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Model-based testing is an automated process in which executable tests are derived from behavioral models of a system. Model checking is a verification technique to reveal errors in which all reachable states of a system can be generated as state space. In the literature, different approaches suggest using model checkers for model-based testing. Model checker explores all possible system states, so utilizing the various paths in the state-space as test cases seems a promising solution. However, these approaches suffer from two main challenges. The first challenge is state space explosion, which prevents generating all reachable states by the model checker. The second one is generating redundant test cases. Recently, several methods using meta-heuristic and evolutionary approaches have been proposed to cope with these problems. Therefore, exploring a portion of state space using an optimization approach to detect the test objectives can be a proper way to manage the state space explosion and generate an optimal test suite with the least redundancy. In this paper, a method is proposed using a Bayesian optimization algorithm (BOA), and a model checker is as a bed to generate test cases for the service-oriented systems. In the proposed approach, the test suite is a set of paths on the state space starting from an initial state and leading to the state in which all the test objectives are satisfied. In this research, we have implemented BOA with three different structures in GROOVE toolset, an open-source toolset for designing and model checking graph transformation. Experimental results show that our solution generates better results in terms of coverage and speed in different case studies than the existing approaches.

Grey Wolf Optimizer represents a relatively new metaheuristic scheme for solving continuous optimization problems. In spite of its interesting characteristics, it presents several flaws such as lack of accuracy, low diversity, premature convergence and imbalance between exploitation and exploration. In this paper, a modified version of the Grey Wolf Optimizer called Group-based Synchronous-Asynchronous Grey Wolf Optimizer is introduced. The proposed scheme incorporates a synchronous-asynchronous processing scheme, a set of different nonlinear functions and an operation to increase diversity. With such mechanisms, the proposed algorithm presents a better balance between exploration and exploitation, an increment in the accuracy and the ability to avoid the convergence in local minima. Such capacities allow its use in complex engineering problems that involve highly multimodal objective functions with a difficult localization of their global optimum. To evaluate the performance of the proposed approach, it has been tested on a representative number of functions of the well-known IEEE Congress on Evolutionary Computation 2017 benchmark set of functions and real-world engineering problems. The results of our method have been compared against those produced by other states of the art metaheuristic algorithms. The results prove the effectiveness and accuracy of the proposed technique.

Images are often corrupted by noise during their process of acquisition, storage and transmission. This alteration usually deteriorates the perception quality of the image. The central challenge in image denoising corresponds to eliminating the corrupted information while it is maintained the fine features of the original image. Anisotropic Diffusion (AD) is considered a well-established scheme for removing noise in digital images without deteriorating their edges. The selection of parameters that define the AD operation presents a very decisive implication in the filtering results. In spite of the importance of the AD operation, the problem of automatically calculating its parameter according to the image requirements has been scarcely considered in the literature. In this paper, a new multi-objective methodology to obtain the AD parameters for effective filter results is presented. In the proposed approach, the appropriate AD parameter values are obtained as the solution that involves the best possible trade-off between two conflicting scenarios: To minimize the noise content in the image while the contrast is maximized. Both objectives cannot be individually improved without worsening another. The proposed scheme uses to solve this formulation of the Non-dominated sorting genetic algorithm based on reference points (NSGA-III), which represent one of the most robust and powerful algorithms for multi-objective optimization. Then, the final solution is obtained from an analysis of the Optimal Pareto front. Experimental results demonstrate that the proposed method presents better results than existing filtering algorithms in terms of visual quality and standard performance metrics.

Multi-feature segmentation has demonstrated its superiority against one-dimensional feature approaches based on only grayscale information. Mean shift (MS) is an algorithm that has been used commonly for multi-feature segmentation. In spite of its interesting results, MS maintains a computational cost that is prohibitive for segmentation scenarios where the feature map consists of multi-dimensional features. In this paper, a new competitive segmentation algorithm for grayscale images is introduced. The proposed approach considers a two-dimensional feature map that includes the grayscale value and the local variance for each pixel in the image. To reduce the computational cost, the Mean shift (MS) algorithm is modified to operate with a very limited number of points from all available data. Under such conditions, two sets of elements are differentiated: involved data (the reduced dataset considered in the MS operation) and not involved data (the rest of the available data). Different from the classical MS, which employs Gaussian functions, in our approach, the process of estimating the feature map is carried out using a more accurate approach such as the Epanechnikov kernel function. Once the MS results are obtained, they are generalized to include the not involved data. Therefore, each unused element is assigned to the same cluster of the closest used data. Finally, clusters with the fewest elements are fused with other neighboring clusters. The proposed segmentation method has been compared with other state-of-art algorithms considering the full number of images from the Berkeley dataset. Experimental results confirm that the proposed scheme produces segmented images with a 50% better quality of visual perception approximately two times (≈ 1.8 − 2) faster than its competitors.

A clustering methodology based on biological visual models that imitates how humans visually cluster data by spatially associating patterns has been recently proposed. The method is based on Cellular Neural Networks and some resolution adjustments. The Cellular Neural Network rebuilds low-density areas while different resolutions find the best clustering option. The algorithm has demonstrated good performance compared to other clustering techniques. However, its main drawbacks correspond to its inability to operate with more than two-dimensional data sets and the computational time required for the resolution adjustment mechanism. This paper proposes a new version of this clustering methodology to solve such flaws. In the new approach, a pre-processing stage is incorporated featuring a Self-Organization Map that maps complex high-dimensional relations into a reduced lattice yet preserving the topological organization of the initial data set. This reduced representation is employed as the two-dimensional data set for further processing. In the new version, the resolution adjustment process is also accelerated through the use of an optimization method that combines the Hill-Climbing and the Random Search techniques. By incorporating such mechanisms rather than evaluating all possible resolutions, the optimization strategy finds the best resolution for a clustering problem by using a limited number of iterations. The proposed approach has been evaluated, considering several two-dimensional and high-dimensional datasets. Experimental evidence exhibits that the proposed algorithm performs the clustering task over complex problems delivering a 46% faster on average than the original method. The approach is also compared to other popular clustering techniques reported in the literature. Computational experiments demonstrate competitive results in comparison to other algorithms in terms of accuracy and robustness.

Optimization is used in different fields of engineering to solve complex problems. In image processing, multilevel thresholding requires to find the optimal configuration of thresholds to obtain accurate segmented images. In this case, the use of two-dimensional histograms is helpful because they permit us to combine information from the image preserving different features. This paper introduces a new method for multilevel image thresholding segmentation based on the improved version of the owl search algorithm (iOSA) and 2D histograms. The performance of the iOSA is enhanced with the inclusion of a new strategy in the optimization process. Moreover, in the initialization step, it is applied the opposition-based learning. Meanwhile, the 2D histograms permit to maintain more information of the image. Considering such modifications, the iOSA performs a better exploration of the search space during the early iterations, preserving the exploitation of the prominent regions using a self-adaptive variable. The iOSA is employed to allocate the optimal threshold values that segment the image by using the 2D Renyi entropy as an objective function. To test the efficiency of the iOSA, a set of experiments were performed which validate the quality of the segmentation and evaluate the optimization results efficacy. Moreover, to prove that the iOSA is a promising alternative for optimization and image processing problems, statistical tests and analyses were also conducted.

This paper proposes an optimal methodology based on the Differential Evolution algorithm for obtaining the set of duty cycles of a recently proposed power electronics converter with input current ripple cancelation capability. The converter understudy was recently introduced to the state-of-the-art as the interleaved connection of two unequal converters to achieve low input current ripple. A latter contribution proposed a so-called proportional strategy. The strategy can be described as the equations to relate the duty cycles of the unequal power stages. This article proposes a third switching strategy that provides a lower input current ripple than the proportional strategy. This is made by considering duty cycles independently of each other instead of proportionally. The proposed method uses the Differential Evolution algorithm to determine the optimal switching pattern that allows high quality at the input current side, given the reactive components, the switching frequency, and power levels. The mathematical model of the converter is analyzed, and thus, the decision variables and the optimization problem are well set. The proposed methodology is validated through numerical experimentation, which shows that the proposed method achieves lower input current ripples than the proportional strategy.

A recent meta-heuristic algorithm called Marine Predators Algorithm (MPA) is enhanced using Opposition-Based Learning (OBL) termed MPA-OBL to improve their search efficiency and convergence. A comprehensive set of experiments are performed to evaluate the MPA-OBL and prove the impact influence of merging OBL strategy with the original MPA in enhancing the quality of the solutions and the acceleration of the convergence speed, using IEEE CEC’2020 benchmark problems as recently complex optimization benchmark. In order to evaluate the performance of the proposed MPA-OBL, the effectiveness of conjunction of OBL with the original MPA and the other counterparts are calculated and compared with LSHADE with semi-parameter adaptation hybrid with CMA-ES (LSHADE_SPACMA-OBL), Restart covariance matrix adaptation ES (CMA_ES-OBL), Differential evolution (DE-OBL), Harris hawk optimization (HHO-OBL), Sine cosine algorithm (SCA-OBL), Salp swarm algorithm (SSA-OBL), and the original MPA. The extensive results and comparisons in terms of optimization metrics have revealed the superiority of the proposed MPA-OBL in solving the IEEE CEC’2020 benchmark problems and improving the convergence speed. Moreover, as a sequel to the proposed MPA-OBL, also, we have conducted experiments using two objective functions of Otsu and Kapur’s methods over a variety of benchmark images at different level of thresholds based on three commonly evaluation matrices namely Peak signal-to-noise ratio (PSNR), Structural similarity (SSIM), and Feature similarity (FSIM) indices are analyzed qualitatively and quantitatively. Eventually, the statistical post-hoc analysis reveal that the MPA-OBL obtains highly efficient and reliable results in comparison with the other competitor algorithms.

The natural aggregation algorithm (NAA) is a new efficient population-based optimizer. The NAA has a competent performance when compared to other well-established optimizers. However, a problem of concern is NAA lack of exploitation in its local search. In this article, we propose an improved version of NAA. The modifications made are: hypercubes with displacement and shrink mechanism applied in each shelter, we designed a new movement operator to search inside the hypercubes, an improved readjustment of the algorithm’s parameters and “leave shelter” formula of NAA, to better mimic the aggregation behavior. To prove the effectiveness of the modified hypercube natural aggregation algorithm (HYNAA), we compared with classics optimizers, such as PSO, DE and ABC, state of the art, such as CMA-ES, MSA and NAA himself with a benchmark of 28 functions. The said functions consist of five unimodal, 19 multimodal and four hybrids, and we compared them on 30, 50 and 100 dimensions. We also made extra comparisons against NAA in 500 and 1000 dimensions to contrast the ability of the hypercubes to reduce the dimensional complexity. Finally, we tested two trajectory optimization problems. Experimental results and statistical tests demonstrate that the performance of HYNAA is significantly better than that of other optimizers.

This paper proposes a general multi-objective thresholding segmentation methodology for color images and a quality metric designed to prevent and quantify the overlapping effect of segmented images. Multi-level thresholding (MTH) has been used to segment color images in recent years; this process considers each channel as a single grayscale image and applies the MTH independently. Although this method provides competitive results, the inherent relationship among color channels is disregarded. Such approaches generate spurious classes on overlapping regions, where new colors are generated, especially on the borders of the objects. The proposed multi-objective color thresholding (MOCTH) approach performs image segmentation while preserving the relationship between image channels. MOCTH is aimed to reduce the overlapping effect on segmented color images without performing additional post-processing. To measure the overlapping classes on a thresholded color image, the overlapping index is proposed to quantify the pixels affected. The presented approach is analyzed on two color spaces (RGB and CIE L*a*b*) using three multi-objective algorithms; they are NSGA-III, SPEA-2, and MOPSO. Results provide evidence pointing out to a better segmentation from MOCTH over the traditional single-objective approaches while reducing overlapped areas on the image.

Evolutionary computation algorithms represent a class of stochastic methods that can be applied to a wide set of different complex optimization problems. Recently, the combination of approaches extracted from different computation techniques represents one of the most successful trends in evolutionary optimization. With this integration, the idea is to overcome the limitations of each single method and to reach a synergetic effect through their integration. In this paper, a hybrid optimization algorithm for solving optimization problems is introduced. The approach, called cluster–chaotic-optimization, combines the classification characteristics of a clustering method with the randomness of chaotic sequences to conduct its search strategy. Under the proposed method, at each generation, the population is divided into different clusters according to its space distribution. Then, individuals are modified considering two kinds of operators: intra-cluster and extra-cluster. In the intra-cluster operation, individuals of the same cluster are locally adjusted considering the position of the best element of the cluster in terms of its fitness value. On the other hand, in the extra-cluster operation, the best individual of each cluster is globally attracted to the best element of the complete population. In both operations, the adjustment on each individual position is produced by using deterministic rules and chaotic sequences. With such mechanisms, the proposed method efficiently examines the search space based on the spatial associations produced by the individuals during the optimization process. To exhibit the performance and robustness of the proposed method, different comparisons to other well-known evolutionary methods and hybrid approaches are conducted. The comparison considers several standard benchmark functions and real-world engineering problems which are typically found in the literature of evolutionary algorithms. The results suggest a high performance of the proposed methodology.

Hybrid evolutionary methods combine approaches extracted from different evolutionary computation techniques to build a single optimization method. The design of such systems represents a current trend in the evolutionary optimization literature. In hybrid algorithms, the objective is to extend the potential advantages of the integrated approaches and eliminate their main drawbacks. In this work, a hybrid method for solving optimization problems is presented. The proposed approach combines (A) the explorative characteristics of the invasive weed optimization method, (B) the probabilistic models of the estimation distribution algorithms and (C) the dispersion capacities of a mixed Gaussian–Cauchy distribution to produce its own search strategy. With these mechanisms, the proposed method conducts an optimization strategy over search areas that deserve a special interest according to a probabilistic model and the fitness value of the existent solutions. In the proposed method, each individual of the population generates new elements around its own location, dispersed according to the mixed distribution. The number of new elements depends on the relative fitness value of the individual regarding the complete population. After this process, a group of promising solutions are selected from the set compound by the (a) new elements and the (b) original individuals. Based on the selected solutions, a probabilistic model is built from which a certain number of members (c) are sampled. Then, all the individuals of the sets (a), (b) and (c) are joined in a single group and ranked in terms of their fitness values. Finally, the best elements of the group are selected to replace the original population. This process is repeated until a termination criterion has been reached. To test the performance of our method, several comparisons to other well-known metaheuristic methods have been made. The comparison consists of analyzing the optimization results over different standard benchmark functions within a statistical framework. Conclusions based on the comparisons exhibit the accuracy, efficiency and robustness of the proposed approach.

In machine learning and data mining, feature selection (FS) is one of the most important tasks required to select the most relevant instances from a dataset. In other words, FS is used to reduce the amount of information, creating a subset that represents the entire pool of data. The accuracy of the FS is reflected in a good classification of the information. This article presents an improved version of the social spider optimization (SSO) algorithm. The SSO tends to fail in local optima during the iterative process and is not possible to avoid this situation in the standard form. The proposed version avoids selecting the irrelevant features that demerit the performance of the FS. To achieve this goal, the opposition-based learning is used, in which there is a rule used to increase the exploration of the search space and the prominent zones in a determined neighborhood. The proposed algorithm is called opposition-based social spider optimization (OBSSO), and it has been tested over different mathematical problems. Moreover, the OBSSO, also, has been tested and compared with similar approaches using different datasets with specific information selected from UCI repository. The experimental results provide the evidence of the capabilities of the OBSSO for solving complex optimization problems.

This manuscript presents the numerical optimization (through a mathematical model and an evolutionary algorithm) of the voltage-doubler boost converter, also called the series-capacitor boost converter. The circuit is driven by two transistors, each of them activated according to a switching signal. In the former operation, switching signals have an algebraic dependence from each other. This article proposes a new method to operate the converter. The proposed process reduces the input current ripple without changing any converter model parameter, only the driving signals. In the proposed operation, switching signals of transistors are independent of each other, providing an extra degree of freedom, but on the other hand, this produces an infinite number of possible combinations of duty cycles (the main parameter of switching signals) to achieve the desired voltage gain. In other words, this leads to a problem with infinite possible solutions. The proposed method utilizes an evolutionary algorithm to determine the switching functions and, at the same time, to minimize the input current ripple of the converter. A comparison made between the former and the proposed operation shows that the proposed process achieves a lower input current ripple while achieving the desired voltage gain.

This chapter presents the main concepts of metaheuristic schemes for Wireless Sensor Networks (WSNs). WSNs are multi-functional, low-cost, and low-power networks and rely on communications among devices, from sensor nodes to one or more sink nodes. Sink nodes, sometimes called coordinator nodes or root nodes, may be more robust and have larger processing capacity than the other nodes.

The world, as we know, it would not exist without the development of machine learning (ML). Its application in processing information gives the human more advantages than in any other age. The use of ML brings the opportunity to perform categorizations into a big amount of elements and into areas that the human simply can not do it without this technology. But the development of ML algorithms is complex since it requires specific theoretical bases and the development of skills to achieve the tuning of these algorithms. Given the wide variety of algorithms and techniques to perform element classification, this chapter focuses on the study of Convolutional Neural Networks (CNN), and the development of tests that facilitate the understanding of the effects of their basic parameters. The classification objects used are images, and only the multiple class classification model is considered.

Hybrid metaheuristic methods combine approaches extracted from different techniques to build a single optimization method. The design of such systems represents a current trend in the metaheuristic optimization literature. In hybrid algorithms, the objective is to extend the potential advantages of the integrated approaches and eliminates their main drawbacks. In this chapter, a hybrid method for solving optimization problems is presented. The presented approach combines (1) the explorative characteristics of the invasive weed optimization (IWO) method, (2) the probabilistic models of the estimation distribution algorithms (EDA), and (3) the dispersion capacities of a mixed Gaussian-Cauchy distribution to produce its own search strategy. With these mechanisms, the method conducts an optimization strategy over search areas that deserve a special interest according to a probabilistic model and the fitness value of the existent solutions. In the presented method, each individual of the population generates new elements around its own location, dispersed according to the mixed distribution. The number of new elements depends on the relative fitness value of the individual regarding the complete population. After this process, a group of promising solutions is selected from the set compound by the (1) new elements and the (2) original individuals. Based on the selected solutions, a probabilistic model is built from which a certain number of members (3) is sampled. Then, all the individuals of the sets (1), (2), and (3) are joined in a single group and ranked in terms of their fitness values. Finally, the best elements of the group are selected to replace the original population. This process is repeated until a termination criterion has been reached. To test the performance of the method, several comparisons to other well-known metaheuristic methods has been conducted. The comparison consists of analyzing the optimization results over different standard benchmark functions within a statistical framework. Conclusions based on the comparisons exhibit the accuracy, efficiency, and robustness of the presented approach.

Clustering involves the process of dividing a collection of abstract objects into a number of groups, which are integrated with similar elements. Several clustering methods have been introduced and developed in the literature with different performance levels. Among such approaches, density algorithms present the best advantages, since they are able to find clusters from a dataset under different scales, shapes, and densities without requiring the number of groups as input. On the other hand, there are processes that humans perform much better than deterministic approaches or computers. In fact, humans can visually cluster data exceptionally well without the necessity of any training. Under this unique capability, clustering, instantaneous, and effortless for humans, represents a fundamental challenge for artificial intelligence. In this chapter, a simple clustering model inspired by the way in which the human visual system associates patterns spatially is presented. The model, at some abstraction level, can be characterized as a density grouping strategy. The approach is based on Cellular Neural Networks (CNNs), which have demonstrated to be the best models for emulating the human visual system. In the method, similar to the biological model, CNN is used to build especially groups while an automatic mechanism tries different resolution scales to find the best possible data categorization. Different datasets have been adopted to evaluate the performance of the algorithm. Their results are also compared with popular density clustering techniques from the literature. Computational results demonstrate that the CNN approach presents competitive results in comparison with other algorithms regarding accuracy and robustness.

In the last years, medical image processing has become an important tool for health care. In particular, the analysis of retinal vessel images has become crucial for achieving a more accurate diagnosis and treatment for several cardiovascular and ophthalmological deceases. However, this task is extremely hard and time-consuming, often requiring human supervised segmentation of fundus images as well as some degree of professional skills. An automatic yet accurate procedure for retinal vessel segmentation is essential to assist ophthalmologists with illness detection. Several retinal vessel segmentation methods have been developed with satisfactory results. Nevertheless, the image preprocessing techniques implemented in this kind of procedure is known to have poor performance, mainly due to the complex nature of retinal vessel imaging. To improve the image preprocessing stage, a fast and accurate approach for retinal vessel segmentation is presented. This approach aims to enhance the contrast between retinal vessels and the image’s background by implementing a natural inspired technique called Lateral Inhibition (LI). Additionally, a cross-entropy minimization procedure based on Differential Evolution (DE) is applied to find the appropriate threshold and then used to define whether an image pixel is a vessel or non-vessel. The described method has been tested by considering two well-known image databases: DRIVE and STARE. The obtained results are compared against those of other related methods in order to prove the accuracy, effectiveness, and robustness of the presented approach.

Corner detection represents one of the most important steps to identify features in images. Due to their powerful local processing capabilities, Cellular Nonlinear/Neural Networks (CNN) are commonly utilized in image processing applications such as image edge detection, image encoding and image hole filling. CNN perform well for locating corner features in binary images. However, their use in grayscale images has not been considered due to their design difficulties. In this chapter, a corner detector based on CNN for grayscale images is presented. In the approach, the original processing scheme of the CNN is modified to include a nonlinear operation for increasing the contrast of the local information in the image. With this adaptation, the final CNN parameters that allow the appropriate detection of corner points are estimated through the Differential evolution algorithm by using standard training images. Different test images have been used to evaluate the performance of the presented corner detector. Its results are also compared with popular corner methods from the literature. Computational simulations demonstrate that the presented CNN approach presents competitive results in comparison with other algorithms in terms of accuracy and robustness.

Almost all industries and businesses make purchases; they usually have more than one possible supplier; in most cases, they have many possible suppliers, since suppliers have different prices, policies, different levels of quality. Making wise decisions may save money, which has an impact on the final price of products, and therefore, on the company’s competitiveness. The problem can be solved using mathematical models to represent the cost of the inventory management, and finally, applying techniques to minimize the cost. Mathematical models of the inventory management problem may be complex and NP-hard, and as a result, evaluating all possible solutions to find the cheapest one may be unfeasible, even with a computer. When that happens, metaheuristic algorithms may be used to find a reasonable solution in a reasonable amount of time. This chapter deals with this topic. This chapter presents an example problem, in order to illustrate the complexity of the inventory management and how a large number of possible solutions may arise, then it solves the problem with traditional and metaheuristic algorithms to demonstrate the advantages of metaheuristic algorithms. Finally, other problems are discussed, and further information about algorithms is provided.

The hyperparameters of a convolutional neural network always have been important, because the performance of the convolutional neural network depends largely on them. To find manually the optimal value requires a lot of work, experience, and time. Metaheuristic algorithms are approximate general-purpose search and optimization algorithms, they are used to find a good solution into search space. In the book chapter, the hyperparameters of a convolutional neural network are optimized. A metaheuristic algorithm is used to finds the best solution, that is, the best hyperparameter value for higher accuracy of the convolutional neural network (CNN). The results of four metaheuristic algorithms that have the same objective are compared, which are PSO (Particle Swarm Optimization), ABC (Artificial Bee Colony), ALO (Ant Lion Optimization), and BA (Bat Algorithm). Each algorithm uses the same convolutional neural network architecture as well as the same dataset (MNIST database) also each algorithm is run 30 times to perform the corresponding statistics. Finally, the results of the algorithms are shown.

Chest radiography is one of the most widely used imaging techniques for the detection and diagnosis of lung diseases. However, the correct extraction of information in these images is a tedious and challenging task that depends on the experience of radiologists. Due to this, in recent years, computer-aided diagnostic tools have been implemented to improve the quality and productivity in the diagnosis of radiological tests. The segmentation process represents an important step for any automatic method of information extraction because it simplifies the representation of images. Recently, the use of the threshold segmentation technique has become popular due to its simplicity in implementation and precision. In this work, is proposed an efficient segmentation approach based on the Whale Optimization Algorithm (WOA) to treat the maximization of two statistical criteria, the Otsu’s variance and the Kapur’s entropy. The method is tested for the segmentation of X-ray images to visually diagnose pneumonia, determining details such as the scope, location of the infection, and some complications that may arise. To validate the efficacy of the approach with both statistical criteria, it is tested with chest radiography images from a database and compared with four other metaheuristic algorithms. Comparisons are made to verify the quality of the segmented image using the PSNR, SSIM and FSIM metrics. According to the experimental results, the WOA algorithm used to maximize the variance of Otsu presents a better segmentation compared to the other algorithms.

This chapter approaches the topic of Extreme Learning Machines (ELM); this technique is implemented in a single layer feedforward neural network and provides to the neural network with a fast performance since it is not necessary to adjust weights and hidden layer biases. ELM is implemented and combined with three metaheuristic algorithms. The selected algorithms belong to the family of Swarm intelligence such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) and Grey Wolf Optimization (GWO); these algorithms are applied to optimize the hidden biases and input weights and are implemented for improving the performance along with techniques of Extreme learning machines in data classification. Also, the performance and behavior of each algorithm can be observed. The data employed in this work consist of information about Australian credit, diabetes detection and hearth disease. The investigation result show that GWO obtained better results in the first dataset and PSO are better in the second and third datasets.

Animals have demonstrated clever hunting strategies when they collaborate with each other. These techniques involve swarm intelligence and have been applied to develop bio-inspired algorithms, a research field for solving optimization problems. In the last years, several methods arise from inspiration in nature to model collective animal behavior.

Power-electronics based converters are essential circuits in renewable energy applications such as electricity generated with photovoltaic panels. The research on the field is getting increasing attention due to climate change problems and their possible attenuation with the use of renewable energy. Mathematical models of the converters are being used to optimize several aspects of their operation. This article is dedicated to optimizing (through the mathematical model and an evolutionary algorithm) the operation of a state-of-the-art converter. The converter, which is composed of two parts or phases, is controlled by pulse width modulation with two switching signals (one for each phase). The converter provides by itself low switching ripple in both the output voltage and the input current, which is beneficial for renewable energy applications. In the traditional operation, one of the switching signals has an algebraic dependence on the other one. This article proposes a new way to select the duty cycle for both signals. In the proposed method, duty cycles of both phases are considered independent of each other, this provides an extra degree of freedom, on the other hand, this produce that the possible combinations of duty cycles which produce a certain voltage gain is infinite, it becomes a problem with infinite possible solutions. The proposed method utilizes the a linear success-history based adaptive differential evolution with linear population reduction, also called L-SHADE algorithm for simplicity, to find the two duty cycles that achieve the desired voltage gain and to minimize the converters switching ripple. The obtained results are compared with the former operation of the converter, the proposed operation achieves a lower output voltage ripple while achieving the desired operation (voltage gain).

There exist hundreds of metaheuristic methods that can be employed to obtain the optimal value in an optimization problem. To present a good performance, every metaheuristic scheme requires to achieve an adequate balance between exploration and exploitation of the search space. Even though exploration and exploitation are considered two important concepts in metaheuristics computation, the main implications with this equilibrium have not yet been completely understood. Most of the existent studies consider only the comparison of their final results, which cannot appropriately evaluate the existent balance between both concepts. This chapter conducts an experimental study where it is analyzed the balance between exploration and exploitation on several of the most popular metaheuristic schemes. In the analysis, a diversity measurement for each dimension is employed to evaluate the equilibrium of each metaheuristic approach, considering a representative set of different optimization problems.

The nature of many real problems in the world is nonlinear type, and identifying their plants and processes symbolizes a challenging task. Nowadays, the block-structure systems, such as the Hammerstein model, are among the most current nonlinear systems. The main characteristic of a Hammerstein model is that its architecture is made up of two blocks; a linear dynamic model preceded by a static nonlinear. The adaptive neuro-fuzzy inference system (ANFIS) is a robust scheme that incorporates a two parts structural design; a nonlinear rule-based and a linear system. In this chapter, it is proposed a new scheme based on the Hammerstein block-structure model for nonlinear system identification. The methodology introduced takes benefit of the correspondence between the ANFIS and Hammerstein structure to couple them and model nonlinear systems. The Gravitational Search Algorithm (GSA) is incorporated to the methodology to identify the model system parameters. The GSA, compared to similar optimization algorithms, achieves more reliable performance in multimodal problems, avoiding being trapped in premature solutions that are not optimal. To test and validate the effectiveness of the methodology, it has been tested over several models and compared with related works in literature showing a higher accuracy in the results.

In recent years, researchers have proved that the electrification of the transport sector is vital for reducing both the emission of green-house pollutants and the dependence on oil for transportation. As a result of this, Plug-in Hybrid Electric Vehicles (PHEVs) have received increasing attention during the last decade. A large scale penetration of PHEVs into the marked is expected to take place shortly; however, an unattended increase on the PHEVs needs may yield to several technical problems that could potentially compromise the stability of power systems. As a result of the growing necessity for addressing such issues, topics related to the optimization of PHEVs’ charging infrastructures have captured the attention of many researchers. Related to this, several state-of-the-art swarm optimization techniques (such as the well-known Particle Swarm Optimization (PSO) algorithm or the recently proposed Gravitational Search Algorithm (GSA) approach) have been successfully applied in the optimization of the average State of Charge (SoC), which stand as one of the most important performance indicators in the context of PHEVs’ intelligent power allocation; however, many of these swarm optimization methods are known to be subject to several critical flaws, such as premature convergence and a lack of balance between the exploration and exploitation of solutions. Such problems are usually related to the evolutionary operators employed by each of such methods on the exploration and exploitation of new solutions. In this chapter, the recently proposed States of Matter Search (SMS) swarm optimization method is proposed for maximizing the average State of Charge of PHEVs within a charging station. In our experiments, several different scenarios consisting of different numbers of PHEVs were considered. In order to test the feasibility of the proposed approach, comparative experiments were performed against other popular PHEVs’ State of Charge maximization approaches based on swarm optimization methods. The results obtained on our experimental set up show that the proposed SMS based SoC maximization approach has an outstanding performance in comparison to that of the other compared methods, and as such, proves to be superior for tackling the challenging problem of PHEVs smart charging.

Various methods are so complex to be handled quantitatively; However, human beings have achieved by using simple rules that are extracted from their experiences. Fuzzy logic resembles human reasoning in its use of information to generate inaccurate decisions. Diffuse logic incorporates an alternative way of processing that allows complex systems to be modeled using a high level of abstraction originating from human knowledge and experiences. Recently, several of the new evolutionary computing algorithms have been proposed with exciting results. Several of them use operators based on metaphors of natural or social elements that evolve candidate solutions. Although humans have demonstrated their potential to solve complicated optimization problems of everyday life, they are not mechanisms to include such aptitudes into an evolutionary optimization algorithm. In this chapter, a methodology to implement human intelligence based on strategy optimization is presented. Under this approach, a procedure carried out is codified in rules based on Takagi-Sugeno diffuse inference system. So, to implement fuzzy practices, they express the conditions under which candidate solutions are evolved into new positions. To show the capability and robustness of the proposed approach, it is compared to other well-known evolutionary methods. The comparison examines several benchmark functions (benchmark) that are generally considered within the literature of evolutionary algorithms. The results confirm a high performance of the method in the search for a global optimum of different benchmark functions.

In recent years, image processing techniques have been an essential tool for health care. A good and timely analysis of retinal vessel images has become relevant in the identification and treatment of diverse cardiovascular and ophthalmological illness. Therefore, an automatic and precise method for retinal vessel and optic disc segmentation is crucial for illness detection. This task is arduous, time-consuming, and generally developed by an expert with a considerable grade of professional skills in the field. Various retinal vessel segmentation approaches have been developed with promissory results. Although, most of such methods present a deficient performance principally due to the complex structure of vessels in retinal images. In this work, an accurate and hybrid methodology for retinal vessel and optic disc segmentation is presented. The method proposed a fusion of two different schemas: the lateral inhibition (LI) and Differential Evolution (DE). LI is used to improve the contrast between the retinal vessel and background. Followed by the minimization of the cross-entropy function to find the threshold value, these performed by the second schema the DE algorithm. To test the performance and accuracy of the proposed methodology, a set of images obtained from three public datasets STARE, DRIVE, and DRISHTI-GS have been used in different experiments. Simulation results demonstrate the high performance of the proposed approach in comparison with related methods reported in the literature.

The development of methods to solve optimization problems has increased in recent years. However, many of these methods are not tested in real applications. Energy problems are a topic of high relevance within the scientific community due to its environmental repercussions. Two representative cases involved in the huge energy consumption are the capacitor placement in radial distribution networks and parameter identification in induction motors. Both problems have intrinsically complex characteristics from the optimization perspective, which makes it difficult to solve them by conventional optimization methods. Some of these properties are their high multi-modality, discontinuity and non-linearity. Alternatively, metaheuristic techniques have had shown performance in solving the solution to a wide range of complex engineering problems. A recent metaheuristic based on the intelligent group behavior of crows and their interaction is the Crow Search Algorithm (CSA). Although CSA shows interesting aspects, its performance in exploration mechanism exhibits considerable disadvantages when it confronts high multi-modal conditions. In this chapter, an enhance variant of the CSA approach is introduced to face complex optimization formulations of energy. The improved method is focused on modifying two main features from the original CSA: (I) the random perturbation and (II) the fixed awareness probability (AP). With the modifications, the enhance methodology conserves diversity in global solution and enhances the convergence to difficult high multi-modal optima. The performance evaluation of the presented methodology is addressed in a series of optimization problems related to distribution networks and inductions motors. The results of the evaluation show the robust performance of the presented methodology when it is contrasted with popular approaches in the literature.

In the evolutionary methods, the optimal balance of exploration and exploitation performance in search strategies improve efficiency to found the best solution. In this chapter, an improved swarm optimization technique called Locust search II (LS-II) based on the desert locust swarm behavior, adapted to an emulation of a group of locusts which interacts to each other based on the biological laws of the cooperative swarm is proposed for solving global optimization problems. Such methodology combines a technique of exploration which avoids premature convergence effectively and a technique of exploitation able to intensify the global solutions. The proposed LS-II method was tested over several well-known benchmark test functions and engineering optimization problems and its performance was further compared against those of other state-of the-art methods such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Bat Algorithm (BA), Differential Evolution (DE), Harmony Search (HS) and the original Locust Search (LS). Our experimental results show LS-II to be superior to all other compared methods in terms of solution quality and as such proves to be an excellent alternative to handle complex optimization problems.

Power losses are presents in the distribution of energy from sources to points of consumption. This power loss is commonly caused by the lack of reactive power. The installation of capacitor banks is an alternative to provided reactive power compensation to Radial Distribution Networks (RDNs). A suitable allocation of capacitor banks brings several advantages to the RDNs, comprising a power loss reduction and enhancement in the voltage profile in the system. These improvements lead to significant energy savings and cost reductions. From an optimization view, this problem is known in literature as Optimal Capacitor Allocation (OCA) and is conceptualized significantly complex by its discontinuity, non-linearity and high multi-modality. In this work, the OCA problem is addressed by the swarm optimization algorithm Locust Search (LS). To measure the performance of the proposed method, it has been tested over diverse and IEEE’s radial distribution test system and compared against other related methodologies currently reported in literature to solve the OCA.

This chapter illustrates the use of metaheuristic computation schemes for the automatic detection of white blood cells embedded into complicated and cluttered smear images. The approach considers the identification problem as the process of detection of multi-ellipse shapes. The scheme uses the Differential Evolution (DE) method, which is easy to use, maintains a quite simple computation scheme presenting acceptable convergence properties. The approach considers the use of five edge points as agents which represent the candidate ellipses in the edge image of the smear. A cost function assesses if such candidate ellipses are present in the actual edge image. With the values of the cost function, the set of agents is modified by using the DE algorithm so that they can approximate the white blood cells contained in the edge-only map of the image.

This chapter presents the main concepts of metaheuristic schemes. The objective of this chapter is to introduce the characteristics and properties of these approaches. An important propose of this chapter is also to recognize the importance of metaheuristic methods to solve optimization problems in the cases in which traditional techniques are not suitable.

Supplier selection and order quantity allocation have a strong influence on a company&rsquo, s profitability and the total cost of finished products. From an optimization perspective, the processes of selecting the right suppliers and allocating orders are modeled through a cost function that considers different elements, such as the price of raw materials, ordering costs, and holding costs. Obtaining the optimal solution for these models represents a complex problem due to their discontinuity, non-linearity, and high multi-modality. Under such conditions, it is not possible to use classical optimization methods. On the other hand, metaheuristic schemes have been extensively employed as alternative optimization techniques to solve difficult problems. Among the metaheuristic computation algorithms, the Grey Wolf Optimization (GWO) algorithm corresponds to a relatively new technique based on the hunting behavior of wolves. Even though GWO allows obtaining satisfying results, its limited exploration reduces its performance significantly when it faces high multi-modal and discontinuous cost functions. In this paper, a modified version of the GWO scheme is introduced to solve the complex optimization problems of supplier selection and order quantity allocation. The improved GWO method called iGWO includes weighted factors and a displacement vector to promote the exploration of the search strategy, avoiding the use of unfeasible solutions. In order to evaluate its performance, the proposed algorithm has been tested on a number of instances of a difficult problem found in the literature. The results show that the proposed algorithm not only obtains the optimal cost solutions, but also maintains a better search strategy, finding feasible solutions in all instances.

Shape recognition in images represents one of the complex and hard-solving problems in computer vision due to its nonlinear, stochastic and incomplete nature. Classical image processing techniques have been normally used to solve this problem. Alternatively, shape recognition has also been conducted through metaheuristic algorithms. They have demonstrated to have a competitive performance in terms of robustness and accuracy. However, all of these schemes use old metaheuristic algorithms as the basis to identify geometrical structures in images. Original metaheuristic approaches experiment several limitations such as premature convergence and low diversity. Through the introduction of new models and evolutionary operators, recent metaheuristic methods have addressed these difficulties providing in general better results. This paper presents a comparative analysis on the application of five recent metaheuristic schemes to the shape recognition problem such as the Grey Wolf Optimizer (GWO), Whale Optimizer Algorithm (WOA), Crow Search Algorithm (CSA), Gravitational Search Algorithm (GSA) and Cuckoo Search (CS). Since such approaches have been successful in several new applications, the objective is to determine their efficiency when they face a complex problem such as shape detection. Numerical simulations, performed on a set of experiments composed of images with different difficulty levels, demonstrates the capacities of each approach.

In the past years, the interest in direct current to direct current converters has increased because of their application in renewable energy systems. Consequently, the research community is working on improving its efficiency in providing the required voltage to electronic devices with the lowest input current ripple. Recently, a hybrid converter which combines the boost and the Cuk converter in an interleaved manner has been introduced. The converter has the advantage of providing a relatively low input current ripple by a former strategy. However, it has been proposed to operate with dependent duty cycles, limiting its capacity to further decrease the input current ripple. Independent duty cycles can significantly reduce the input current ripple if the same voltage gain is achieved by an appropriate duty cycle combination. Nevertheless, finding the optimal duty cycle combination is not an easy task. Therefore, this article proposes a new pulse-width-modulation strategy for the hybrid interleaved boost-Cuk converter. The strategy includes the development of a novel mathematical model to describe the relationship between independent duty cycles and the input current ripple. The model is introduced to minimize the input current ripple by finding the optimal duty cycle combination using the differential evolution algorithm. It is shown that the proposed method further reduces the input current ripple for an operating range. Compared to the former strategy, the proposed method provides a more balanced power-sharing among converters.

The design of two-dimensional Infinite Impulse Response (2D-IIR) filters has recently attracted attention in several areas of engineering because of their wide range of applications. Synthesizing a user-defined filter in a 2D-IIR structure can be interpreted as an optimization problem. However, since 2D-IIR filters can easily produce unstable transfer functions, they tend to compose multimodal error surfaces, which are computationally difficult to optimize. On the other hand, Evolutionary Computation (EC) algorithms are well-known global optimization methods with the capacity to explore complex search spaces for a suitable solution. Every EC technique holds distinctive attributes to properly satisfy particular requirements of specific problems. Hence, a particular EC algorithm is not able to solve all problems adequately. To determine the advantages and flaws of EC techniques, their correct evaluation is a critical task in the computational intelligence community. Furthermore, EC algorithms are stochastic processes with random operations. Under such conditions, for obtaining significant conclusions, appropriate statistical methods must be considered. Although several comparisons among EC methods have been reported in the literature, their conclusions are based on a set of synthetic functions, without considering the context of the problem or appropriate statistical treatment. This paper presents a comparative study of various EC techniques currently in use employed for designing 2D-IIR digital filters. The results of several experiments are presented and statistically analyzed.