Adaptive-Partitioning-Based Stochastic Optimization Algorithm and Its Application to Fuzzy Control Design

2006 ◽  
pp. 67-76
Author(s):  
Chang-Wook Han ◽  
Jung-Il Park
Keyword(s):  
Fuzzy Control ◽  
Stochastic Optimization ◽  
Optimization Algorithm ◽  
Control Design ◽  
Adaptive Partitioning ◽  
Stochastic Optimization Algorithm

scholarly journals PID2018 Benchmark Challenge: Multi-Objective Stochastic Optimization Algorithm

2018 ◽  
Vol 51 (4) ◽  
pp. 877-881 ◽  
Author(s):  
Abdullah Ates ◽  
Jie Yuan ◽  
Sina Dehghan ◽  
Yang Zhao ◽  
Celaleddin Yeroglu ◽  
...  
Keyword(s):  
Stochastic Optimization ◽  
Optimization Algorithm ◽  
Multi Objective ◽  
Stochastic Optimization Algorithm

A NOVEL OPTIMIZATION ALGORITHM BASED ON ATMOSPHERE CLOUDS MODEL

2013 ◽  
Vol 12 (01) ◽  
pp. 1350002 ◽  
Author(s):  
GAO-WEI YAN ◽  
ZHAN-JU HAO
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Stochastic Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Pso Algorithm ◽  
Natural World ◽  
Model Optimization ◽  
Swarm Optimization ◽  
Stochastic Optimization Algorithm

This paper introduces a novel numerical stochastic optimization algorithm inspired from the behaviors of cloud in the natural world, which is designated as atmosphere clouds model optimization (ACMO) algorithm. It is tried to simulate the generation behavior, move behavior and spread behavior of cloud in a simple way. The ACMO algorithm has been tested on a set of benchmark functions in comparison with two other evolutionary-based algorithms: particle swarm optimization (PSO) algorithm and genetic algorithm (GA). The results demonstrate that the proposed algorithm has certain advantages in solving multimodal functions, while the PSO algorithm has a better result in terms of convergence accuracy. In conclusion, the ACMO algorithm is an effective method in solving optimization problems.

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