Evolutionary Computation Techniques Research Articles

Evolutionary Computation-Based Techniques Over Multiple Data Sets: An Empirical Assessment

In the realm of software testing various organizations wish to predict the faults in their software systems prior to their deployment. This improves the delivered quality and also reduces the maintenance effort. A multitude of software metrics and statistical models have been developed to solve this problem and one such method is called defect prediction. Defect prediction is the process of identifying the defects in the software program prior to its deployment. In recent times, a class of learners called evolutionary computation (EC) techniques has emerged. These EC techniques apply the Darwinian principle of ‘survival of the fittest’. This study performs an empirical assessment of the performance of various EC techniques in the prediction of software defects over multiple data sets. An empirical assessment compares and assesses the performance capability of 16 EC techniques for evaluating the relationship between object-oriented metrics and defect prediction. The developed models are validated using 7 data sets obtained from open source software systems developed by the Software Foundation. On investigating their predictive capabilities and comparative performance, it was found that a majority of EC techniques proved to be highly effective. DTG (a hybridized algorithm) was observed to be the best performing technique. The work done in the current study shows that EC techniques are very effective and can be highly beneficial to testers in the realm of defect prediction in the future.

Controller Design of DFIG Based Wind Turbine by Using Evolutionary Soft Computational Techniques

This manuscript illustrates the controller design for a doubly fed induction generator based variable speed wind turbine by using a bioinspired scheme. This methodology is based on exploiting two proficient swarm intelligence based evolutionary soft computational procedures. The particle swarm optimization (PSO) and bacterial foraging optimization (BFO) techniques are employed to design the controller intended for small damping plant of the DFIG. Wind energy overview and DFIG operating principle along with the equivalent circuit model is adequately discussed in this paper. The controller design for DFIG based WECS using PSO and BFO are described comparatively in detail. The responses of the DFIG system regarding terminal voltage, current, active-reactive power, and DC-Link voltage have slightly improved with the evolutionary soft computational procedure. Lastly, the obtained output is equated with a standard technique for performance improvement of DFIG based wind energy conversion system.

Controller Design of DFIG Based Wind Turbine by Using Evolutionary Soft Computational Techniques

Controller Design of DFIG Based Wind Turbine by Using Evolutionary Soft Computational Techniques

Evolutionary feature manipulation in data mining/big data

Known as the GIGO (Garbage In, Garbage Out) principle, the quality of the input data highly influences or even determines the quality of the output of any machine learning, big data and data mining algorithm. The input data which is often represented by a set of features may suffer from many issues. Feature manipulation is an effective means to improve the feature set quality, but it is a challenging task. Evolutionary computation (EC) techniques have shown advantages and achieved good performance in feature manipulation. This paper reviews recent advances on EC based feature manipulation methods in classifcation, clustering, regression, incomplete data, and image analysis, to provide the community the state-of-the-art work in the field.

Nonstructural Flood Control using Multi-reservoir Operation

Nonstructural flood damage minimization through optimizing a short term multi-reservoir system operation is considered in this paper using a simulation-based optimization model. The well known evolutionary computation technique of particle swarm optimization (PSO) has been combined with a simulation model of river flood routing. The hydraulic routing model includes numerical solution of unsteady gradually varied flow equations by Preissmann method. The developed model has been used in a three-reservoir system as a real case study southwest of Iran. The results show applicability and efficiency of the proposed simulation-optimization model in determining optimal reservoir releases.

Evolutionary computation for automatic Web service composition: an indirect representation approach

Web services have become increasingly popular in recent years, and they are especially suitable to the process of Web service composition, which is when several services are combined to create an application that accomplishes a more complex task. In recent years, significant research efforts have been made on developing approaches for performing Quality of Service -aware Web service composition. Evolutionary computing (EC) techniques have been widely used for solving this problem, since they allow for the quality of compositions to be optimised, meanwhile also ensuring that the solutions produced have the required functionality. Existing EC-based composition approaches perform constrained optimisation to produce solutions that meet those requirements, however these constraints may hinder the effectiveness of the search. To address this issue, a novel framework based on an indirect representation is proposed in this work. The core idea is to first generate candidate service compositions encoded as sequences of services. Then, a decoding scheme is developed to transform any sequence of services into a corresponding feasible service composition. Given a service sequence, the decoding scheme builds the workflow from scratch by iteratively adding the services to proper positions of the workflow in the order of the sequence. This is beneficial because it allows the optimisation to be carried out in an unconstrained way, later enforcing functionality constraints during the decoding process. A number of encoding methods and corresponding search operators, including the PSO, GA, and GP-based methods, are proposed and tested, with results showing that the quality of the solutions produced by the proposed indirect approach is higher than that of a baseline direct representation-based approach for twelve out of the thirteen datasets considered. In particular, the method using the variable-length sequence representation has the most efficient execution time, while the fixed-length sequence produces the highest quality solutions.

Cooperative object transport with a swarm of e-puck robots: robustness and scalability of evolved collective strategies

Cooperative object transport in distributed multi-robot systems requires the coordination and synchronisation of pushing/pulling forces by a group of autonomous robots in order to transport items that cannot be transported by a single agent. The results of this study show that fairly robust and scalable collective transport strategies can be generated by robots equipped with a relatively simple sensory apparatus (i.e. no force sensors and no devices for direct communication). In the experiments described in this paper, homogeneous groups of physical e-puck robots are required to coordinate and synchronise their actions in order to transport a heavy rectangular cuboid object as far as possible from its starting position to an arbitrary direction. The robots are controlled by dynamic neural networks synthesised using evolutionary computation techniques. The best evolved controller demonstrates an effective group transport strategy that is robust to variability in the physical characteristics of the object (i.e. object mass and size of the longest object’s side) and scalable to different group sizes. To run these experiments, we designed, built, and mounted on the robots a new sensor that returns the agents’ displacement on a 2D plane. The study shows that the feedback generated by the robots’ sensors relative to the object’s movement is sufficient to allow the robots to coordinate their efforts and to sustain the transports for an extended period of time. By extensively analysing successful behavioural strategies, we illustrate the nature of the operational mechanisms underpinning the coordination and synchronisation of actions during group transport.

A novel approach to frequency support in a wind integrated power system

This paper discusses the impact of wind penetration on frequency control of a thermal dominated system considering Generation Rate Constraints (GRC) and dead band non-linearities. The hidden inertia emulation and coordinated operation of conventional power generation systems with wind energy can effectively alleviate the frequency excursions during sudden load disturbances. Conventional energy storage device like Flywheel Energy Storage (FES) system can be used in conjunction with wind integrated power system to overcome the intermittent nature of power generation. Thyristor Controlled Series Compensator (TCSC) is found to be effective in damping low frequency oscillations in weak tie-lines and supplement the frequency regulation. A stochastic population based evolutionary computation technique - Particle Swarm Optimization (PSO) is used to tune the controller gains. A strategy comprising inertia control, coordinated operation of conventional generation units with wind energy and TCSC-FES has been proposed to enhance the frequency regulation which is effective in controlling low frequency oscillations as established by the simulation results.

Estimating solar irradiance using genetic programming technique and meteorological records

Solar irradiance is one of the most important parameters that need to be estimated and modeled before engaging in any solar energy project. This article describes a non-linear regression model based on genetic programming technique for estimating solar irradiance in a specific region in the United Arab Emirates. The genetic programming is an evolutionary computing technique that enables automatic search for complex solutions. The best nonlinear modeling function that can estimate the global solar radiation on horizontal will be developed taking into account measured meteorological data. A reference approach to model the solar radiation is first presented. An enhanced approach is then presented which consists of multi nonlinear functions of regression in a parallel structure where each function is designed to estimate the global solar irradiance in a specific seasonal period of the year. Statistical analysis measures have been used to evaluate the performance of the proposed approaches. The obtained results are comparable with the outcomes of models developed by other researchers in the field.

An analysis of association rule mining for classification based on artificial immune system

One of the most important and effective data mining techniques is association rule mining. Associative classification uses the principle of rule finding and technique of classification to generate a classifier for prediction. The rule search method is also computationally expensive for small support threshold values, which are critical for designing an efficient classifier. The artificial immune system (AIS) employs the powerful informational capacities of the immune system. The population-based search model combined with evolutionary computation techniques allows the artificial immune system clonal selection methodology to manage a complex search space. This study calculated accuracy across a variety of clonal characteristics and generations to assess the efficacy of the artificial immune system-based categorization method. The output of these systems is shown on several benchmark datasets. Based on the accuracy of the different clonal factors (0.1 to 0.9) and generations (10, 20, 30, 40, 50, and 60), a comparison study is performed. The accuracy is computed using four standard datasets. It is observed that in every dataset for several generations, the approach provides the maximum accuracy with a clonal factor of 0.4.

Feature Selection using Gravitational Search Algorithm for Biomedical Data

Analysis of medical data for disease prediction requires efficient feature selection techniques, as the data contains a large number of features. Researchers have used evolutionary computation (EC) techniques like genetic algorithms, particle swarm optimization etc. for FS and have found them to be faster than traditional techniques. We have explored a relatively new EC technique called gravitational search algorithm (GSA) for feature selection in medical datasets. This wrapper based method, that we have employed, using GSA and k-nearest neighbors reduces the number of features by an average of 66% and considerably improves the accuracy of prediction.

Particle swarm optimisation-based parameters optimisation of PID controller for load frequency control of multi-area reheat thermal power systems

The current study presents the load frequency control (LFC) of multi-area reheat thermal power system with proportional-integral-derivative (PID) controller. The interconnected control areas are provided with a single stage reheat turbine in all areas. The proportional gain (KP), integral gain (KI) and derivative gain (KD) values of the PID controller are simultaneously optimised using recent and powerful evolutionary computational intelligence technique, namely the particle swarm optimisation (PSO) algorithm. The superiority of the PSO-based PID controller has been proved by comparing its performance to recent modern optimisation techniques such as hill climbing (HC) algorithm and genetic algorithm (GA) tuned controllers for the same multi-area thermal power system. For the analysis, the time domain specification and 1% step load perturbation (1% SLP) are considered in thermal area 1. The simulation result showed that the proposed PSO-based PID controller provides superior dynamic response over other optimisation technique (HC and GA)-based PID controller.

Toward hybrid platform for evolutionary computations of hard discrete problems

Memetic agent-based paradigm, which combines evolutionary computation and local search techniques in one of promising meta-heuristics for solving large and hard discrete problem such as Low Autocorrellation Binary Sequence (LABS) or optimal Golomb-ruler (OGR). In the paper as a follow-up of the previous research, a short concept of hybrid agent-based evolutionary systems platform, which spreads computations among CPU and GPU, is shortly introduced. The main part of the paper presents an efficient parallel GPU implementation of LABS local optimization strategy. As a means for comparison, speed-up between GPU implementation and CPU sequential and parallel versions are shown. This constitutes a promising step toward building hybrid platform that combines evolutionary meta-heuristics with highly efficient local optimization of chosen discrete problems.

Hybrid Input Data Selection Technique for Neural Network Based Load Forecast Model

Selection of best model parameters is a key to building an effective neural network based prediction model. This paper focuses on short term load forecasting (STLF) framework emphasizing on a new hybrid data selection preprocessing and selection technique to enhance forecast accuracy. A Multilayer perceptron neural network (MLPNN) is used to predict the load demand of one week ahead. Because of the mapping and memorizing ability of the artificial neural network (ANN) towards the non-linear relations between input and output variables, they are widely used in predictive modeling, such as, electrical load demand prediction models. The non-linear behaviour of the load can only be narrated effectively by ANN, if a group of appropriate inputs is identified prior to the training process. A new input variable selection method based on statistical methods and evolutionary computation technique genetic algorithm (GA) is proposed in this research. A case study is developed to verify the efficacy of the optimized training dataset and input variable selection method. In this approach, we trained the ANN with conventional back propagation (BP) training algorithm with and without using the optimized and reduced dataset. Results show that this dataset selection method is effective and forecasts electrical load demand with reduced mean absolute percentage error (MAPE) and reduced training time.

An ACO-based approach to mine high-utility itemsets

High-utility itemset mining (HUIM) is a major contemporary data mining issue. It is different from frequent itemset mining (FIM), which only considers the frequency factor. HUIM applies both the quantity and profit factors to be used to reveal the most profitable products. Several previous approaches have been proposed to mine high-utility itemsets (HUIs) and most of them have to handle the exponential search space for discovering HUIs when the number of distinct items and the size of the database are both very large. Therefore, two evolutionary computation (EC) techniques, genetic algorithm (GA) and particle swarm optimization (PSO), were previously proposed to mine HUIs. In these studies, GAs and PSOs also could obtain the huge amount of high-utility items in a limitation time. In this paper, a novel algorithm based on the other evolutionary computation technique, ant colony optimization (ACO), is proposed to resolve this issue. Unlike GAs and PSOs, ACOs produce a feasible solution in a constructive way. They can avoid generating unreasonable solutions as much as possible. Thus, a well-defined ACO approach can always obtain suitable solutions efficiently. An ant colony system (ACS), which is extended from ACO and consists of high-utility itemset mining by ACS (HUIM-ACS), is proposed to efficiently find HUIs. In general, an EC algorithm cannot make sure the provided solution is the global optimal solution. But the designed HUIM-ACS algorithm maps the completed solution space into the routing graph and includes two pruning processes. Therefore, it guarantees that it obtains all of the HUIs when there is no candidate edge from the starting point. In addition, HUIM-ACS does not estimate the same feasible solution again in its process in order to avoid wasting computational resource. Substantial experiments on real-life datasets show that the proposed algorithm outperforms the other heuristic algorithms for mining HUIs in terms of the number of discovered HUIs, and convergence.

A model of Thai’s sky luminance distribution based on reduced CIE standard sky types

In this study, a method is used to develop a model for estimating luminance distributions of Thai’s sky. From the sky characterization, five out of the fifteen CIE standard skies are selected for the distribution estimation. This selection represents the minimum sky types that best fit to the distribution records of Bangkok (Thailand) and cover three sky conditions of clear, partly cloudy, and overcast. The developed model uses the five standard skies each to estimate prevailing luminance distributions within a particular range of insolation condition defined by lower and upper limit values of Perez’s clearness (ε) and sky brightness (Δ). In this scheme, a stochastic search technique of evolutionary computation was applied to optimize limit values for an accurate sky classification. Equations of optimal limit values were also formulated as part of the method. From the comparative evaluation with other sky models (i.e. Perez’s model, ASRC-CIE model, Harrison’s model, CIE-Si model, and Igawa’s all-sky model), the proposed model offers an alternative option for generating luminance distribution of Thai’s sky.

Wiring Economy of Pyramidal Cells in the Juvenile Rat Somatosensory Cortex.

Ever since Cajal hypothesized that the structure of neurons is designed in such a way as to save space, time and matter, numerous researchers have analyzed wiring properties at different scales of brain organization. Here we test the hypothesis that individual pyramidal cells, the most abundant type of neuron in the cerebral cortex, optimize brain connectivity in terms of wiring length. In this study, we analyze the neuronal wiring of complete basal arborizations of pyramidal neurons in layer II, III, IV, Va, Vb and VI of the hindlimb somatosensory cortical region of postnatal day 14 rats. For each cell, we search for the optimal basal arborization and compare its length with the length of the real dendritic structure. Here the optimal arborization is defined as the arborization that has the shortest total wiring length provided that all neuron bifurcations are respected and the extent of the dendritic arborizations remain unchanged. We use graph theory and evolutionary computation techniques to search for the minimal wiring arborizations. Despite morphological differences between pyramidal neurons located in different cortical layers, we found that the neuronal wiring is near-optimal in all cases (the biggest difference between the shortest synthetic wiring found for a dendritic arborization and the length of its real wiring was less than 5%). We found, however, that the real neuronal wiring was significantly closer to the best solution found in layers II, III and IV. Our studies show that the wiring economy of cortical neurons is related not to the type of neurons or their morphological complexities but to general wiring economy principles.

Multilevel Thresholding with Membrane Computing Inspired TLBO

The selection of optimal thresholds is still a challenging task for researchers in case of multilevel thresholding. Many swarm and evolutionary computation techniques have been applied for obtaining optimal values of thresholds. The performance of all these computation techniques is highly dependent on proper selection of algorithm-specific parameters. In this work, a new hybrid optimization technique, membrane computing inspired teacher-learner-based-optimization (MCTLBO), is proposed which is based on the structure of membrane computing (MC) and teacher-learner-based-optimization (TLBO) algorithm. To prove the efficacy of proposed algorithm, it is applied to solve multilevel thresholding problem in which the Kapur's entropy criterion is considered as figure-of-merit. In this experiment, four benchmark test images are considered for multilevel thresholding. The optimal values of thresholds are obtained using TLBO, MC and particle swarm optimization (PSO) in addition to proposed algorithm to accomplish the comparative study. To support the superiority of proposed algorithm over others, various quantitative and qualitative results are presented in addition to statistical analysis.

Optimal design of linear phase multi-band stop filters using improved cuckoo search particle swarm optimization

Filtering and design of suitable filters are the basic requirement in most of modern signal processing networks. In this manuscript, linear phase multiband stop filters (LPMBSFs) are designed using a robust hybrid metaheuristic algorithm called improved cuckoo search particle swarm optimization (ICSPSO) for searching desired impulse responses of the filters. Generally LPMBSFs are considered for simultaneous processing of group of spectra widely used in digital communication and signal processing applications. The window method and the simple frequency sampling method of designing linear phase multiband filters (LPMBFs) have no precise control over the stop band and pass band cut-off frequencies. Thus in order to improve the design strategy of the LPMBSF, optimum design criterion is devised where the error between ideal frequency response and practical frequency response is reduced by incorporating the efficient modern swarm and global evolutionary computation technique. The ICSPSO technique is such an excellent hybrid global random search technique for error minimization, and is structured incorporating the advantages of cuckoo search algorithm into particle swarm optimization (PSO) while the global searching capability is further accelerated by tuning the mutation operation of differential evolution (DE) technique. The quality of the proposed ICSPSO based filter design has been compared with other prominent optimal method of design such as hybrid cuckoo search particle swarm optimization (CSPSO), improved cuckoo search (ICS) optimization, and PSO.

Antisway Control for a Rotary Crane by Using Evolutionary Computation

[abstFig src='/00280005/05.jpg' width='250' text='Photograph of the experimental setup' ] We present a simple antisway control method for a rotary crane, whose load can move in a two-dimensional plane. In particular, we investigate the suppression of residual sway motion of a rotary crane with a boom that performs point-to-point motion. In the proposed method, we attempt to generate the trajectory of the boom using a combination of polynomial and cycloidal functions. The profile of the generated trajectory depends on the coefficients of the polynomial function. Thus, it is necessary to tune the coefficients to minimize the sway motion in the two-dimensional plane as much as possible. We adopt a particle swarm optimization algorithm, an evolutionary computation technique, to tune the coefficients and then obtain the optimal trajectory. By rotating the boom along the optimal trajectory, the two-dimensional residual sway motion is suppressed, i.e., an open-loop control is realized. The effectiveness and feasibility of the proposed control scheme is demonstrated via simulations and experiments.