Swarm intelligence (SI) is a research field which has recently attracted the attention of several scientific communities. An SI approach tries to characterize the collective behavior of animal or insect groups to build a search strategy. These methods consider biological systems, which can be modeled as optimization processes to a certain extent. The Social Spider Optimization (SSO) is a novel swarm algorithm that is based on the cooperative characteristics of the social spider. In SSO, search agents represent a set of spiders which collectively move according to the biological behavior of the colony. In most of SI algorithms, all individuals are modeled considering the same properties and behavior. In contrast, SSO defines two different search agents: male and female. Therefore, according to the gender, each individual is conducted by using a different evolutionary operation which emulates its biological role in the colony. This individual categorization allows reducing critical flaws present in several SI approaches such as incorrect exploration-exploitation balance and premature convergence. After its introduction, SSO has been modified and applied in several engineering domains. In this paper, the state of the art, improvements, and applications of the SSO are reviewed.

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One crucial step in several image processing and computer vision applications is Image Contrast Enhancement (ICE), whose main objective is to improve the quality of the information contained in the processed images. Most of the proposed schemes attack the problem by redistributing the pixel intensities in a histogram, leading to undesirable effects such as noise amplification, over-saturation, and lousy human perception. On the other hand, Agent-Based Models (ABM) are computational models that allow describing the behavior and interactions of autonomous agents when they operate cooperatively. These agents follow behavioral rules rather than mathematical formulations. This mechanism allows the implementation of complex behavioral patterns in agents through their interactions. This paper proposes a two-step method where pixels in the processed image are considered agents whose behavioral rules permit to enhance significatively the contrast. In our approach, the interactions among the agents are characterized by the differences in intensity values among the pixels or agents. In the first step, pixels or agents that present enough high differences in their intensity are modified to increase even more their differences. In the second step, pixels or agents that maintain a very small difference are altered to assume a homogeneous intensity value. The proposed approach has been tested considering different public datasets commonly used in the literature. Its results are also compared with those produced by other well-known ICE techniques. Evaluation of the experimental results demonstrates that the proposed approach highlights the important details of the image taking a lower computational execution time.INDEX TERMS Agent-based modeling, algorithms, complex systems, image contrast enhancement, image processing.

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Recently, several new metaheuristic schemes have been introduced in the literature. Although all these approaches consider very different phenomena as metaphors, the search patterns used to explore the search space are very similar. On the other hand, second-order systems are models that present different temporal behaviors depending on the value of their parameters. Such temporal behaviors can be conceived as search patterns with multiple behaviors and simple configurations. In this paper, a set of new search patterns are introduced to explore the search space efficiently. They emulate the response of a second-order system. The proposed set of search patterns have been integrated as a complete search strategy, called Second-Order Algorithm (SOA), to obtain the global solution of complex optimization problems. To analyze the performance of the proposed scheme, it has been compared in a set of representative optimization problems, including multimodal, unimodal, and hybrid benchmark formulations. Numerical results demonstrate that the proposed SOA method exhibits remarkable performance in terms of accuracy and high convergence rates.