Coordinated controller tuning of a boiler turbine unit with new binary particle swarm optimization algorithm

Abstract

Coordinated controller tuning of the boiler turbine unit is a challenging task due to the nonlinear and coupling characteristics of the system. In this paper, a new variant of binary particle swarm optimization (PSO) algorithm, called probability based binary PSO (PBPSO), is presented to tune the parameters of a coordinated controller. The simulation results show that PBPSO can effectively optimize the control parameters and achieves better control performance than those based on standard discrete binary PSO, modified binary PSO, and standard continuous PSO.

Freight transport network design using particle swarm optimisation in supply chain–transport supernetwork equilibrium

“…The existing research explores the several important parameters and their proper values, including x, vel min , vel max , prob min , prob max , h 1 , h 2 , s 1 and s 2 (Kennedy and Eberhart, 1997;Shi and Eberhart, 1998;Salman et al, 2002;Wang et al, 2008;Menhas et al, 2011Menhas et al, , 2012. The subsequent testing to determine the best parameter values of the PSO-based procedures is undertaken on the basis of these findings.…”

“…Due to this superiority, the paper focuses on PSOs among a lot of heuristic techniques. Menhas et al (2012) developed a probability-based discrete binary PSO (PBPSO) (see also Wang et al (2008) and Menhas et al (2011) for PBPSO) to solve single-objective combinatorial optimisation problems. They indicate that PBPSO can offer a better performance than DBPSO and MBPSO in terms of quality and stability of solutions.…”

Set-based particle swarm optimization applied to the multidimensional knapsack problem

“…The parameters R min and R max used in the linear transformation are usually chosen such that R max > 0 and R min = −R max . (Menhas et al, 2011) extended the PBPSO algorithm to also include a mutation operator. After application of the linear transformation in equation (8), each bit was given a probability p mut ∈ [0, 1] of mutating, resulting in the position update,…”

Particle Swarm Algorithm variants for the Quadratic Assignment Problems - A probabilistic learning approach

“…In the binary version of PSO, the concept of the velocity is redefined to reflect the particle's probability of changing state. Several variants of the binary PSO were proposed, they differ mainly in the way new positions are created from the velocity (Wang et al, 2008;Menhas et al, 2011;Shen et al, 2014). In another method to discretize PSO, a rank-based approach was used (Liu et al, 2007).…”