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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.

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.

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.

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.

The efficient use of energy in electrical systems has become a relevant topic due to its environmental impact. Parameter identification in induction motors and capacitor allocation in distribution networks are two representative problems that have strong implications in the massive use of energy. From an optimization perspective, both problems are considered extremely complex due to their non-linearity, discontinuity, and high multi-modality. These characteristics make difficult to solve them by using standard optimization techniques. On the other hand, metaheuristic methods have been widely used as alternative optimization algorithms to solve complex engineering problems. The Crow Search Algorithm (CSA) is a recent metaheuristic method based on the intelligent group behavior of crows. Although CSA presents interesting characteristics, its search strategy presents great difficulties when it faces high multi-modal formulations. In this paper, an improved version of the CSA method is presented to solve complex optimization problems of energy. In the new algorithm, two features of the original CSA are modified: (I) the awareness probability (AP) and (II) the random perturbation. With such adaptations, the new approach preserves solution diversity and improves the convergence to difficult high multi-modal optima. In order to evaluate its performance, the proposed algorithm has been tested in a set of four optimization problems which involve induction motors and distribution networks. The results demonstrate the high performance of the proposed method when it is compared with other popular approaches.