Differential Evolution (DE) is a widely used optimization algorithm due to its robustness and simplicity as a population search technique. However, it can struggle to balance the trade-off between exploring and exploiting the search space effectively, which is a common issue with evolutionary algorithms. To address this, this article introduces an Evolutionary Game Theory (EGT)-based DE (EGT-DE) to enhance DE's performance. EGT-DE promotes the competition among agents through the exchange of information about the search space, the modification of strategies, and the regulation of exploration and exploitation behaviors. The proposed EGT-DE algorithm was tested against nine well-known algorithms on a set of 30 benchmark functions in 50 dimensions. The Wilcoxon rank-sum test and Friedman test were used to determine the significance of the EGT -DE approach. The results showed that the proposed scheme was effective, with its performance significantly outperforming that of the other algorithms.

This paper proposes an alternative multilevel thresholding (MLT) image segmentation method by improving the behavior of the grasshopper optimization algorithm (GOA). This is achieved by using the operators of the sine-cosine algorithm (SCA) to work in a competitive manner with the operators of traditional GOA. This will lead to enhance the quality of the solutions during the updating process that will affect the convergence of the proposed GOASCA towards the global solution. In addition, the proposed GOASCA aims to minimize the difference between the fuzzy entropy and its opposite fuzzy entropy that is used as a fitness function to evaluate the quality of the solution. This objective function gives the GOASCA to explore the whole search space. To assess the quality of the obtained threshold values by GOASCA, a set of eight images are used which have different characteristics. Moreover, the results of GOASCA are compared with a set of well-known MLT image segmentation approaches, and these results have shown the high quality of GOASCA to segmented the image, as well as, shown that the current objective function provides results better than the traditional fuzzy entropy in terms of the performance measures of image segmentation.

Evolutionary Computation (EC) algorithms have been proposed as stochastic methods to solve complex optimization problems. The design of EC methods typically involves the construction of empirical operators based on abstractions of animal behaviors or physical and biological phenomena. Through its operators, every EC approach proposes a particular solution to the exploration-exploitation balance which is currently considered an unsolved problem within EC literature. On the other hand, multi-agent systems have been utilized as intelligent, cooperative and self-organized structures where the synergy of simple rules creates complex interactions among agents. In this paper, a novel EC algorithm called Neighborhood-based Consensus for Continuous Optimization (NCCO) is presented. NCCO is based on typical processes present in multi-agent systems, such as local consensus formulations and reactive responses. These operations are conducted by using appropriate operators that are applied in each evolutionary stage. A traditional EC algorithm considers in its operation the application of every operator without examining its final impact in the searching process. In contrast to other EC techniques, the proposed method uses additional operators to avoid the undesirable effects produced by the over-exploitation or suboptimal exploration of conventional operations. In order to illustrate the performance and accuracy of the proposed NCCO approach, it is compared to several well-known, state-of-the-art algorithms over a set of benchmark functions and real-world design applications. The experimental results demonstrate that NCCO's performance is superior to the test algorithms.

The authors propose two equations based on the pixel geometry and connectivity properties, which can be used to compute, efficiently, the Euler number of a binary digital image with either thick or thin boundaries. Although computing this feature, the authors’ technique extracts the underlying topological information provided by the shape pixels of the given image. The correctness of computing the Euler number using the new equations is also established theoretically. The performance of the proposed method is compared against other available alternatives. Experimental results on a large image database demonstrate that the authors technique for computing the Euler number outperforms the earlier approaches significantly in terms of the number of basic arithmetic operations needed per pixel. Both equations are specialised only for 4‐connectivity cases.

In this article, we propose an objective method to evaluate stroke-width measures. With this aim, we discuss the relevance of features based on the stroke width for document analysis. Then, we point out that most of the consulted references have a vague definition of stroke width. Because of this, we propose a formal definition of the stroke-width and remark the linearity of the stroke-width as an important property. Inspired by these ideas, we propose a measure together with a dataset to evaluate the linearity of the measurements of the stroke width and conduct an evaluation for seven well-known stroke-width methods. Our experiments have interesting results, like the fact that the most popular method is the one with the worst performance and that the best method is the easiest to implement. We hope that our objective evaluation assists further authors to choose suitable stroke-width methods for their applications.

We use a neurofuzzy approach, the NEFCON model, to generate and optimize a fuzzy controller for real-time control of an underactuated robot: the Pendubot, which consists of a two link inverted pendulum actuated only at the first join. The NEFCON learning algorithm is able to learn fuzzy rules as well as fuzzy sets. We present the results of the learning process for a fuzzy controller to balance the Pendubot in its highest inverted position, simulation results, and real-time results. The extension of this work to include the learning process of a swing-up procedure is in progress.