Evolutionary Algorithm Method Research Articles

A New Automated Design Method Based on Machine Learning for CMOS Analog Circuits

ABSTRACTA new simulation based automated CMOS analog circuit design method which applies a multi-objective non-Darwinian-type evolutionary algorithm based on Learnable Evolution Model (LEM) is proposed in this article. The multi-objective property of this automated design of CMOS analog circuits is governed by a modified Strength Pareto Evolutionary Algorithm (SPEA) incorporated in the LEM algorithm presented here. LEM includes a machine learning method such as the decision trees that makes a distinction between high- and low-fitness areas in the design space. The learning process can detect the right directions of the evolution and lead to high steps in the evolution of the individuals. The learning phase shortens the evolution process and makes remarkable reduction in the number of individual evaluations. The expert designer’s knowledge on circuit is applied in the design process in order to reduce the design space as well as the design time. The circuit evaluation is made by HSPICE simulator. In order to improve the design accuracy, bsim3v3 CMOS transistor model is adopted in this proposed design method. This proposed design method is tested on three different operational amplifier circuits. The performance of this proposed design method is verified by comparing it with the evolutionary strategy algorithm and other similar methods.

Simulated Annealing and Evolutionary Algorithm for Base Station Location Problem: a Comparison of Methods

A modifications of the evolutionary algorithm and simulated annealing method for solving the base station location problem for creating a wireless data network is introduced in the article. By the way of computer simulation a comparison of speed and accuracy of solutions obtained by the proposed methods and the method of exhaustive search is produced. The study revealed that new simulated annealing method show better results than the modified evolutionary algorithm.

Risk Assessment of Drought Based on IEAPP-IDM in Qujing, Yunnan Province, China

A new model for risk assessment of drought based on projection pursuit optimized by immune evolutionary algorithm and information diffusion method (IEAPP-IDM) was proposed. Due to the fact that drought risk assessment is a complex multicriteria and multilevel problem, the IEAPP-IDM model can project the multidimensional indicators of samples into one-dimension projection scores; then, the information carried by the projection scores was diffused into drought risk levels; finally, the drought disaster risk estimate was obtained. In the present study, Qujing was employed to assess the drought risk with the proposed model. The results showed that Xuanwei possessed higher risk, while Luliang and Zhanyi possessed lower risk. At the same time, the probability risk of drought in Malong and Luoping was increasing, while the probability risk of drought in in Qilin and Shizong was decreasing. The results obtained by the assessment model are consistent with the actual situation of Qujing and verify the model’s effectiveness. The study can provide scientific reference in drought risk management for Qujing and other places of China.

Optimization of a large horizontal-axis wind turbine using comparison between two hybrid evolutionary algorithms

Twist and chord distribution are important parameters in optimal blade shape of MW class wind turbines. Although this is a serious challenge for the designer, their optimum design is an imperative for success in reaching the optimal blade shape. In this regard, the use of a suitable tool can be very effective in achieving this important goal. Furthermore, in order to improve the performance of large wind turbines, an objective function involving the combination of parameters of solidity and power coefficients are provided. The output power of the wind turbine is presented as the power coefficient of this objective function. Based on the presented objective function, it is sought to maximize power generation and minimize solidity. A decrease in solidity can cause weight loss of blades and ultimately reduce costs and consumed materials. One of the useful skills in solving optimization problems with high accuracy and convergence rate is hybridization of intelligent optimization algorithms. In this paper, we provided a comparison between two hybrid Evolutionary algorithm methods as genetic-based bees algorithm (GBBA) and harmony search-based bat algorithm (HSBBATA) optimization algorithms for designing the optimum shape of MW wind turbine blades. The present paper introduces two hybrid algorithms called HSBBAT and GBBA. GBBA is a novel population-based hybrid algorithm (proposed method). Therefore; we have tried using the hybrid algorithms to achieve better results for finding global optimum points with higher power generation and convergence rate for optimal blade shape of large wind turbines. On the other hand, defining three different scenarios for twist and chord, it is intended to achieve an optimal distribution for these aerodynamic parameters. The results show that the suggested algorithm GBBA, proves to have better results compared to HSBBATA.

Structure and water oxidation activity of 3d metal oxides

Water splitting driven by solar energy is regarded as the candidate for the next generation of power source. The reaction is however kinetically hindered by the oxygen evolution reaction (OER) involving four proton–electron transfer steps. The ideal OER catalyst should avoid using precious elements, such as Ir, Ru, and Pt, and have a long‐term stability under positive bias potential. Recent experiments have shown that most 3d oxides are OER active catalysts, while some can even achieve comparable activities to commercial Ir/Ru catalysts in lab condition. In this article, we review the recent theoretical progress for characterizing the structure of 3d oxides and understanding the photo‐electrocatalytic water splitting mechanism over these catalysts. The methodology for global structure exploration, including evolutionary algorithm and stochastic surface walking method, is first introduced together with their applications in exploring the potential energy surface of TiO2 and NiOx systems. The current theoretical approaches to investigate the thermodynamics and kinetics of photo‐/electrochemical reactions are discussed and the latest understanding for OER reactions are summarized. WIREs Comput Mol Sci 2016, 6:47–64. doi: 10.1002/wcms.1236This article is categorized under: Structure and Mechanism > Computational Materials Science Computer and Information Science > Computer Algorithms and Programming Theoretical and Physical Chemistry > Reaction Dynamics and Kinetics

Multi-Objective Distribution feeder reconfiguration to improve transient stability, and minimize power loss and operation cost using an enhanced evolutionary algorithm at the presence of distributed generations

Abstract This paper proposes a multi-objective evolutionary algorithm method for Distribution feeder reconfiguration (DFR) with distributed generators (DG) in a practical system. Considering the low inertia constant of DG units in order to take the transient stability of DGs into account is one of the major issues in power systems. Especially when the penetration of DGs is low, the impacts of them on the distribution system transient stability may be neglected. However, when the penetration of DG increases, the transient stability of them must be taken into account (more DGs, more transient issues). To this end, the DFR problem has been solve by an enhanced Gravitational Search Algorithm (EGSA) to improve the transient stability index and decrease losses and operation cost in a distribution test system with multiple micro-turbines. The effectiveness of the proposed approach is studied based on a typical 33-bus test system. For getting close to the practical condition and considering the detailed dynamic models of the generators and other electric devices in power system, simulation and programming of this approach are done by the DIgSILENT® Power Factory software.

Diagnosis of Distributed Denial of Service Attacks using the Combination Method of Fuzzy Neural Network and Evolutionary Algorithm

Availability, integrity and confidentiality are the key concepts of cyber security. Distributed denial of service attacks affecting the availability of information sources. This type of attack when be successful which led to the non-availability to the information sources. The success and impact of service denial attacks will be identified based on the victims and the risk level, threat and consequences of this attack, according to each case is different. The aim of this study is DDOS attack detection with differential evolutionary algorithm combination method and the fuzzy neural network ANFIS. For this purpose will pay to simulate and evaluate the proposed approach. In order to simulate in this research which is used MATLAB 2013b software which is a programming language and environment for scientific computing. The result of comparison showed that the ANFIS combination algorithm and differential evolution than to the ANFIS algorithm has more accuracy.

Evolutionary structural and spatial adaptation of topologically differentiated tensile systems in architectural design

AbstractThis paper presents research in the development of heuristic evolutionary algorithms (EAs) for generating and exploring differentiated force-based structures. The algorithm is weighted toward design exploration of topological differentiation while including specific structural and material constraints. An embryological EA model is employed to “grow” networks of mass-spring elements achieving desired mesh densities that resolve themselves in tensile force (form-active) equilibrium. The primal quadrilateral quadrisection method serves as the foundation for a range of extensible subdivision methods. Unique to this research, the quad is addressed as a “cell” rather than a topological or geometric construct, allowing for the contents of the cell to vary in number of mass-spring elements and orientation. In this research, this approach has been termed the quadrilateral quadrisection with n variable topological transformation method. This research culminates with the introduction of a method for grafting meshes where emergent features from the evolved meshes can be transposed and replicated in an explicit yet informed manner. The EA and grafting methods function within a Java-based software called springFORM, developed in previous research, which utilizes a mass-spring based library for solving force equilibrium and allows for both active (manual) and algorithmic topology manipulation. In application to a specific complex tensile mesh, the design framework, which combines the generative EA and mesh grafting method, is shown to produce emergent and highly differentiated topological arrangements that negotiate the specific relationships among a desired maximal mesh density, geometric patterning, and equalized force distribution.

Exergoeconomic multi-objective optimization of an externally fired gas turbine integrated with a biomass gasifier

This study deals with thermodynamic and economic analysis of a combined gas turbine and Organic Rankine Cycle integrated with a biomass gasifier. A modified model is used to increase the precision of the gasifier thermodynamic model. Seven decision variables, namely, biomass gasification temperature (Tgasif), combustion temperature (Tcomb), gas turbine inlet temperature (T3), gas turbine isentropic efficiency (ηGT), compressor isentropic efficiency (ηcomp), compressor pressure ration (rp) and maximum ORC operating pressure (P3R), are selected as the main decision variables of the combined system. The total cost rate and exergy efficiency of the system are chosen as the two main objective functions. A group method of data handling (GMDH) type neural network and evolutionary algorithm (EAs) are used for modeling the effects of the seven decision variables on both objective functions. The result of multi-objective optimization shows that the exergy efficiency of the system is 15.6%, which can be increase to 17.9% in the optimal state, regardless of the total cost rate of system as objective function. In addition, in order to better illustrate the effects of decision variables change in three selected points of the Pareto curve, a sensitive analysis is performed.

Ranking and comparing evolutionary algorithms with Hellinger-TOPSIS

When multiple algorithms are applied to multiple benchmarks as it is common in evolutionary computation, a typical issue rises, how can we rank the algorithms? It is a common practice in evolutionary computation to execute the algorithms several times and then the mean value and the standard deviation are calculated. In order to compare the algorithms performance it is very common to use statistical hypothesis tests. In this paper, we propose a novel alternative method based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to support the performance comparisons. In this case, the alternatives are the algorithms and the criteria are the benchmarks. Since the standard TOPSIS is not able to handle the stochastic nature of evolutionary algorithms, we apply the Hellinger-TOPSIS, which uses the Hellinger distance, for algorithm comparisons. Case studies are used to illustrate the method for evolutionary algorithms but the approach is general. The simulation results show the feasibility of the Hellinger-TOPSIS to find out the ranking of algorithms under evaluation.

Parameters Estimation For A Patellofemoral Joint Of A Human Knee Using A Vector Method

Position and displacement analysis of a spherical model of a human knee joint using the vector method was presented. Sensitivity analysis and parameter estimation were performed using the evolutionary algorithm method. Computer simulations for the mechanism with estimated parameters proved the effectiveness of the prepared software. The method itself can be useful when solving problems concerning the displacement and loads analysis in the knee joint.

EXAFS study of the local structure of crystalline and nanocrystalline Y2O3 using evolutionary algorithm method

Temperature-dependent local structure and lattice dynamics of yttria (Y2O3) were studied by X-ray absorption spectroscopy. Novel method, which combines the reverse Monte Carlo and evolutionary algorithm techniques, was applied for the analysis of extended X-ray absorption fine structure at the Y K-edge. This approach allowed us to reconstruct 3D atomic structure models of crystalline and nanocrystalline Y2O3.

A hybrid multi-objective optimization method considering optimization problems in power distribution systems

Various kinds of new engineering technologies have been studied to realize the low-carbon and sustainable power supply systems all over the world. In actual implementation of these technologies, mostly, there are multiple objectives with trade off relationships among each other, and also various constraints in the achievement of these objectives. Therefore, it should be essential to solve multi-objective optimization problems effectively in the applications of these new technologies in power systems. This paper proposes an improved method to realize multi-objective optimization for critical challenges in advanced power systems. To realize that, in an optimal dispersed generation installation problem, that is, one of effective measures for low-carbon power systems, various optimization methods and their combination methods are evaluated and a hybrid method for evolutionary algorithms was developed. The method can provide improved results compared with other state-of-the-art multi-objective optimization methods.

A-TOPSIS – An Approach Based on TOPSIS for Ranking Evolutionary Algorithms

Abstract In this paper, we propose an alternative novel method based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to solve the problem of ranking and comparing algorithms. In evolutionary computation, algorithms are executed several times and then a statistic in terms of mean values and standard deviations are calculated. In order to compare algorithms performance it is very common to handle such issue by means of statistical tests. Ranking algorithms, e.g., by means of Friedman test may also present limitations since they consider only the mean value and not the standard deviation of the results. Since the TOPSIS is not able to handle directly this kind of data, we develop an approach based on TOPSIS for algorithm ranking named as A-TOPSIS. In this case, the alternatives consist of the algorithms and the criteria are the benchmarks. The rating of the alternatives with respect to the criteria are expressed by means of a decision matrix in terms of mean values and standard deviations. A case study is used to illustrate the method for evolutionary algorithms. The simulation results show the feasibility of the A-TOPSIS to find out the ranking of algorithms under evaluation.

Comparative Study of Performance of Chemical Reaction Optimization with Genetic Algorithm (GA)

Chemical reaction optimisation (CRO) is well suited in searching global solutions to varied nature of optimisation problems. It is amongst newer methods of evolutionary algorithms, nature inspired meta-heuristics for optimisation. A chemical reaction is a process of transforming reactants; the unstable substances into products the relatively stable ones. In chemical reactions, the reactants with some initial energy interact with each other through a sequence of elementary steps. At the end, molecules with minimum energy to support their stable structure are formed. This phenomenon is the source of inspiration in development of algorithm for CRO to get optimal solution. Present work aims at development of CRO using MATLAB. It also aims in study of dynamics of various parameters of CRO in searching optimum solutions. It is further extended in comparative studies of performance of CRO with other conventional as well as evolutionary optimisation methods such as Genetic Algorithm. Numerical experiments for two test functions in the category of non-linear constrained optimisation problems reported in the literature are carried. The results are indicative of the utility of CRO and its performance is observed at par with other optimisation methods. It can be concluded that there is lot of potential in CRO as an effective alternate optimisation method with universal applicability. There is need for more numerical experimentation to substantiate this claim.

Modifications of Fitness Assignment Parts of Some Multi-Objective Evolutionary Algorithms

Accepted: 03.12.2014 Evolutionary algorithms are very popular methods to solve multiobjective optimization problems. In literature, there are many multi-objective evolutionary algorithm methods. It is possible to improve these methods. This paper proposes a line of refinements and modifications on the fitness assignment part of well known SPEA method. The density information generated by k-NN (k-th nearest neighbor method) and the concept of domination power are used to modify SPEA in order to deliver a much refinement fitness values in terms of uniqueness and homogeneity. Two modified methods are proposed (named SPEAmod1 and SPEAmod2). The main goal is to extract more information from a population (and is to deliver to the decision maker) by using proposed variants than the original method. The original method and its modifications are tested on four well-known test functions in the literature. The proposed modifications are shown to be superior to the original method. Finally, fitness assignment capability of original SPEA method is significantly improved.

Capacitance Characteristic Optimization of Germanium MOSFETs with Aluminum Oxide by Using a Semiconductor-Device-Simulation-Based Multi-Objective Evolutionary Algorithm Method

This paper, for the first time, optimizes the characteristics of capacitance–voltage (C–V) of germanium (Ge) metal-oxide-semiconductor field effect transistors (MOSFETs) with aluminum oxide (Al2O3) by using a semiconductor-device-simulation-based multi-objective evolutionary algorithm (MOEA) technique. By solving a set of 2D semiconductor device transport equations, numerical simulation is intensively performed for the optimization of the C–V curve of Ge MOSFET devices. To optimize the capacitance of Ge MOSFETs with respect to the applied voltage, by minimizing the total errors of the C–V curve between the device simulation and a given specification (and experimentally measured data), the thicknesses of Al2O3 and GeO2, the work function of gate electrodes, the distribution range of channel doping, the dielectric constants of Al2O3 and GeO2, and the source/drain doping concentration are considered in the process of optimization. The semiconductor device simulation and the MOEA method are integrated and performed based on a unified optimization framework. According to the sharp variation characteristics of the C–V curve, except for using a residual sum of squares (RSS) (i.e., the sum of squares of residuals) as an objective function, physical key parts of the curve are also considered in the optimization problem. The engineering results of this study indicate that the semiconductor-device-simulation-based MOEA method shows great performance to optimize the parameters, which not only minimize the objective values but also match the curve shape.

Discussion on “Solving Preventive-Corrective SCOPF by a Hybrid Computational Strategy”

Preventive control (PC) and corrective control (CC) are complementary actions in protecting large power systems against the risk of blackout. This paper addresses the optimal coordination between PC and CC via a preventive-corrective security-constrained optimal power flow model. The objective is to minimize the total expected-security-control cost which is the sum of the costs of PC and CC considering the probability of the contingencies. The constraints include system operating limits in pre- and post-contingency states, the existence of the post-contingency short-term equilibrium, and the coupling constraints on the CC actions. To solve the model, a hybrid computational strategy combining evolutionary algorithm and interior-point method is developed. The solution process consists of globally searching the critical feasible region and locally optimizing the operating variables in the found region, the two procedures interact iteratively to progressively tighten the solution region leading to the final solution. The proposed model and computational strategy are demonstrated on the IEEE 14-bus and 118-bus test systems. To speed up the computation, parallel processing of the approach is implemented.

Closure to Discussion on “Solving Preventive-Corrective SCOPF by a Hybrid Computational Strategy”

Preventive control (PC) and corrective control (CC) are complementary actions in protecting large power systems against the risk of blackout. This paper addresses the optimal coordination between PC and CC via a preventive-corrective security-constrained optimal power flow model. The objective is to minimize the total expected-security-control cost which is the sum of the costs of PC and CC considering the probability of the contingencies. The constraints include system operating limits in pre- and post-contingency states, the existence of the post-contingency short-term equilibrium, and the coupling constraints on the CC actions. To solve the model, a hybrid computational strategy combining evolutionary algorithm and interior-point method is developed. The solution process consists of globally searching the critical feasible region and locally optimizing the operating variables in the found region, the two procedures interact iteratively to progressively tighten the solution region leading to the final solution. The proposed model and computational strategy are demonstrated on the IEEE 14-bus and 118-bus test systems. To speed up the computation, parallel processing of the approach is implemented.

Update-based evolution control: A new fitness approximation method for evolutionary algorithms

Evolutionary algorithms are robust optimization methods that have been used in many engineering applications. However, real-world fitness evaluations can be computationally expensive, so it may be necessary to estimate the fitness with an approximate model. This article reviews design and analysis of computer experiments (DACE) as an approximation method that combines a global polynomial with a local Gaussian model to estimate continuous fitness functions. The article incorporates DACE in various evolutionary algorithms, to test unconstrained and constrained benchmarks, both with and without fitness function evaluation noise. The article also introduces a new evolution control strategy called update-based control that estimates the fitness of certain individuals of each generation based on the exact fitness values of other individuals during that same generation. The results show that update-based evolution control outperforms other strategies on noise-free, noisy, constrained and unconstrained benchmarks. The results also show that update-based evolution control can compensate for fitness evaluation noise.