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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.

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.