Boosting salp swarm algorithm by sine cosine algorithm and disrupt operator for feature selection. (1st May 2020) - Neggaz, Nabil; Ewees, Ahmed A.; Elaziz, Mohamed Abd; Mafarja, Majdi
Highlights: Propose a novel FS method, called ISSAFD, which improves Salp Swarm Algorithm (SSA). ISSAFS enhanced the followers in SSA using SCA and Disrupt operator (DO). Evaluating the influence of the operators of SCA on the behavior of leaders in SSA. Comparing the performance of ISSAFD with swarm intelligence (SI). The proposed ISSAFD provided better results in terms of performance measures. Abstract: Features Selection (FS) plays an important role in enhancing the performance of machine learning techniques in terms of accuracy and response time. As FS is known to be an NP-hard problem, the aim of this paper is to introduce basically a new variant of Salp Swarm Optimizer (SSA) for FS (called ISSAFD (Improved Followers of Salp swarm Algorithm using Sine Cosine algorithm and Disrupt Operator), that updates the position of followers (F) in SSA using sinusoidal mathematical functions that were inspired from the Sine Cosine Algorithm (SCA). This enhancement helps to improve the exploration phase and to avoid stagnation in a local area. Moreover, the Disruption Operator ( Dop ) is applied for all solutions, in order to enhance the population diversity and to maintain the balance between exploration and exploitation processes. Two other variants of SSA are developed based on SCA called ISSALD (Improved Leaders of Salp swarm Algorithm using Sine Cosine algorithm and Disrupt Operator) and ISSAF (Improved Followers of Salp swarm Algorithm using Sine Cosine algorithm). The updating process in consists to update the leaders (L) position by SCA and applying ( Dop ), whereas in ISSAF, the Dop is omitted and the position of followers is updated by SCA. Experimental results are evaluated on twenty datasets where four of them represent high dimensionality with a small number of instances. The obtained results show a good performance of ISSAFD in terms of accuracy, sensitivity, specificity, and the number of selected features in comparison with other metaheuristics (MH).