In this paper, different Inertia weight techniques in Particle Swarm Optimization algorithm (PSO) have been compared to search for the optimal PID controller gains for a Continuous Stirred Tank Reactor (CSTR) process. The optimization problem considered is highly nonlinear, complex with multiple objectives, wide operating range and constraints. The efficiency of PSO algorithm lies in the suitab...

In this study, a simple method based on the dynamic equation of motion was introduced to determine the moment of inertia using a commercial dynamometer, and an optimization technique was utilized to estimate inertial parameters with the determined moment of inertia. To evaluate the feasibility of the developed method, three different passive speeds (i.e. 240, 270 and 300°/s) were chosen to conf...

Particle swarm optimization (PSO) is an evolutionary computing method based on intelligent collective behavior of some animals. It is easy to implement and there are few parameters to adjust. The performance of PSO algorithm depends greatly on the appropriate parameter selection strategies for fine tuning its parameters. Inertia weight (IW) is one of PSO's parameters used to bring about a balan...

Limited research has been done on exoskeletons to enable faster movements of the lower extremities. An exoskeleton’s mechanism can actually hinder agility by adding weight, inertia and friction to the legs; compensating inertia through control is particularly difficult due to instability issues. The added inertia will reduce the natural frequency of the legs, probably leading to lower step freq...

Limited research has been done on exoskeletons to enable faster movements of the lower extremities. An exoskeleton’s mechanism can actually hinder agility by adding weight, inertia and friction to the legs; compensating inertia through control is particularly difficult due to instability issues. The added inertia will reduce the natural frequency of the legs, probably leading to lower step freq...

Particle swarm optimization (PSO) is a new evolutionary computation technique. Although PSO algorithm possesses many attractive properties, the methods of selecting inertia weight need to be further investigated. Under this consideration, the inertia weight employing random number uniformly distributed in [0,1] was introduced to improve the performance of PSO algorithm in this work. Three bench...

An efficient oscillating inertia weight of particle swarm optimisation for tracking optima in dynamic environments Javidan Kazemi Kordestani, Alireza Rezvanian & Mohammad Reza Meybodi To cite this article: Javidan Kazemi Kordestani, Alireza Rezvanian & Mohammad Reza Meybodi (2016) An efficient oscillating inertia weight of particle swarm optimisation for tracking optima in dynamic environments,...

This paper presents a novel improved fuzzy particle swarm optimization (IFPSO) algorithm to the intelligent identification and control of a dynamic system. The proposed algorithm estimates optimally the parameters of system and controller by minimizing the mean of squared errors. The particle swarm optimization is enhanced intelligently by using a fuzzy inertia weight to rationally balance the ...

The particle swarm optimization (PSO) is an algorithm for finding optimal regions of complex search space through interaction of individuals in a population of particles. Search is conducted by moving particles in the space. Some methods area attempted to improve performance of PSO since is founded, including linearly decreasing inertia weight. The present paper proposes a new variation of PSO ...

This paper reviews recent literature on the mechanisms for adapting parameters of particle swarm optimization (PSO) algorithm. The review covers the mechanisms for adaptively setting such parameters as inertia weight, acceleration constants, number of particles and number of iterations. In additions, a more thorough review of the mechanisms for adapting inertia weight and acceleration constants...