A new algorithm inspired in the behavior of the social-spider for constrained optimization
In this paper, a novel swarm algorithm called the Social Spider Optimization (SSO-C) is proposed for solving constrained optimization tasks. In the proposed method, individuals emulate a group of spiders which interact to each other based on the biological laws of the cooperative colony. For constraint handling, the approach incorporates the combination of two different paradigms: a penalty function and a feasibility criterion. Comparisons based on several well-studied benchmarks functions demonstrate the effectiveness, efficiency and stability of the proposed method.
constrained optimization swarm algorithms bio-inspired algorithms