Adaptive dynamic disturbance strategy for differential evolution algorithm

To overcome the problems of slow convergence speed, premature convergence leading to local optimization and parameter constraints when solving high-dimensional multi-modal optimization problems, an adaptive dynamic disturbance strategy for differential evolution algorithm (ADDSDE) is proposed. Firstly, this entails using the chaos mapping strategy to initialize the population to increase population diversity, and secondly, a new weighted mutation operator is designed to weigh and combinemutation strategies of the standard differential evolution (DE). The scaling factor and crossover probability are adaptively adjusted to dynamically balance the global search ability and local exploration ability. Finally, a Gauss perturbation operator is introduced to generate a random disturbance variation, and to accelerate premature individuals to jump out of local optimization. The algorithm runs independently on five benchmark functions 20 times, and the results show that the ADDSDE algorithm has better global optimization search ability, faster convergence speed and higher accuracy and stability compared with other optimization algorithms, which provide assistance insolving high-dimensionaland complex problems in engineering and information science

Intelligent Learning Algorithms for Active Vibration Control

YesThis correspondence presents an investigation into the comparative performance of an active vibration control (AVC) system using a number of intelligent learning algorithms. Recursive least square (RLS), evolutionary genetic algorithms (GAs), general regression neural network (GRNN), and adaptive neuro-fuzzy inference system (ANFIS) algorithms are proposed to develop the mechanisms of an AVC system. The controller is designed on the basis of optimal vibration suppression using a plant model. A simulation platform of a flexible beam system in transverse vibration using a finite difference method is considered to demonstrate the capabilities of the AVC system using RLS, GAs, GRNN, and ANFIS. The simulation model of the AVC system is implemented, tested, and its performance is assessed for the system identification models using the proposed algorithms. Finally, a comparative performance of the algorithms in implementing the model of the AVC system is presented and discussed through a set of experiments

Programmable models of growth and mutation of cancer-cell populations

In this paper we propose a systematic approach to construct mathematical models describing populations of cancer-cells at different stages of disease development. The methodology we propose is based on stochastic Concurrent Constraint Programming, a flexible stochastic modelling language. The methodology is tested on (and partially motivated by) the study of prostate cancer. In particular, we prove how our method is suitable to systematically reconstruct different mathematical models of prostate cancer growth - together with interactions with different kinds of hormone therapy - at different levels of refinement.Comment: In Proceedings CompMod 2011, arXiv:1109.104

Multi-objective power quality optimization of smart grid based on improved differential evolution

In the modern generation, Electric Power has become one of the fundamental needs for humans to survive. This is due to the dependence of continuous availability of power. However, for electric power to be available to the society, it has to pass through a number of complex stages. Through each stage power quality problems are experienced on the grid. Under-voltages and over-voltages are the most common electric problems experienced on the grid, causing industries and business firms losses of Billions of dollars each year. Researchers from different regions are attracted by an idea that will overcome all the electrical issues experienced in the traditional grid using Artificial Intelligence (AI). The idea is said to provide electric power that is sustainable, economical, reliable and efficient to the society based on Evolutionary Algorithms (EAs). The idea is Smart Grid. The research focused on Power Quality Optimization in Smart Grid based on improved Differential Evolution (DE), with the objective functions to minimize voltage swells, counterbalance voltage sags and eliminate voltage surges or spikes, while maximizing the power quality. During Differential Evolution improvement research, elimination of stagnation, better and fast convergence speed were achieved based on modification of DE’s mutation schemes and parameter control selection. DE/Modi/2 and DE/Modi/3 modified mutation schemes proved to be the excellent improvement for DE algorithm by achieving excellent optimization results with regards to convergence speed and elimination of stagnation during simulations. The improved DE was used to optimize Power Quality in smart grid in combination with the reconfigured and modified Dynamic Voltage Restorer (DVR). Excellent convergence results of voltage swells and voltage sags minimization were achieved based on application of multi-objective parallel operation strategy during simulations. MATLAB was used to model the proposed solution and experimental simulations.Electrical and Mining EngineeringM. Tech. (Electrical Engineering

Optimal design of a quadratic parameter varying vehicle suspension system using contrast-based Fruit Fly Optimisation

In the UK, in 2014 almost fifty thousand motorists made claims about vehicle damages caused by potholes. Pothole damage mitigation has become so important that a number of car manufacturers have officially designated it as one of their priorities. The objective is to improve suspension shock performance without degrading road holding and ride comfort. In this study, it is shown that significant improvement in performance is achieved if a clipped quadratic parameter varying suspension is employed. Optimal design of the proposed system is challenging because of the multiple local minima causing global optimisation algorithms to get trapped at local minima, located far from the optimum solution. To this end an enhanced Fruit Fly Optimisation Algorithm − based on a recent study on how well a fruit fly’s tiny brain finds food − was developed. The new algorithm is first evaluated using standard and nonstandard benchmark tests and then applied to the computationally expensive suspension design problem. The proposed algorithm is simple to use, robust and well suited for the solution of highly nonlinear problems. For the suspension design problem new insight is gained, leading to optimum damping profiles as a function of excitation level and rattle space velocity

A Hierarchal Planning Framework for AUV Mission Management in a Spatio-Temporal Varying Ocean

The purpose of this paper is to provide a hierarchical dynamic mission planning framework for a single autonomous underwater vehicle (AUV) to accomplish task-assign process in a limited time interval while operating in an uncertain undersea environment, where spatio-temporal variability of the operating field is taken into account. To this end, a high level reactive mission planner and a low level motion planning system are constructed. The high level system is responsible for task priority assignment and guiding the vehicle toward a target of interest considering on-time termination of the mission. The lower layer is in charge of generating optimal trajectories based on sequence of tasks and dynamicity of operating terrain. The mission planner is able to reactively re-arrange the tasks based on mission/terrain updates while the low level planner is capable of coping unexpected changes of the terrain by correcting the old path and re-generating a new trajectory. As a result, the vehicle is able to undertake the maximum number of tasks with certain degree of maneuverability having situational awareness of the operating field. The computational engine of the mentioned framework is based on the biogeography based optimization (BBO) algorithm that is capable of providing efficient solutions. To evaluate the performance of the proposed framework, firstly, a realistic model of undersea environment is provided based on realistic map data, and then several scenarios, treated as real experiments, are designed through the simulation study. Additionally, to show the robustness and reliability of the framework, Monte-Carlo simulation is carried out and statistical analysis is performed. The results of simulations indicate the significant potential of the two-level hierarchical mission planning system in mission success and its applicability for real-time implementation

Handling packet dropouts and random delays for unstable delayed processes in NCS by optimal tuning of PIλDμ controllers with evolutionary algorithms

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.The issues of stochastically varying network delays and packet dropouts in Networked Control System (NCS) applications have been simultaneously addressed by time domain optimal tuning of fractional order (FO) PID controllers. Different variants of evolutionary algorithms are used for the tuning process and their performances are compared. Also the effectiveness of the fractional order PI(λ)D(μ) controllers over their integer order counterparts is looked into. Two standard test bench plants with time delay and unstable poles which are encountered in process control applications are tuned with the proposed method to establish the validity of the tuning methodology. The proposed tuning methodology is independent of the specific choice of plant and is also applicable for less complicated systems. Thus it is useful in a wide variety of scenarios. The paper also shows the superiority of FOPID controllers over their conventional PID counterparts for NCS applications.This work has been supported by the Board of Research in Nuclear Sciences (BRNS) of the Department of Atomic Energy (DAE), India, sanction no. 2009/36/62-BRNS, dated November 2009