Shahdan Sudin

A Particle Swarm Optimization Approach for Routing in VLSI

The performance of very large scale integration (VLSI) circuits is depends on the interconnected routing in the circuits. In VLSI routing, wire sizing, buffer sizing, and buffer insertion are techniques to improve power dissipation, area usage, noise, crosstalk, and time delay. Without considering buffer insertion, the shortest path in routing is assumed having the minimum delay and better performance. However, the interconnect delay can be further improved if buffers are inserted at proper locations along the routing path. Hence, this paper proposes a heuristic technique to simultaneously find the optimal routing path and buffer location for minimal interconnect delay in VLSI based on particle swarm optimization (PSO). PSO is a robust stochastic optimization technique based on the movement and information sharing of swarms. In this study, location of doglegs is employed to model the particles that represent the routing solutions in VLSI. The proposed approach has a good potential in VLSI routing and can be further extended in futureTo seek for a hyperchaotic attractor with complex topological attractor structure, a new four-dimensional continuous autonomous hyperchaotic system is proposed. Within a wider region of the variation of the control parameter, this system can generate novel hperchaotic and chaotic attractors along with quasi-periodic and periodic orbits. By employing Lyapunov exponent spectrum, bifurcation diagram, Poincaré mapping and phase portrait, etc., the existence of hyperchaotic behaviors of new system is verified and the dynamical routes from period, quasi-period, chaos and hyperchaos are observed. Furthermore, a practical circuit is designed to realize the system, which the experimental results indicate that new four-dimensional hyperchaotic system is a realizable chaotic system with potential values of engineering applications.

Implementation of an ant colony system for DNA sequence optimization

DNA computation exploits the computational power inherent in molecules for information processing. However, in order to perform the computation correctly, a set of good DNA sequences is crucial. A lot of work has been carried out on designing good DNA sequences to archive a reliable molecular computation. In this article, the ant colony system (ACS) is introduced as a new tool for DNA sequence design. In this approach, the DNA sequence design is modeled as a path-finding problem, which consists of four nodes, to enable the implementation of the ACS. The results of the proposed approach are compared with other methods such as the genetic algorithm.

Exploitation selection of alpha parameter in Gravitational Search Algorithm of PID controller for computational time analysis

In this paper, a modification is done in Gravitational Search Algorithm (GSA) where an alpha parameter (α) is modified. This modification is done in order to provide different gravitational constant, G(t) and acceleration (a) to each agent as trying to improve its performance. Then this modified GSA algorithm is tested to see the effectiveness of PID controller in finding the value of KP, KI and KD of an activated sludge process (ASP) system in terms of computational time. These results are then compared with the computational time of original GSA approach and Particle Swarm Optimization (PSO) to see the effectiveness of the new approach.

Synchronous gravitational search algorithm vs asynchronous gravitational search algorithm: a statistical analysis

Gravitational search algorithm (GSA) is a new member of swarm intelligence algorithms. It stems from Newtonian law of gravity and motion. The performance of synchronous GSA (S-GSA) and asynchronous GSA (A-GSA) is studied here using statistical analysis. The agents in S-GSA are updated synchronously, where the whole population is updated after each member’s performance is evaluated. On the other hand, an agent in A-GSA is updated immediately after its performance evaluation. Hence an agent in A-GSA is updated without the need to synchronize with the entire population. Asynchronous update is more attractive from the perspective of parallelization. The results show that both implementations have similar performance.

Modelling and control of MacPherson active suspension system using Composite Nonlinear Feedback under chassis twisted road condition

A ride quality test for active suspension system is real significant in modern automotive suspension control performance validation for ride comfort evaluation. The Composite Nonlinear Feedback (CNF) controller is proposed as a control strategy to reduce the high overshoot on the transient response and fast settling time. The mathematical modeling of a MacPherson quarter car suspension is utilized in this paper. A chassis twists road procedure in ride quality test has been applied. The multi-body dynamics system software so-called CarSim is used for validation. The strength of the proposed control scheme is shown by numerical experiment results and numerical simulation results.

Investigation on Different Kernel Functions for Weighted Kernel Regression in Solving Small Sample Problems

Previously, weighted kernel regression (WKR) has proved to solve small problems. The existing WKR has been successfully solved rational functions with very few samples. The design and development of WKR is important in order to extend the capability of the technique with various kernel functions. Based on WKR, a simple iteration technique is employed to estimate the weight parameters with Gaussian as a kernel function before WKR can be used in predicting the unseen test samples. In this paper, however, we investigate various kernel functions with Particle Swarm Optimization (PSO) as weight estimators as it offers such flexibility in defining the objective function. Hence, PSO has the capability to solve non-closed form solution problem as we also introduce regularization term with L1 norm in defining the objective function as to solve training sample, which corrupted by noise. Through a number of computational experiments, the investigation results show that the prediction quality of WKR is primarily dominated by the smoothing parameter selection rather than the type of kernel function.

Novel Information Flow Topology for Vehicle Convoy Control

This paper analyzed a novel information flow topology (IFT) for vehicle convoy. The topology used two-vehicle look-ahead with an immediate rear-vehicle inclusive. Mass spring damper and Newton’s second law were utilized to provide the behavior and basics for the vehicles motion respectively. The concept of homogeneous vehicle convoy and constant headway time (CHT) policy was in cooperated for the inter-vehicular spacing. The new IFT was compared with the conventional topology of the two-vehicle look-ahead to ascertain its improvement. The novel topology provides good inter-vehicular space of 0.42 m ahead of the conventional topology. Moreover, the proposed topology obeys the rate of change of speed throughout the vehicles journey than the conventional type. Low jerk of \( 0.44\,{\text{ms}}^{ - 3} \) was achieved against \( 0.47\,{\text{ms}}^{ - 3} \) of the conventional. Finally, the new topology is visible throughout the journey than the earlier, which discontinues after 117 s in all parameters.

The Effect of Matrix C in Sliding Mode Control with Composite Nonlinear Feedback Control Strategy in MacPherson Active Suspension System

The C matrix in Sliding Mode Control (SMC) is significant to the control performance in MacPherson active suspension system. The SMC was combined with Composite Nonlinear Feedback (CNF) controller due to its characteristics on the transient response and fast settling time. The Neural Network is used to determine the matrix of C based on the road profiles used in this research work. The Proportional Integral (PI) was combined with SMC to overcome the uncertainties, unmatched condition and steady state error occurred in the MacPherson active suspension system. The three road profiles have been applied to this research work. The multi-body dynamics system software called CarSim is used for validation. The numerical experiment results are shown the effect of the C matrix in SMC with CNF controller performance in acceleration of sprung mass.

A control performance analysis for MacPherson active suspension system under bounce sine sweep road profile

This paper studies a control performance analysis for MacPherson active suspension system. The ride comfort quality is a very important specification for modern automotive suspension system. The Proportional Integral Sliding Mode Control-Evolutionary Strategy-Composite Nonlinear Feedback (PISMC-ES-CNF) controller is designed to solve the transient problem occurred in vertical acceleration of sprung mass. The control performance is tested by using PISMC-ES-CNF and compared with Sliding Mode Controller (SMC) and Composite Nonlinear Feedback (CNF) under Bounce Sine Sweep road profile. The ISO 2631-1, 1997 is a standard for vertical acceleration of sprung mass level and degree of comfort. The one way Analysis of Variance (ANOVA) and standard deviation have showed that the PISMC– ES-CNF controller compared with others controllers achieved the best control performance.

Simultaneous Computation of Model Order and Parameter Estimation for System Identification Based on Gravitational Search Algorithm

System identification is a technique used to obtain a mathematical model of a system by performing analysis of input-output characteristic of the system. Most significant steps of system identification process are generally summarized into four main stages. The initial stage is collection of experimental data. After that, the model order and structure are selected. The next stage is to approximate the parameters of the model and finally, the mathematical model is validated. In this paper, a technique termed as Simultaneous Model Order and Parameter Estimation (SMOPE), which is specifically based on Gravitational Search Algorithm (GSA) is proposed to combine model order selection and parameter estimation in one process. Both the model order and the parameters of the system are estimated simultaneously to attain the best mathematical model of a system. From the simulation, it is proven that the proposed method can be an alternative technique for solving the system identification problem.