An Optimal Steering Vector Generation Using Chaotic Binary Crow Search Algorithm for MIMO System

2021 ◽  
pp. 423-438
Author(s):  
P. Sekhar Babu ◽  
P. V. Naganjaneyulu ◽  
K. Satya Prasad
Keyword(s):  
Search Algorithm ◽  
Mimo System ◽  
Steering Vector ◽  
Vector Generation

A Comparison among Multi-Agent Stochastic Optimization Algorithms for State Feedback Regulator Design of a Twin Rotor MIMO System

2016 ◽  
pp. 409-448 ◽  
Author(s):  
Kaushik Das Sharma
Keyword(s):  
Stochastic Optimization ◽  
State Feedback ◽  
Search Algorithm ◽  
Mimo System ◽  
Single Agent ◽  
The State ◽  
Multi Agent ◽  
Twin Rotor Mimo System ◽  
Twin Rotor ◽  
Feedback Regulator

Multi-agent optimization or population based search techniques are increasingly become popular compared to its single-agent counterpart. The single-agent gradient based search algorithms are very prone to be trapped in local optima and also the computational cost is higher. Multi-Agent Stochastic Optimization (MASO) algorithms are much powerful to overcome most of the drawbacks. This chapter presents a comparison of five MASO algorithms, namely genetic algorithm, particle swarm optimization, differential evolution, harmony search algorithm, and gravitational search algorithm. These MASO algorithms are utilized here to design the state feedback regulator for a Twin Rotor MIMO System (TRMS). TRMS is a multi-modal process and the design of its state feedback regulator is quite difficult using conventional methods available. MASO algorithms are typically suitable for such complex process optimizations. The performances of different MASO algorithms are presented and discussed in light of designing the state regulator for TRMS.

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