In the paper, the authors present the approach to modelling of austenitic steel hardening basing on the Frederick-Armstrong’s rule and Chaboche elastic-plastic material model with mixed hardening. Non-linear uniaxial constitutive equations are derived from more general relations with the assumption of an appropriate evolution of back stress. The aim of the paper is to propose a robust and efficient identification method of a well known material model.

A typical LCF strain-controlled test was conducted for selected amplitudes of total strain. Continuous measurements of instant stress and total strain values were performed. Life time of a specimen, signals amplitudes and load frequency were also recorded.

Based on the measurement, identification of constitutive equation parameters was performed. The goal was to obtain a model that describes, including hardening phenomenon, a material behaviour during the experiment until the material failure. As a criterion of optimisation of the model least square projection accuracy of the material response was selected.

Several optimisation methods were examined. Finally, the differential evolution method was selected as the most efficient one. The method was compared to standard optimisation methods available in the MATLAB environment. Significant decrease of computation time was achieved as all the optimisation procedures were run parallel on a computer cluster.

Department of Electrical Engineering, Anna University Regional Centre, Coimbatore, India This paper presents a new approach to solve economic load dispatch (ELD) problem in thermal units with non-convex cost functions using differential evolution technique (DE). In practical ELD problem, the fuel cost function is highly non linear due to inclusion of real time constraints such as valve point loading, prohibited operating zones and network transmission losses. This makes the traditional methods fail in finding the optimum solution. The DE algorithm is an evolutionary algorithm with less stochastic approach to problem solving than classical evolutionary algorithms.DE have the potential of simple in structure, fast convergence property and quality of solution. This paper presents a combination of DE and variable neighborhood search (VNS) to improve the quality of solution and convergence speed. Differential evolution (DE) is first introduced to find the locality of the solution, and then VNS is applied to tune the solution. To validate the DE-VNS method, it is applied to four test systems with non-smooth cost functions. The effectiveness of the DE-VNS over other techniques is shown in general.

The problem of improving the voltage profile and reducing power loss in electrical networks must be solved in an optimal manner. This paper deals with comparative study of Genetic Algorithm (GA) and Differential Evolution (DE) based algorithm for the optimal allocation of multiple FACTS (Flexible AC Transmission System) devices in an interconnected power system for the economic operation as well as to enhance loadability of lines. Proper placement of FACTS devices like Static VAr Compensator (SVC), Thyristor Controlled Switched Capacitor (TCSC) and controlling reactive generations of the generators and transformer tap settings simultaneously improves the system performance greatly using the proposed approach. These GA & DE based methods are applied on standard IEEE 30 bus system. The system is reactively loaded starting from base to 200% of base load. FACTS devices are installed in the different locations of the power system and system performance is observed with and without FACTS devices. First, the locations, where the FACTS devices to be placed is determined by calculating active and reactive power flows in the lines. GA and DE based algorithm is then applied to find the amount of magnitudes of the FACTS devices. Finally the comparison between these two techniques for the placement of FACTS devices are presented.

In the paper an application of differential evolution algorithm to design digital filters with non-standard amplitude characteristics is presented. Three filters with characteristics: linearly growing, linearly falling, and non-linearly growing are designed with the use of the proposed method. The digital filters obtained using this method are stable, and their amplitude characteristics fulfill all design assumptions.

Groundwater contamination due to leakage of gasoline is one of the several causes which affect the groundwater environment by polluting it. In the past few years, In-situ bioremediation has attracted researchers because of its ability to remediate the contaminant at its site with low cost of remediation. This paper proposed the use of a new hybrid algorithm to optimize a multi-objective function which includes the cost of remediation as the first objective and residual contaminant at the end of the remediation period as the second objective. The hybrid algorithm was formed by combining the methods of Differential Evolution, Genetic Algorithms and Simulated Annealing. Support Vector Machines (SVM) was used as a virtual simulator for biodegradation of contaminants in the groundwater flow. The results obtained from the hybrid algorithm were compared with Differential Evolution (DE), Non Dominated Sorting Genetic Algorithm (NSGA II) and Simulated Annealing (SA). It was found that the proposed hybrid algorithm was capable of providing the best solution. Fuzzy logic was used to find the best compromising solution and finally a pumping rate strategy for groundwater remediation was presented for the best compromising solution. The results show that the cost incurred for the best compromising solution is intermediate between the highest and lowest cost incurred for other non-dominated solutions.