Pérez-Cisneros, M. A swarm optimization algorithm inspired in the behavior of the social-spider, AbstractSwarm intelligence is a research field that models the collective behavior in swarms of insects or animals. Several algorithms arising from such models have been proposed to solve a wide range of complex optimization problems. In this paper, a novel swarm algorithm called the Social Spider Optimization (SSO) is proposed for solving optimization tasks. The SSO algorithm is based on the simulation of cooperative behavior of social-spiders. In the proposed algorithm, individuals emulate a group of spiders which interact to each other based on the biological laws of the cooperative colony. The algorithm considers two different search agents (spiders): males and females. Depending on gender, each individual is conducted by a set of different evolutionary operators which mimic different cooperative behaviors that are typically found in the colony. In order to illustrate the proficiency and robustness of the proposed approach, it is compared to other well-known evolutionary methods. The comparison examines several standard benchmark functions that are commonly considered within the literature of evolutionary algorithms. The outcome shows a high performance of the proposed method for searching a global optimum with several benchmark functions. Please cite this article as: Cuevas, E., Cienfuegos, M., Zaldívar, D., Pérez-Cisneros, M. A swarm optimization algorithm inspired in the behavior of the social-spider, Expert Systems with Applications, 40 (16), (2013), pp. 6374-6384 Please cite this article as: Cuevas, E., Cienfuegos, M., Zaldívar, D., Pérez-Cisneros, M. A swarm optimization algorithm inspired in the behavior of the social-spider, Expert Systems with Applications, 40 (16), (2013), pp. 6374-6384 Please cite this article as: Cuevas, E., Cienfuegos, M., Zaldívar, D., Pérez-Cisneros, M. A swarm optimization algorithm inspired in the behavior of the social-spider, Expert Systems with Applications, 40 (16), (2013), pp. 6374-6384   1,2, , max ( ( )) k kN best J   S s and   1,2, , min ( ( )) k kN worst J   S s (4) Please cite this article as: Cuevas, E., Cienfuegos, M., Zaldívar, D., Pérez-Cisneros, M. A swarm optimization algorithm inspired in the behavior of the social-spider, Expert Systems with Applications, 40 (16), (2013), pp. 6374-6384 Please cite this article as: Cuevas, E., Cienfuegos, M., Zaldívar, D., Pérez-Cisneros, M. A swarm optimization algorithm inspired in the behavior of the social-spider, Expert Systems with Applications, 40 (16), (2013), pp. 6374-6384 17 PSO and ABC algorithms, such as the premature convergence and the incorrect exploration-exploitation balance.SSO has been experimentally tested considering a suite of 19 benchmark functions. The performance of SSO has been also compared to the following swarm algorithms: the Particle Swarm Optimization method (PSO) [16], and the Artificial Bee Colony (ABC) algorithm [38]. Results have confirmed a acceptable performance of the proposed method ...

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This study investigated the structure of sensorimotor representations during goal-directed grasping actions and explored their relationship with object features. Sixteen 3D-printed spheres that varied in size (i.e., a diameter of 20 mm, 40 mm, 60 mm, 80 mm) and weight (i.e., 40 g, 52 g, 76 g, 91 g) were used as experimental stimuli. The Structural Dimensional Analysis of Mental Representation (SDA-M) method was used to assess the sensorimotor representation structure during grasping. Participants were instructed in each trial to weigh, lift, or transport sets of two different spheres and to judge the similarity of the objects’ features, taking into account the executed grasping movement. Each participant performed a total of 240 trials, and object presentation was randomized. The results suggest that the functional interplay between object features and goal-directed actions accounts for the significant variations in the structure of sensorimotor representations after grasping. Specifically, the relevance of the perceived objects’ size and weight is closely interrelated to the grasping task demands and movement dynamics of the executed action. Our results suggest that distinct sensorimotor representations support individual grasping actions according to top-down influences modulated by motor intentions, functional task demands, and task-relevant object features.

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