with Substrate Layers (CRO-SL), which considers several search procedures within the same evolutionary population. A local search to improve the solutions has been considered in all cases, to obtain powerful memetic operators for this problem. It is also shown how the different memetic versions of the CRO (specially those involving multi-methods and Differential Evolution search procedures), together with the parametric encoding, are able to obtain nearly-optimal geometries for underwater installations. The performance of the proposed algorithm has been compared with state-of-the-art algorithms for optimization: L-SHADE and HCLPSO. Statistical tests have carried out with the aim of comparing the results. It is shown that there is not significant differences between the proposed results by the three algorithms.