The Experimental Analysis on Transfer Function of Binary Particle Swarm Optimization

2021 ◽  
pp. 254-264
Author(s):  
Yixuan Luo ◽  
Jianhua Liu ◽  
Xingsi Xue ◽  
Renyuan Hu ◽  
Zihang Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Transfer Function ◽  
Experimental Analysis ◽  
Particle Swarm ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization

scholarly journals A New Hybrid Particle Swarm Optimization and Greedy for 0-1 Knapsack Problem

2016 ◽  
Vol 1 (3) ◽  
pp. 411 ◽  
Author(s):  
Phuong Hoai Nguyen ◽  
Dong Wang ◽  
Tung Khac Truong
Keyword(s):  
Particle Swarm Optimization ◽  
Transfer Function ◽  
Knapsack Problem ◽  
Binary Code ◽  
Particle Swarm ◽  
Superior Performance ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Greedy Strategy ◽  
Hybrid Particle Swarm Optimization

This paper proposes a new binary particle swarm optimization with a greedy strategy to solve 0-1 knapsack problem. Two constraint handling techniques are consider to cooperation with binary particle swarm optimization that are penalty function and greedy. The sigmoid transfer function is used to convert real code to binary code. The experimental results have proven the superior performance of the proposed algorithm.

scholarly journals Z-Shaped Transfer Functions for Binary Particle Swarm Optimization Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-21
Author(s):  
Sha-sha Guo ◽  
Jie-sheng Wang ◽  
Meng-wei Guo
Keyword(s):  
Particle Swarm Optimization ◽  
Transfer Function ◽  
Transfer Functions ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Search Space ◽  
Binary Particle Swarm Optimization ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Intelligent Searching

Particle swarm optimization (PSO) algorithm is a swarm intelligent searching algorithm based on population that simulates the social behavior of birds, bees, or fish groups. The discrete binary particle swarm optimization (BPSO) algorithm maps the continuous search space to a binary space through a new transfer function, and the update process is designed to switch the position of the particles between 0 and 1 in the binary search space. Aiming at the existed BPSO algorithms which are easy to fall into the local optimum, a new Z-shaped probability transfer function is proposed to map the continuous search space to a binary space. By adopting nine typical benchmark functions, the proposed Z-probability transfer function and the V-shaped and S-shaped transfer functions are used to carry out the performance simulation experiments. The results show that the proposed Z-shaped probability transfer function improves the convergence speed and optimization accuracy of the BPSO algorithm.

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