scholarly journals An hybrid particle swarm optimization with crow search algorithm for feature selection

2021 ◽  
pp. 100108
Author(s):  
Abdulhameed Adamu ◽  
Mohammed Abdullahi ◽  
Sahalu Balarabe Junaidu ◽  
Hassan Ibrahim Hayatu
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Hybrid Particle ◽  
Hybrid Particle Swarm Optimization

scholarly journals Software Defects Prediction Based on Hybrid Particle Swarm Optimization and Sparrow Search Algorithm

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 60865-60879
Author(s):  
Liu Yang ◽  
Zhen Li ◽  
Dongsheng Wang ◽  
Hong Miao ◽  
Zhaobin Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Software Defects ◽  
Swarm Optimization ◽  
Hybrid Particle ◽  
Hybrid Particle Swarm Optimization

scholarly journals Sequential Hybrid Particle Swarm Optimization and Gravitational Search Algorithm with Dependent Random Coefficients

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Shanhe Jiang ◽  
Chaolong Zhang ◽  
Shijun Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Particle Swarm ◽  
Random Coefficients ◽  
Superior Performance ◽  
Swarm Optimization ◽  
Hybrid Particle ◽  
Hybrid Particle Swarm Optimization ◽  
Gravitational Search

Particle swarm optimization (PSO) has been proven to show good performance for solving various optimization problems. However, it tends to suffer from premature stagnation and loses exploration ability in the later evolution period when solving complex problems. This paper presents a sequential hybrid particle swarm optimization and gravitational search algorithm with dependent random coefficients called HPSO-GSA, which first incorporates the gravitational search algorithm (GSA) with the PSO by means of a sequential operating mode and then adopts three learning strategies in the hybridization process to overcome the aforementioned problem. Specifically, the particles in the HPSO-GSA enter into the PSO stage and update their velocities by adopting the dependent random coefficients strategy to enhance the exploration ability. Then, the GSA is incorporated into the PSO by using fixed iteration interval cycle or adaptive evolution stagnation cycle strategies when the swarm drops into local optimum and fails to improve their fitness. To evaluate the effectiveness and feasibility of the proposed HPSO-GSA, the simulations were conducted on benchmark test functions. The results reveal that the HPSO-GSA exhibits superior performance in terms of accuracy, reliability, and efficiency compared to PSO, GSA, and other recently developed hybrid variants.

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