A new binary grasshopper optimization algorithm for feature selection problem

An effective solution to numerical and multi-disciplinary design optimization problems using chaotic slime mold algorithm

“…The result reveals that the parameters of the optimal function can be achieved for balancing the power performance and provides economic operation 10 Chaotic harmony search algorithm [ 78 ] 2019 In the research, properties such as uniform distribution to generate random numbers, employing virtual harmony memories, and dynamically tuning the algorithm parameters are explored. Combined economic emission dispatch problems were analyzed for Six test systems having 6, 10, 13, 14, 40, and 140 units 11 Binary grasshopper optimization algorithm [ 79 ] 2019 This paper presents binary grasshopper algorithm and comparative results of five well-known swarm-based algorithms used in feature selection problems for 20 data sets with various sizes 12 Chaotic dragonfly algorithm [ 80 ] 2019 In this paper, the Chaotic Dragonfly Algorithm using ten chaotic maps were implemented by adjusting the main parameters of dragonflies’ activities to increase the convergence rate and enhance the competence of DA 13 Modified dolphin swarm algorithm [ 81 ] 2019 In this paper, chaotic mapping was incorporated with DSA. Rastrigin function with an optimal chaotic map was explored among eight chaotic maps.…”

Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019)

“…Twentytwo benchmark datasets were considered for evaluating the performance of the proposed approach. Hichem et al [210] introduced a new transfer function Hamming distance which converted continuous variables into a binary vector. The new version of GOA (NBGOA) utilized for 20 standard datasets and compared with other versions of GOA.…”

Grasshopper Optimization Algorithm: Theory, Variants, and Applications

“…Hichem et al [79] proposed a Novel Binary GOA (NBGOA) for solving the feature selection problem. The effectiveness of NBGOA was evaluated using 20 datasets with various sizes taken from the UCI datasets repository in comparison with five well-regarded optimization techniques in the feature selection field.…”

“…The effectiveness of NBGOA was evaluated using 20 datasets with various sizes taken from the UCI datasets repository in comparison with five well-regarded optimization techniques in the feature selection field. Simulation results revealed that BGOA [78] NBGOA [79] BGOA [67] ECGOAs [80] LMGOA [81] ECGOA [82] CGOA [83] CGOA [84] SFECGOAs [85] OLCGOA [86] ECGOAs [87] ECAGOA [88] IGOA [70] EGOA [89] PGOA [90] LGOA [91] IGOA [92] AGOA [93] MI-LFGOA [94] LGOA [95] GOA_EPD [65] DJGOA [96] DQBGOA_MR [97] Fuzzy GOA [98] GO-FLC [99] EGOA-FC [100] AGOA [69] AGOA [101] GHO [102] self-adaptive GOA [103] OGOA [104] OBLGOA [105] IGOA [106] MOGOA [75] MOGOA [76] MOGOA [66] MOGOA [107] MOGOA [108] MOGOA [109] LWSGOA [110] MGOA [111] GOFS [112] PCA-GOA [113] OGOA [114] IGOA [115] Fractional-GOA…”