A Hybrid Particle Swarm Optimization and Artificial Bee Colony Algorithm for Image Contrast Enhancement

2018 ◽  
pp. 277-285
Author(s):  
Saorabh Kumar Mondal ◽  
Arpitam Chatterjee ◽  
Bipan Tudu
Keyword(s):  
Particle Swarm Optimization ◽  
Contrast Enhancement ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Image Contrast ◽  
Swarm Optimization ◽  
Hybrid Particle ◽  
Bee Colony ◽  
Hybrid Particle Swarm Optimization ◽  
Image Contrast Enhancement

Dynamic Function Optimization by Improved Artificial Bee Colony Algorithm

2014 ◽  
Vol 556-562 ◽  
pp. 3562-3566 ◽  
Author(s):  
Shuo Jiang
Keyword(s):  
Particle Swarm Optimization ◽  
Greedy Algorithm ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Dynamic Environment ◽  
Particle Swarm ◽  
Function Optimization ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Dynamic Function

In this paper, an improved artificial bee colony algorithm (IABC) for dynamic environment optimization has been proposed. As we compared the IABC with greedy algorithm (GA), Particle swarm optimization (PSO) and original artificial bee colony algorithm (ABC), the result of dynamic function optimization shows that the IABC can obtain satisfactory solutions and good tracing performance for dynamic function in time.

Infrared Image Contrast Enhancement Based on Modified Particle Swarm Optimization

2013 ◽  
Vol 760-762 ◽  
pp. 1389-1393
Author(s):  
Ren Tao Zhao ◽  
You Yu Wang ◽  
Hua De Li ◽  
Jun Tie
Keyword(s):  
Particle Swarm Optimization ◽  
Contrast Enhancement ◽  
Particle Swarm ◽  
Infrared Image ◽  
Optimal Solution ◽  
Image Contrast ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Image Contrast Enhancement ◽  
Modified Pso

Adaptive infrared image contrast enhancement is presented based on modified particle swarm optimization (PSO) and incomplete Beta Function. On the basis of traditional PSO, modified PSO integrates into the theory of Multi-Particle Swarm and evolution theory algorithm. By using separate search space optimal solution of multiple particles, the global search ability is improved. And in the iteration procedures, timely adjustment of acceleration coefficients is convenient for PSO to find the global optimal solution in the later iteration. Through infrared image simulation, experimental results show that the modified PSO is better than the standard PSO in computing speed and convergence.

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