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Proposing new metaheuristic methods is crucial for continuous algorithmic improvement and the ability to effectively address increasingly complex real-world optimization problems. On the other hand, Evolutionary Game Theory analyzes how trough competition is possible to modify the strategies of individuals within a population in order to spread successful mechanisms and reduce or remove less successful strategies. This paper introduces a novel optimization approach based on the principles of evolutionary game theory. In the proposed method, all individuals are initialized using the Metropolis–Hasting technique, which sets the solutions at a starting point closer to the optimal or near-optimal regions of the problem. An original strategy is then assigned to each individual in the population. By considering the interactions and competition among different agents in the optimization problem, the approach modifies the strategies to improve search efficiency and find better solutions. To evaluate the performance of the proposed technique, it is compared with eight well-known metaheuristic algorithms using 30 benchmark functions. The proposed methodology demonstrated superiority in terms of solution quality, dimensionality, and convergence when compared to other approaches. [ABSTRACT FROM AUTHOR]

Bonobo optimizer (BO) is a recent metaheuristic algorithm inspired by the social behavior of bonobos. BO maintains a robust search strategy based on two distinct phases that involve interesting mechanisms, such as the fission–fusion method for selection and an efficient process for producing new candidate solutions. Despite its remarkable capacity, BO presents a critical flaw that corresponds to an inappropriate balance between exploration and exploitation. Under such conditions, suboptimal or even poor solutions can be obtained. This paper proposes a modified BO algorithm in which the trajectory of each search agent is modified dynamically through the adaptation of the main parameters of the algorithm. With this new mechanism, the algorithm allows for the exploration and exploitation of different regions of the solution space and determines the global optimum in a faster manner. The performance of the proposed algorithm is evaluated by considering two scenarios. One is the optimization of 23 benchmark functions and the second is the problem of power allocation in wireless networks. The results show that the proposed scheme enhances the performance of the basic BO method by increasing the robustness and providing a better solution quality. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Template matching (TM) is a technique used in computer vision that helps us to identify small pieces of a predefined sub image known as template and a corresponding area inside a large image. There are several approaches in the literature that solve this problem. In spite of their acceptable results, the existing methods fail to detect templates when they are rotated in a large image. On the other hand, the structural similarity (SSIM) index is a resemblance model that allows us to evaluate the affinity between two images. Different from other similarity indexes, SSIM considers important structural information while also incorporating essential elements such as luminance and contrast terms. This paper presents a new approach for template matching when an image is rotated. Our method considers the complete process as an optimization problem where a metaheuristic algorithm is used to find the best resemblance between the template and its rotated or unrotated image. In order to obtain a robust detection, our approach considers the use of the SSIM index as an objective function. Although metaheuristic approaches produce interesting results, there is not an ideal algorithm that can competitively solve all problems. Under such conditions, our study analyzes the performance of five different metaheuristic techniques for solving the problem of TM. In the comparison, several complicated computer experiments have been conducted considering different performance indexes. The results show that the proposed technique solves the problem of TM competitively for rotated instances in terms of accuracy and robustness. [ABSTRACT FROM AUTHOR]