An adaptive bio-inspired optimisation model based on the foraging behaviour of a social spider. Issue 1 (1st January 2019)

• Record Type: Journal Article
• Authors: Otor, Samera Uga; Akinyemi, Bodunde Odunola; Aladesanmi, Temitope Adegboye; Aderounmu, Ganiyu Adesola; Kamagaté, B.H.
• Editors: Pratap, Saurabh
• Is Part Of: Cogent engineering. Volume 6:Issue 1(2019)
• Publication Date: 2019-01-01
• Subjects: bio-inspired -- optimisation -- social spider -- self-evolving; Engineering -- Periodicals; Technology -- Periodicals; Engineering; Technology; Periodicals; 620
• DOI: 10.1080/23311916.2019.1588681
• Languages: English
• ISSNs: 2331-1916

Abstract

Abstract: Existing bio-inspired models are challenged with premature convergence among others. In this paper, an adaptive social spider colony optimisation model based on the foraging behaviour of social spider was proposed as an optimisation problem. The algorithm mimics the prey capture behaviour of the social spider in which, the spider senses the presence of the prey through vibrations transmitted along the web thread. Spiders are the search agents while the web is the search space of the optimisation problem. The natural or biological phenomenon of vibration was modeled using wave theory while optimisation theory was considered in optimizing the objective function of the optimisation problem. This objective function was considered to be the frequency of vibration of the spiders and the prey as this is the function that enables the spider differentiates the vibration of the prey from that of neighbouring spiders and therefore forages maximally. To address the parameter tuning problem, the search pattern was controlled by the position of the prey for convergence. The proposed model was tested for convergence using several benchmark functions with different characteristics to evaluate its performance and results compared to an existing state of the arts' spider algorithm. Results showed that the proposed model performed better by searching the optimum solution of the benchmark functions used to test the model.