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- W4236324717 abstract "In this chapter, a novel swarm algorithm, namely the Social Spider Optimization (SSO) is presented for solving optimization tasks. The SSO algorithm is based on the simulation of the cooperative behavior of social-spiders. In the presented 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 the gender, each individual is conducted by a set of different evolutionary operators which mimic the different cooperative behaviors assumed in the colony. To illustrate the proficiency and robustness of the presented approach, it is compared to other well-known evolutionary methods. The comparison examines several standard benchmark functions which are commonly considered within the literature of evolutionary algorithms. The results and examples show a high performance of the presented method when searching for a global optimum of several benchmark functions." @default.
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- W4236324717 date "2016-01-01" @default.
- W4236324717 modified "2023-10-16" @default.
- W4236324717 title "A Swarm Global Optimization Algorithm Inspired in the Behavior of the Social-Spider" @default.
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- W4236324717 doi "https://doi.org/10.1007/978-3-319-28503-0_2" @default.
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