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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.

Accurate color image segmentation has stayed as a relevant topic between the researches/scientific community due to the wide range of application areas such as medicine and agriculture. A major issue is the presence of illumination variations that obstruct precise segmentation. On the other hand, the machine learning unsupervised techniques have become attractive principally for the easy implementations. However, there is not an easy way to verify or ensure the accuracy of the unsupervised techniques; so these techniques could lead to an unknown result. This paper proposes an algorithm and a modification to the HSV color model in order to improve the accuracy of the results obtained from the color segmentation using the K-means++ algorithm. The proposal gives better segmentation and less erroneous color detections due to illumination conditions. This is achieved shifting the hue and rearranging the H equation in order to avoid undefined conditions and increase robustness in the color model.

As a mean to provide reactive power compensation to radial distribution networks, capacitor banks are commonly installed within these electrical distribution systems. When properly allocated, capacitor banks provide several benefits, including an improvement on the feeder’s voltage profile and significant reductions on power loss, which, in turn, leads to important amounts of energy savings and cost reductions. This formulation is known in the literature as optimal capacitor placement (OCP) problem. From an optimization perspective, OCP is considered remarkably complex due to its high multi-modality, discontinuity, and non-linearity. In this paper, the swarm optimization algorithm known as locust search (LS) is proposed for solving the OCP problem. The proposed approach has been tested by considering several IEEE’s radial distribution test systems, and its performance has also been compared against that of other techniques currently reported on the literature to solve the OCP problem. Our experimental results suggest that the proposed LS-based method is able to competitively solve the OCP problem in terms of accuracy and robustness.

The identification of real-world plants and processes, which are nonlinear in nature, represents a challenging problem. Currently, the Hammerstein model is one of the most popular nonlinear models. A Hammerstein model involves the combination of a nonlinear element and a linear dynamic system. On the other hand, the Adaptive-network-based fuzzy inference system (ANFIS) represents a powerful adaptive nonlinear network whose architecture can be divided into a nonlinear block and a linear system. In this paper, a nonlinear system identification method based on the Hammerstein model is introduced. In the proposed scheme, the system is modeled through the adaptation of an ANFIS scheme, taking advantage of the similarity between it and the Hammerstein model. To identify the parameters of the modeled system, the proposed approach uses a recent nature-inspired method called the Gravitational Search Algorithm (GSA). Compared to most existing optimization algorithms, GSA delivers a better performance in complex multimodal problems, avoiding critical flaws such as a premature convergence to sub-optimal solutions. To show the effectiveness of the proposed scheme, its modeling accuracy has been compared with other popular evolutionary computing algorithms through numerical simulations on different complex models.

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.