Parameters identification of unknown delayed genetic regulatory networks by a switching particle swarm optimization algorithm

Abstract

This paper presents a novel particle swarm optimization (PSO) algorithm based on Markov chains and competitive penalized method. Such an algorithm is developed to solve global optimization problems with applications in identifying unknown parameters of a class of genetic regulatory networks (GRNs). By using a evolutionary factor, a new switching PSO (SPSO) algorithm is first proposed and analyzed, where the velocity updating equation jumps from one mode to another according to a Markov chain, and acceleration coefficients are dependent on mode switching. Furthermore, a leader competitive penalized multilearning approach (LCPMLA) is introduced to improve the global search ability and refine the convergent solutions. The LCPMLA can automatically choose search strategy using a learning and penalizing mechanism. The presented SPSO algorithm is compared with some well-known PSO algorithms in the experiments. It is shown that the SPSO algorithm has faster local convergence speed, higher accuracy and algorithm reliability, resulting in better balance between the global and local searching of the algorithm, and thus generating good performance. Finally, we utilize the presented SPSO algorithm to identify not only the unknown parameters but also the coupling topology and time-delay of a class of GRNs.

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

“…In the last few years, our group has achieved many noteworthy results in improving the PSO algorithms [107][108][109][110]. Compared with traditional PSO algorithms, our modified PSO algorithms have better performances in many aspects.…”

Effect of Leakage Delay on Stability of Neutral-Type Genetic Regulatory Networks

“…Up to now, several models of GRNs have been established, for example, Boolean network models [5], Bayesian network models [6], Petri network models [7,8], and the differential equation models. It is more convenient to analyze the dynamical behaviors by using differential equation models and have been widely studied by many experts [9][10][11][12].…”

Image-Based Quantitative Analysis of Gold Immunochromatographic Strip via Cellular Neural Network Approach

“…The PSO algorithm developed by Kennedy and Eberhart [12] stimulates the social behaviors of birds blocking or fish schooling, etc. It has been successfully applied in a variety of fields due to its effectiveness in performing difficult optimization tasks and its convenience for implementation with fast convergence to a reasonably good solution [12,26,29]. In [29], a switching PSO algorithm has been developed that introduces a mode-dependent velocity updating equation with Markovian switching parameters in order to overcome the contradiction between the local search and global search.…”

“…It has been successfully applied in a variety of fields due to its effectiveness in performing difficult optimization tasks and its convenience for implementation with fast convergence to a reasonably good solution [12,26,29]. In [29], a switching PSO algorithm has been developed that introduces a mode-dependent velocity updating equation with Markovian switching parameters in order to overcome the contradiction between the local search and global search. The switching PSO algorithm developed in [29] can avoid the local search stagnating in a local area (hence wasting more time on an invalid search), and also lead the swarm move to a more potential area quickly which helps to obtain a global search greatly.…”

“…In [29], a switching PSO algorithm has been developed that introduces a mode-dependent velocity updating equation with Markovian switching parameters in order to overcome the contradiction between the local search and global search. The switching PSO algorithm developed in [29] can avoid the local search stagnating in a local area (hence wasting more time on an invalid search), and also lead the swarm move to a more potential area quickly which helps to obtain a global search greatly. Thus, the main intention of this paper is therefore to optimize the template parameters of CNN by the switching particle swarm optimization (SPSO) algorithm for accurately segmenting the test and control lines of GICS image.…”