Inertia weight is a most important parameter of particle swarm optimization (PSO), which can keep a right balance between the global search and local search. In this paper, a novel PSO with non-linear inertia weight based on the tangent function is provided. The paper also presents the method of determining a control parameter in our proposed method, saving the user from a tedious trial and err...

The addition of inertia to exercising astronauts could increase ground reaction forces and potentially provide a greater health benefit. However, conflicting results have been reported regarding the adaptations to additional mass (inertia) without additional net weight (gravitational force) during locomotion. We examined the effect of increasing inertia while maintaining net gravitational force...

Particle swarm optimization(PSO) algorithm has the advantages of simplicity and easy implementation, but it exits the weaknesses of the being easy to fall into local minimum and premature convergence. In order to overcome these weaknesses of PSO algorithm, the inertia weight and learning factor are improved and the PSO algorithm is initialized by using chaotic optimization in order to propose a...

This paper proposes a modified particle swarm optimization algorithm with dynamic adaptation. In this algorithm, a modified velocity updating formula of the particle is used, where the randomness in the course of updating particle velocity is relatively decreased and the inertia weight of each particle is different. Moreover, this algorithm introduces two parameter describing the evolving state...

this paper presents designing an optimal adaptive controller for tracking control of robot manipulators based on particle swarm optimization (pso) algorithm. pso algorithm has been employed to optimize parameters of the controller and hence to minimize the integral square of errors (ise) as a performance criteria. in this paper, an improved pso using logic is proposed to increase the convergenc...