AN EFFICIENT BINARY SOCIAL SPIDER ALGORITHM FOR FEATURE SELECTION PROBLEM Abstract   The social spider algorithm (SSA) is a heuristic algorithm created on spider behaviors to solve continuous problems. In this paper, firstly a binary version of the social spider algorithm called binary social spider algorithm (BinSSA) is proposed. Currently, there is insufficient focus on the binary version of SSA in the literature. The main part of the binary version is the transfer function. The transfer function is responsible for mapping continuous search space to binary search space. In this study, eight of the transfer functions divided into two families, Sshaped and Vshaped, are evaluated. BinSSA is obtained from SSA, by transforming constant search space to binary search space with eight different transfer functions (SShapes and VShaped). Thus, eight different variations of BinSSA are formed as BinSSA1, BinSSA2, BinSSA3, BinSSA4, BinSSA5, BinSSA6, BinSSA7, and BinSSA8. For increasing, exploration and exploitation capacity of BinSSA, a crossover operator is added as BinSSACR. In secondly, the performances of BinSSA variations are tested on feature selection task. The optimal subset of features is a challenging problem in the process of feature selection. In this paper, according to different comparison criteria (mean of fitness values, the standard deviation of fitness values, the best of fitness values, the worst of fitness values, accuracy values, the mean number of the selected features, CPU time), the best BinSSA variation is detected. In the feature selection problem, the Knearest neighbor (KNN) and support vector machines (SVM) are used as classifiers. A detailed study is performed for the fixed parameter values used in the fitness function. BinSSA is evaluated on lowscaled, middlescaled and largescaled twentyone wellknown UCI datasets and obtained results are compared with stateofart algorithms in the literature. Obtained results have shown that BinSSA and BinSSACR show superior performance and offer quality and stable solutions.

Cat Swarm Optimization Algorithm  A Survey and Performance Evaluation This paper presents an indepth survey and performance evaluation of the Cat Swarm Optimization (CSO) Algorithm. CSO is a robust and powerful metaheuristic swarmbased optimization approach that has received very positive feedback since its emergence. It has been tackling many optimization problems and many variants of it have been introduced. However, the literature lacks a detailed survey or a performance evaluation in this regard. Therefore, this paper is an attempt to review all these works, including its developments and applications, and group them accordingly. In addition, CSO is tested on 23 classical benchmark functions and 10 modern benchmark functions (CEC 2019). The results are then compared against three novel and powerful optimization algorithms, namely Dragonfly algorithm (DA), Butterfly optimization algorithm (BOA) and Fitness Dependent Optimizer (FDO). These algorithms are then ranked according to Friedman test and the results show that CSO ranks first on the whole. Finally, statistical approaches are employed to further confirm the outperformance of CSO algorithm.