Evolutionary Computation Techniques Research Articles

Resource Allocation in Decode-and-Forward Cooperative Communications Networks with Limited Rate Feedback Channel

In this paper, we propose a limited rate feedback scheme for decode-and-forward (DF) relay-assisted networks. It is assumed that the cellular user is far from the base station such that the direct communication is impossible and communication is performed with the help of a DF relay. To study the limited feedback channel, two cases are proposed: Perfect channel distribution information (CDI)-quantized channel state information (CSI) and quantized CDI-quantized CSI. While in the former, the space of channel gains is quantized into a finite number of regions, in the latter case, the space of CDI parameters is also quantized. We formulate the proposed resource allocation into an optimization problem whose objective is to maximize the average achievable rate under an average transmission power constraint. To solve the proposed optimization problems, we adopt the block coordinated descent algorithm, which is based on the space of variables partitioning into finite sets, and particle swarm optimization (PSO), which is an evolutionary computation technique. To solve the optimization problem for quantized CDI-quantized CSI case, we consider an approach based on the Lloyd algorithm. We also model and study the error in the feedback channel. Finally, the impact of different system parameters on the performance of the proposed scheme is investigated through simulations.

並列PSOによる電圧無効電力制御に対するディペンダビリティの評価

Various evolutionary computation techniques have been applied to reactive power and voltage control. High penetration of renewable energies and deregulation of power systems require to shorten an interval of the control. One of the practical solutions for this problem is applications of parallel and distributed computing with dependability, which is an ability to keep sustainable control against various accidents. This paper presents evaluations of dependability for parallel particle swarm optimization based voltage reactive power control. Simulation results with IEEE 14, 30, 57, and 118 bus systems indicate high dependability of the method.

Load area aggregation considering integration of electric vehicles to the system

<span>Current electric power systems have an increasing penetration of electric vehicles, and its effect has to be considered in different <span>studies, such as optimal dispatch or voltage stability, among others. Additionally, considering that power system analysis becomes <span>complex when the number of buses increase, this paper presents a methodology for aggregation of load areas that use a measurement-based load modeling approach based on an evolutionary computational technique and a classical reduction method. This aggregate <span>load area model is proposed to reduce areas that consider electric vehicle (EV) load models. The proposed method provides a static <span>equivalent load model and an equivalent network that can be used to reduce the computational effort required by power system<br /><span>studies. In order to validate the application of the proposed methodology, a 30-bus power system considering several disturbances <span>and levels of penetration of the electric vehicles was used. The results show that the equivalent network model allows the reproduction <span>of different events with an acceptable accuracy when it is compared to the original system behavior.</span></span></span></span></span></span></span><br /><br class="Apple-interchange-newline" /></span>

Load area aggregation considering integration of electric vehicles to the system

<span>Current electric power systems have an increasing penetration of electric vehicles, and its effect has to be considered in different <span>studies, such as optimal dispatch or voltage stability, among others. Additionally, considering that power system analysis becomes <span>complex when the number of buses increase, this paper presents a methodology for aggregation of load areas that use a measurement-based load modeling approach based on an evolutionary computational technique and a classical reduction method. This aggregate <span>load area model is proposed to reduce areas that consider electric vehicle (EV) load models. The proposed method provides a static <span>equivalent load model and an equivalent network that can be used to reduce the computational effort required by power system<br /><span>studies. In order to validate the application of the proposed methodology, a 30-bus power system considering several disturbances <span>and levels of penetration of the electric vehicles was used. The results show that the equivalent network model allows the reproduction <span>of different events with an acceptable accuracy when it is compared to the original system behavior.</span></span></span></span></span></span></span><br /><br class="Apple-interchange-newline" /></span>

A Hybrid Evolutionary System for Automated Artificial Neural Networks Generation and Simplification in Biomedical Applications

Data mining and data classification over biomedical data are two of the most important research fields in computer science. Among the great diversity of techniques that can be used for this purpose, Artifical Neural Networks (ANNs) is one of the most suited. One of the main problems in the development of this technique is the slow performance of the full process. Traditionally, in this development process, human experts are needed to experiment with different architectural procedures until they find the one that presents the correct results for solving a specific problem. However, many studies have emerged in which different ANN developmental techniques, more or less automated, are described. In this paper, the authors have focused on developing a new technique to perform this process over biomedical data. The new technique is described in which two Evolutionary Computation (EC) techniques are mixed to automatically develop ANNs. These techniques are Genetic Algorithms and Genetic Programming. The work goes further, and the system described here allows to obtain simplified networks with a low number of neurons to resolve the problems. The system is compared with the already existent system which also uses EC over a set of well-known problems. The conclusions reached from these comparisons indicate that this new system produces very good results, which in the worst case are at least comparable to existing techniques and in many cases are substantially better.

An intelligent model for cost prediction in new product development projects

Precise cost prediction of new product development (NPD) projects has been a challenge for both academia and practitioners that often requires much effort and experience. In this paper, a combination of particle swarm optimization (PSO), cross validation (CV) and support vector regression (SVR) is proposed to predict the cost of NPD projects. SVR, a novel intelligent technique for time series analysis, can overcome some shortcomings in the conventional approaches; and PSO, a new evolutionary computation technique, is utilized to set the optimal parameters of the SVR. The proposed intelligent model avoids manual selection of these parameters. The PSO solves the difficulty of setting these parameters appropriately and enhances the efficiency and capability of cost prediction. In addition, the CV is employed to train the SVR and improve the reliability of model performance. Then a real dataset of a home appliances manufacturer is provided to illustrate the proposed model and demonstrate the high performance and applicability to cost prediction of the NPD project. Finally, the effectiveness of the support vector model is compared with well-known techniques including multilayer perceptron networks (MLP), normalized radial basis function (NRBF) neural network, and pure SVR in terms of the accuracy measures. Based on the real world dataset, it is observed that the proposed model outperforms other well-known techniques.

Improving the Evolutionary Computation for General Keyboard Arrangement Problem

Thai traditional computer keyboard layouts are derived from the Thai typewriter keyboard's layout, which supports typing with ten fingers. Hence, this layout is not optimized for typing on touch screen device with one finger. This article presents a new method in arrangement the position of Thai characters on the virtual keyboard to support typing by a single finger. The process of the new method consist of analysing Thai Language to create difference digraph table from Benchmark for Enhancing the Standard of Thai language processing corpus (BEST corpus), designed keyboard layout using evolutionary computation technique and estimated time speed of typing by model human hand movement with Fitts' Law. The experiment shows that new keyboard designed by the proposed method has achieved a typing speed faster than the Thai traditional keyboard layout.

Indirect estimation of the ultimate bearing capacity of shallow foundations resting on rock masses

The success of a foundation design for structures is to precisely estimate the bearing capacity of underlying soils or rocks. To avoid the elaborate in-situ experimental methods, several approaches presented by various researchers for the estimation of the bearing capacity factor. Despite this fact, there still exists a serious need to develop more robust predictive models. The aim of this paper is to propose a novel formulation for the ultimate bearing capacity of shallow foundations resting on/in rock masses, using a powerful evolutionary computational technique, namely linear genetic programming. Thus, a comprehensive set of data is collected to develop the model. In order to evaluate the validity of the obtained model, several analyses are conducted and compared with those provided by other researchers. Consequently, the results clearly demonstrate the proposed model accurately characterize the bearing capacity factor and reach a notably better prediction performance than the traditional models.

Dynamic particle swarm optimization and K-means clustering algorithm for image segmentation

K-means clustering is usually used in image segmentation due to its simplicity and rapidity. However, K-means is heavily dependent on the initial number of clusters and easily falls into local falls into local optimum. As a result, it is often difficult to obtain satisfactory visual effects. As an evolutionary computation technique, particle swarm optimization (PSO) has good global optimization capability. Combined with PSO, K-means clustering can enhance its global optimization capability. But PSO also has the shortcoming of easily falling into local optima. This study proposes a new image segmentation algorithm called dynamic particle swarm optimization and K-means clustering algorithm (DPSOK), which is based on dynamic particle swarm optimization (DPSO) and K-means clustering. The calculation methods of its inertia weight and learning factors have been improved to ensure DPSOK algorithm keeping an equilibrium optimization capability. Experimental results show that DPSOK algorithm can effectively improve the global search capability of K-means clustering. It has much better visual effect than K-means clustering in image segmentation. Compared with classic particle swarm optimization K-means clustering algorithm (PSOK), DPSOK algorithm has obvious superiority in improving image segmentation quality and efficiency.

Introduction of Biogeography-Based Programming as a new algorithm for solving problems

Application of evolutionary computation techniques is relatively novel for machine learning. Motivated by different types of evolutionary computation techniques, different types of automatic programming were proposed. Biogeography-Based Optimization (BBO) is a new evolutionary algorithm that is inspired by the science of biogeography and has been shown to be competitive to other population-based algorithms. Inspired by biogeography theory and previous results, in this paper Biogeography-Based Programming (BBP) is proposed as a new type of automatic programming for creating polynomial regression models. In order to show the effectiveness of the proposed BBP, a number of experiments were carried out on a suite set of benchmark functions and the results were also compared with several existing automatic programming algorithms. Furthermore, sensitivity analysis was performed for the parameter settings of the proposed BBP. The results indicate that the proposed model is promising in terms of success rate and accuracy and it performs better than other algorithms investigated in this consideration.

Cricket behaviour-based evolutionary computation technique in solving engineering optimization problems

Meta-heuristicalgorithms are widely used in various areas such as engineering, statistics, industrial, image processing, artificial intelligence etc. In this study, the Cricket algorithm which is a novel nature-inspired meta-heuristic algorithm approach which can be used for the solution of some global engineering optimization problems was introduced. This novel approach is a meta-heuristic method that arose from the inspiration of the behaviour of crickets in the nature. It has a structure for the use in the solution of various problems. In the development stage of the algorithm, the good aspects of the Bat, Particle Swarm Optimization and Firefly were experimented for being applied to this algorithm. In addition to this, because of the fact that these insects intercommunicate through sound, the physical principles of sound propagation in the nature were practiced in the algorithm. Thanks to this, the compliance of the algorithm to real life tried to be provided. This new developed approach was applied on the familiar global engineering problems and the obtained results were compared with the results of the algorithm applied to these problems.

Computational Intelligence in Optimization of Process Parameters in Turning Metals and Composites – A Review

In the modern competitive scenario in manufacturing industries, producing products with low cost, less time and good quality are the ultimate goal of any manufacturer. To achieve the goal, several optimization tools are developed to optimize the process parameters of the machining process. Turning is one of the machining processes that cannot be avoided in any manufacturing industries. In this review, optimization of process parameters in turning process by computational intelligence (CI) paradigms for the past ten years is studied. Optimization by CI paradigms such as Fuzzy System (FS), Evolutionary Computation techniques Genetic Algorithm (GA), Swarm Intelligence including Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Neural Networks (ANN) etc., is considered. In turning process, surface roughness, tool wear, production time and cost are optimized.

Brillouin distributed temperature sensor employing phase modulation and optimization techniques

The process of signal-to-noise ratio (SNR) improvement and the suppression of stimulated Brillouin scattering (SBS) effects on a long-range distributed sensor are presented in this paper. We have proposed an improved performance Brillouin distributed temperature sensor using phase modulation and optimization techniques. Global evolutionary computing-based optimization techniques (genetic algorithm (GA), particle swarm optimization (PSO) and differential evolution algorithm (DE)) are applied for both fiber and receiver optimization. The combination of phase modulation and the global evolutionary computing technique improved the SBS threshold power of the proposed system to an extent of 5.95dBm. However, with receiver and fiber optimization, for an input power of 0dBm, the proposed system provides SNR improvement up to 3.5dB, for temperature sensing over a distance of 50km with temperature resolution of 1.44K and spatial resolution of 32m without using filter. However, for the equivalent sensing range, we have achieved a temperature resolution as 0.51K using the above optimized system with filter.

Perturbative methods for maximum power point tracking (MPPT) of photovoltaic (PV) systems: a review

Over the past few decades, the world demand for energy has risen steadily, forcing the world communities to look for alternative sources. Photovoltaic (PV) is seen as the most promising solution for this demand. However, the PV system is popularly known to suffer from low-energy harvesting due to the change of environment conditions. An inexpensive and practical solution to extract the energy from the PV is by improving the maximum power point tracking (MPPT) controller technique. An ideal MPPT should be able to track the true maximum power operating point accurately under all circumstances and overcome all nonlinearities in the characteristic I-V curves. This paper presents an updated review of the techniques based on the perturbative MPPT methods, both using the conventional and soft computing methods. The working principles of the techniques, parameter effects, and their limitations are discussed. The focus of this review is to direct the readers to the new direction of MPPT using the artificial intelligence and evolutionary computation techniques. Besides serving as a comprehensive source of information, the paper also provides a critical review on the relative performance of the selected MPPT methods. This includes the module dependency, tracking performance, and the ability to handle the partial shading conditions. Copyright © 2015 John Wiley & Sons, Ltd.

Automatic generation control of multi area thermal system using Bat algorithm optimized PD–PID cascade controller

Abstract This article presents automatic generation control (AGC) of an interconnected multi area thermal system. The control areas are provided with single reheat turbine and generation rate constraints of 3%/min. A maiden attempt has been made to apply a Proportional derivative–Proportional integral derivative (PD–PID) cascade controller in AGC. Controller gains are optimized simultaneously using more recent and powerful evolutionary computational technique Bat algorithm (BA). Performance of classical controllers such as Proportional Integral (PI) and Proportional Integral Derivative (PID) controller are investigated and compared with PD–PID cascade controller. Investigations reveal that PI, and PID provide more or less same response where as PD–PID cascade controller provides much better response than the later. Dynamic analysis has also been carried out for the controllers in presence of random load pattern, which reveals the superior performance of the PD–PID cascade controller. Sensitivity analysis reveals that the BA optimized PD–PID Cascade controller parameters obtained at nominal condition of loading, size and position of disturbance and system parameter (Inertia constant, H) are robust and need not be reset with wide changes in system loading, size, position of disturbance and system parameters. The system dynamic performances are studied with 1% step load perturbation in Area1.

Verification of Dependability on Parallel Particle Swarm Optimization Based Voltage and Reactive Power Control

This paper verifies dependability on parallel particle swarm optimization (PSO) based voltage reactive power control. Various evolutionary computation techniques have been applied to reactive power and voltage control (Volt/Var Control: VVC). High penetration of renewable energies and deregulation of power systems require to shorten an interval of the control. One of the practical solutions for this problem is applications of parallel and distributed computing with dependability, which is an ability to keep sustainable control against various accidents. Simulation results with IEEE 14, 30, and 57 bus systems indicate high dependability of the method.

An Analysis of Efficiency on Multi agent Systems with Symbiotic Learning and Evolution

Masbiole (Multiagent Systems with Symbiotic Learning and Evolution) is a new methodology in conventional Multiagent Systems (MASs). In Masbiole, agents evolve considering not only their own benefits and losses, but also the benefits and losses of opponent agents. As a result, Masbiole can escape from Nash Equilibria and obtain better performances than conventional MASs. On the other hand, a newly developed evolutionary computing technique called Genetic Network Programming (GNP) which has the directed graph-type gene structure can develop and design the required intelligence mechanism for agents. As a result, GNP is considered to be well-suited for optimization problems in agents of MASs. Therefore, in this study, a test bed negotiation model is proposed using the evolutionary method of Masbiole as well as the evolutionary method of GNP, with the aim to study the effectiveness and efficiency of Masbiole in dynamic problems. The results obtained by the symbiotic evolution of the Masbiole systems are compared with those obtained by the GNP evolution.

An Effective Hybrid Particle Swarm Optimization for Flexible Job Shop Scheduling Problem

The job shop scheduling problem is one of the most arduous combinatorial optimization problems. Flexible job shop scheduling problem (FJSP) is an important extension of the classical job shop scheduling problem, where the same operation could be processed on more than one machine. This paper proposed a new effective approach based on the hy- bridization of the particle swarm optimization (PSO) and local search algorithm of variable neighborhood search (VNS) to solve the FJSP for minimizing the makespan, the maximal machine workload, and the total workload of machines. PSO is a highly efficient evolutionary computation technique inspired by bird's flight and communication behaviors. PSO inte- grating the local search and global search has highly search ability. VNS has the strong local search ability. Benchmark problems are used to evaluate and study the performance of the proposed algorithm. Computational results show that the proposed hybrid algorithm is an efficient and effective approach.

Optimum Features selection by fusion using Genetic Algorithm in CBIR

The evaluation of the performance of the Content Based Image Retrieval is undertaken for the consideration in this paper. Here the point of the discussion is the performance of the CBIR system using object oriented image segmentation and the evolutionary computational technique. The visual characteristics of the objects such as color, intensity and texture are extracted by the conventional methods. Object oriented image segmentation along with the evolutionary computational technique is proposed here for Image Retrieval Algorithm. Unsupervised Curve evolution method is used for object oriented segmentation of the Image and genetic Algorithm is used for the Optimum Classification and reduction in the Feature dimensionality. The Algorithm is tested on the images which are characterized by the low depth. The Berkeley database is found to be suitable for this purpose. The experimental result shows that the Genetic Algorithm enhances the performance of this Content Based Image Retrieval and found to be suitable for optimization of features selection and compression technique for Feature space.

A Modified Matricial PSO Algorithm Applied to System Identification with Convergence Analysis

Recently, several evolutionary computation techniques have been used in research areas such as parameter estimation of linear and nonlinear dynamic processes. This motivates the use of algorithms such as the particle swarm optimization (PSO) in the aforementioned fields of knowledge. However, little is known about the convergence of this algorithm, and mainly the analyses and studies have focused on experimental results. Therefore, the objective of this work is to propose a structure for the PSO that better analyze the convergence of the algorithm analytically. For this, the PSO is restructured to assume a matrix form, reformulated as a piecewise linear system. There was a convergence analysis of the algorithm as a whole, using an almost sure convergence criterion applicable to switched systems. Subsequently, traditional parameter identification algorithms were combined with the matricial PSO (MPSO), so as to make the identification results as good as or better than identifying only using the PSO or only the traditional algorithms. The obtained functions, after the identification, using the MPSO algorithm combined with the conventional identification algorithms, presented a better generalization and proper identification. The conclusions reached were that the hybridization permits a minimum performance and also contributes to improve the results obtained with the traditional algorithms, allowing the system representation in a higher range of frequencies.