A New Hybrid Seagull Optimization Algorithm for Feature Selection

Abstract

Hybrid algorithms have attracted more and more attention in the field of optimization algorithms. In this paper, three hybrid algorithms are proposed to solve feature selection problems based on seagull optimization algorithm (SOA) and thermal exchange optimization (TEO). In the first algorithm, we take the roulette wheel to choose one of the two algorithms for located updating. Another method is to join the TEO algorithm for optimization after SOA algorithm iteration. The last method is to adopt TEO algorithm's heat exchange formula to improve the seagull attack mode of SOA algorithm, so as to improve the exploitation ability of SOA algorithm. The performance of proposed methods is evaluated on 20 standard benchmark datasets in the UCI repository and compared with three well-known hybrid optimization feature selection methods in the literature. The experimental results illustrate that the proposed algorithm has high efficiency in improving classification accuracy, ensuring the ability of hybrid SOA algorithm in feature selection and classification task information attribute selection, and reducing the CPU time.

A Hybrid Whale Optimization Algorithm for Global Optimization

“…In this subsection, to further evaluate the GWOA-TEO algorithm's performance, a comparison is performed with other state-of-the-art feature selection algorithms. Some brief descriptions of state-of-the-art feature selection algorithms are as follows: the hybrid seagull optimization algorithm (SOA) and thermal exchange optimization (TEO) are based on improved local searchability, named SOA-TEO3 [44]; the heap-based optimizer is based on the corporate rank hierarchy (CRH) principle, named HBO [41]; the binary version of the ant lion optimizer is based on crossover operation and mutation operation to improve the local searchability, named BALO-1 [45]; the high-level relay hybrid (HRH) model for whale optimization algorithm and simulated annealing (SA) is based on tournament selection to maintain the diversity of the population, named WOASAT-2 [46].…”

Intelligence Bearing Fault Diagnosis Model Using Multiple Feature Extraction and Binary Particle Swarm Optimization With Extended Memory

“…In order to an objective assessment of the ability of the proposed approach in the feature selection problem, a comparison between BPSO-EM and the results cited from the state-of-art feature selection algorithms which represent recent research directions on this problem has been carried out. Brief description of state-of-art feature subset selection algorithms is as follows: 1) A hybrid feature selection method is proposed seagull optimization algorithm (SOA) and thermal exchange optimization (TEO) is named SOA- TEO3 [45]; 2) An approach to improve the PSO algorithm on the update mechanism and in conjunction with the spiral mechanism to enhance local search around the optimal solution called HPSO-SSM [46]; 3) BGOA-M proposed in [47] is based on a combination of grasshopper optimization algorithm and mutation operator to enhance the exploration ability of the algorithm; 4) A binary variant of the butterfly optimization algorithm (BOA) proposed in [48] to improve the exploration ability in the feature search space, which is called S-bBOA; 5) A hybrid algorithm based on a combination of the whale optimization algorithm (WOA) and the simulated annealing algorithm (SA) called WOASAT-2, which is proposed to enhance exploitation ability by searching the optimal solution in a promising area [49]. All five optimization algorithms are categorized as swarm intelligent algorithms.…”

A Hybrid Whale Optimization with Seagull Algorithm for Global Optimization Problems

“…Jiang et al studied a hybrid classification method based on the oppositional seagull optimization algorithm [25]. Jia et al proposed a new hybrid seagull optimization algorithm for feature selection [26]. Dhiman et al studied Emo SOA: a new evolutionary multitarget gull global optimization algorithm [27].…”