Showing papers on "Evolutionary programming published in 2020"

Evolutionary algorithms and their applications to engineering problems

Abstract: The main focus of this paper is on the family of evolutionary algorithms and their real-life applications. We present the following algorithms: genetic algorithms, genetic programming, differential evolution, evolution strategies, and evolutionary programming. Each technique is presented in the pseudo-code form, which can be used for its easy implementation in any programming language. We present the main properties of each algorithm described in this paper. We also show many state-of-the-art practical applications and modifications of the early evolutionary methods. The open research issues are indicated for the family of evolutionary algorithms.

Particle swarm optimisation with opposition learning-based strategy: an efficient optimisation algorithm for day-ahead scheduling and reconfiguration in active distribution systems

Abstract: In operation of active electric distribution networks, optimal configuration and schedule of distributed generation and reactive power resources are determined. This represents a formidable multi-modal constrained optimisation problem with discrete decision variables. Metaheuristics are the most common approaches for solving this problem. However, due to its multi-modal nature, metaheuristics commonly converge prematurely into local optima and cannot find near-global solutions. In this research, a new particle swarm optimisation (PSO) variant is put forward for finding optimal configuration and schedule of distributed generation and reactive power resources in distribution systems including both dispatchable and renewable distributed energy resources. In the proposed PSO variant, opposition-based learning concept is incorporated into PSO which reduces premature convergence probability through enhancement of swarm leaders. The results of the proposed opposition-based PSO in IEEE 69 bus system indicate its outperformance over conventional PSO, time-varying acceleration coefficient PSO, fractal optimisation algorithm and evolutionary programming.

State-of-art of genetic programming applications in water-resources systems analysis.

Abstract: Evolutionary algorithms (EAs) have become competitive solvers of a wide variety of water-resources optimization problems. Genetic programming (GP) has become a leading EA since its inception in 1985. This paper reviews the state-of-the-art of GP and its applications in water-resources systems analysis. A comprehensive knowledge about GP’s theory and modeling approach is essential for its successful application in water-resources systems analysis. This review presents variants of GP that have been proven useful in various applications to water resources problems. Several examples of applications of GP in water-resources systems analysis are herein presented. This review reveals GP’s capability and superiority compared to other conventional methods, which makes it suitable for solving a wide variety of water-related problems including rainfall-runoff modeling, streamflow sediment prediction, flood prediction and routing, evaporation and evapotranspiration forecasting, reservoir operation, groundwater modeling, water quality modeling, water demand forecasting, and water distribution systems.

Evolutionary Programming Based Deep Learning Feature Selection and Network Construction for Visual Data Classification

Abstract: Convolutional Neural Network (CNN) models and many accessible large-scale public visual datasets have brought lots of research work to a remarkable new stage Benefited from well-trained CNN models, small training datasets can learn comprehensive features by utilizing the preliminary features from transfer learning However, the performance is not guaranteed when taking these features to construct a new model, as the differences always exist between the source and target domains In this paper, we propose to build an Evolution Programming-based framework to address various challenges This framework automates both the feature learning and model building processes It first identifies the most valuable features from pre-trained models and then constructs a suitable model to understand the characteristic features for different tasks Each model differs in numerous ways Overall, the experimental results effectively reach optimal solutions, demonstrating that a time-consuming task could also be conducted by an automated process that exceeds the human ability

Performance Analysis of Self Adaptive Equalizers using EPLMS Algorithm

Abstract: Digital communication has become an important part of our lives and technology has been undergoing advancements. The main two problems faced in digital communication is noise and inter-symbol interference (ISI). The IS I is induced due to channel characteristics, which is time-varying and unknown. Hence an adaptive channel equalizer is used to inverse the effect channel had on the signal to get back the initial information. There are many adaptive algorithms to update the coefficients of equalizers, evolutionary algorithms are used in this paper to do so. The two algorithms used before are particle swarm optimization (PSO) and conventional differential evolution (DE). The newest algorithm is the Evolutionary Programming Least Mean Square Algorithm (EPLMS) this gives a better solution faster.

Development and Evaluation of an Evolutionary Programming-Based Tropical Cyclone Intensity Model

Abstract: A statistical–dynamical tropical cyclone (TC) intensity model is developed from a large ensemble of algorithms through evolutionary programming (EP). EP mimics the evolutionary principles o...

Quantum-Behaved Bat Algorithm for Solving the Economic Load Dispatch Problem Considering a Valve-Point Effect

Abstract: Quantum computing-inspired metaheuristic algorithms have emerged as a powerful computational tool to solve nonlinear optimization problems. In this paper, a quantum-behaved bat algorithm (QBA) is implemented to solve a nonlinear economic load dispatch (ELD) problem. The objective of ELD is to find an optimal combination of power generating units in order to minimize total fuel cost of the system, while satisfying all other constraints. To make the system more applicable to the real-world problem, a valve-point effect is considered here with the ELD problem. QBA is applied in 3-unit, 10-unit, and 40-unit power generation systems for different load demands. The obtained result is then presented and compared with some well-known methods from the literature such as different versions of evolutionary programming (EP) and particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), simulated annealing (SA) and hybrid ABC_PSO. The comparison of results shows that QBA performs better than the above-mentioned methods in terms of solution quality, convergence characteristics and computational efficiency. Thus, QBA proves to be an effective and a robust technique to solve such nonlinear optimization problem.

Dynamic economic dispatch incorporating renewable energy sources and pumped hydroenergy storage

Abstract: Due to mounting infiltration of solar and wind energy sources, it becomes essential to investigate its brunt on the dynamic economic dispatch. Here, solar–wind–thermal system integrating pumped-storage hydraulic unit has been considered. This work recommends chaotic fast convergence evolutionary programming (CFCEP) rooted in Tent equation for solving dynamic economic dispatch problem incorporating renewable energy sources and pumped-storage hydraulic unit. Chaotic sequences increase the exploitation ability in the searching space and enhance the convergence property. In the recommended technique, chaotic sequences have been pertained for acquiring the dynamic scaling factor setting in fast convergence evolutionary programming (FCEP). The efficiency of the recommended technique is revealed on two test systems. Simulation outcomes of the suggested technique have been matched up to those acquired by FCEP, differential evolution and particle swarm optimization. It has been observed from the comparison that the recommended CFCEP technique has the capability to confer with better quality solution.

Index-based transmission for distributed generation in voltage stability and loss control incorporating optimization technique

Abstract: The integration of Distributed Generation (DG) in a distribution network may significantly affect distribution performance. With the penetration of DG, voltage security is no longer an issue in the transmission network. This paper presents a study of Distributed Generation on the IEEE 26-Bus Reliability Test System (RTS) with the use of Fast Voltage Stability Index (FVSI) for determining its location and incorporated with Grasshopper Optimization Algorithm (GOA) to optimize the sizing of the DG. The study emphasizes the power loss of the system in which a comparison between Evolutionary Programming (EP) and Grasshopper Optimization Algorithm is done to determine which optimization technique gives an optimal result for the DG solution. The results show that the proposed algorithm is able to provide a slightly better result compared to EP.

Modelling and measurement in synthetic biology

Abstract: Synthetic biology applies engineering principles to make progress in the study of complex biological phenomena. The aim is to develop understanding through the praxis of construction and design. The computational branch of this endeavour explicitly brings the tools of abstraction and modularity to bear. This thesis pursues two distinct lines of inquiry concerning the application of computational tools in the setting of synthetic biology. One thread traces a narrative through multi-paradigm computational simulations, interpretation of results, and quantification of biological order. The other develops computational infrastructure for describing, simulating and discovering, synthetic genetic circuits. The emergence of structure in biological organisms, morphogenesis, is critically important for understanding both normal and pathological development of tissues. Here, we focus on epithelial tissues because models of two dimensional cellular monolayers are computationally tractable. We use a vertex model that consists of a potential energy minimisation process interwoven with topological changes in the graph structure of the tissue. To make this interweaving precise, we define a language for propagators from which an unambiguous description of the simulation methodology can be constructed. The vertex model is then used to reproduce laboratory results of patterning in engineered mammalian cells. The assertion that the claim of reproduction is justified is based on a novel measure of structure on coloured graphs which we call path entropy. This measure is then extended to the setting of continuous regions and used to quantify the development of structure in house mouse (Mus musculus) embryos using three dimensional segmented anatomical models. While it is recognised that DNA can be considered a powerful computational environment, it is far from obvious how to program with nucleic acids. Using rule-based modelling of modular biological parts, we develop a method for discovering synthetic genetic programs that meet a specification provided by the user. This method rests on the concept of annotation as applied to rule-based programs. We begin with annotating rules and proceed to generating entire rule-based programs from annotations themselves. Building on those tools we describe an evolutionary algorithm for discovering genetic circuits from specifications provided in terms of probability distributions. This strategy provides a dual benefit: using stochastic simulation captures circuit behaviour at low copy numbers as well as complex properties such as oscillations, and using standard biological parts produces results that are implementable in the laboratory.

An Improved Underwater Electric Field-Based Target Localization Combining Subspace Scanning Algorithm And Meta-EP PSO Algorithm

Abstract: In this paper, we propose an improved three-dimensional underwater electric field-based target localization method. This method combines the subspace scanning algorithm and the meta evolutionary programming (meta-EP) particle swarm optimization (PSO) algorithm. The subspace scanning algorithm is applied as the evaluation function of the electric field-based underwater target locating problem. The meta-EP PSO method is used to select M elite particles by the q-tournament selection method, which could effectively reduce the computational complexity of the three-dimensional underwater target localization. Moreover, the proposed meta-EP PSO optimization algorithm can avoid subspace scanning trapping into local minima. We also analyze the positioning performance of the uniform circular and cross-shaped electrodes arrays by using the subspace scanning algorithm combined with meta–EP PSO. According to the simulation, the calculation amount of the proposed algorithm is greatly reduced. Moreover, the positioning accuracy is effectively improved without changing the positioning accuracy and search speed.

Extrapolation With Gaussian Random Processes and Evolutionary Programming

Abstract: Emulation plays an indispensable role in engineering design. However, the majority of emulation methods are formulated for interpolation purposes and their performance significantly deteriorates in extrapolation. In this paper, we develop a method for extrapolation by integrating Gaussian processes (GPs) and evolutionary programming (EP). Our underlying assumption is that there is a set of free-form parametric bases that can model the data source reasonably well. Consequently, if we can find these bases via some training data over a region, we can do predictions outside of that region. To systematically and efficiently find these bases, we start by learning a GP without any parametric mean function. Then, a rich dataset is generated by this GP and subsequently used in EP to find some parametric bases. Afterwards, we retrain the GP while using the bases found by EP. This retraining essentially allows to validate and/or correct the discovered bases via maximum likelihood estimation. By iterating between GP and EP we robustly and efficiently find the underlying bases that can be used for extrapolation. We validate our approach with a host of analytical problems in the absence or presence of noise. We also study an engineering example on finding the constitutive law of a composite microstructure.

Optimal Scheduling of Hydrothermal Plant Using Particle Swarm Optimization

Abstract: This paper presents a particle swarm optimization (PSO) technique for solving hydrothermal problem by considering demand constraint, thermal generator constraint, reservoir capacity constraint, hydrogenerator constraint and water discharge constraint. The possibility examination of the proposed method is exhibited on one hydro-plant and a steam plant by utilizing MATLAB. The comparison of the simulation results shows that PSO has a better performance than the other programs like gravitational search algorithm, genetic algorithm, classical evolutionary programming, simulated annealing approach and fast evolutionary programming.

Supervised evolutionary programming based technique for multi-DG installation in distribution system

Abstract: Installing DG in network system, has supported the distribution system to provide the increasing number of consumer demand and load, in order to achieve that this paper presents an efficient and fast converging optimization technique based on a modification of traditional evolutionary programming method for obtain the finest optimal location and power loss in distribution systems. The proposed algorithm that is supervised evolutionary programming is implemented in MATLAB and apply on the 69-bus feeder system in order to minimize the system power loss and obtaining the best optimal location of the distributed generators.

Economic Load Dispatch with Emission and Line Constraints using Biogeography Based Optimization Technique

Abstract: Economic load dispatch is the economic loading of different generating units for variable load conditions of any power system. Emission contributions of conventional thermal power plants plays major role in the overall air pollution. The total emissions need to satisfy the allowable limits of the emission. This paper proposes the load scheduling for combined economic emission and load dispatch (CEELD) problems. In this paper, Biogeography Based optimization (BBO) technique is employed to estimate the fuel cost and emission of all the generating units. The aim of this paper is to minimize the fuel cost and emission of all the units for a given load. Result obtained by BBO are compared with the results obtained by different other optimization techniques such as genetic algorithm (GA), particle swarm optimization (PSO), evolutionary programming (EP) and differential evolution (DE) with respect to total fuel cost, solution time and convergence criteria. The proposed BBO technique is employed on a standard IEEE 30 bus - 6 generator power system to obtain the CEELD solution. The BBO algorithm is carried out in MATLAB environment. The solutions obtained by BBO technique are quite encouraging.

Distribution network reconfiguration based on artificial network reconfiguration for variable load profile

Abstract: Abstract: Network reconfiguration is a process to change the open-switches in distribution system for a minimum power loss. In the past, metaheuristic techniques were applied widely for network reconfiguration with consideration of a fixed loading profile. When the loading changes, the current configuration may not be the optimal one. Thus, the technique needs to be executed to find a new optimal configuration based on the latest loading. The process is time-consuming since metaheuristic techniques commonly require high computational times and produces inconsistent results. Therefore, this paper proposes a network reconfiguration technique based on artificial neural network (ANN) for variable loading conditions. The proposed ANN model is tested on IEEE 33-bus, IEEE 69-bus, and IEEE-118 bus systems. The test results indicate the efficiency of the proposed technique in three aspects: processing time, simple structure, and high accuracy.

Shuffled Frog-Leaping Programming for Solving Regression Problems

Abstract: There are various automatic programming models inspired by evolutionary computation techniques. Due to the importance of devising an automatic mechanism to explore the complicated search space of mathematical problems where numerical methods fails, evolutionary computations are widely studied and applied to solve real world problems. One of the famous algorithm in optimization problem is shuffled frog leaping algorithm (SFLA) which is inspired by behaviour of frogs to find the highest quantity of available food by searching their environment both locally and globally. The results of SFLA prove that it is competitively effective to solve problems. In this paper, Shuffled Frog Leaping Programming (SFLP) inspired by SFLA is proposed as a novel type of automatic programming model to solve symbolic regression problems based on tree representation. Also, in SFLP, a new mechanism for improving constant numbers in the tree structure is proposed. In this way, different domains of mathematical problems can be addressed with the use of proposed method. To find out about the performance of generated solutions by SFLP, various experiments were conducted using a number of benchmark functions. The results were also compared with other evolutionary programming algorithms like BBP, GSP, GP and many variants of GP.

Short Term Hydro-Thermal Scheduling Using Backtracking Search Algorithm

Abstract: In this article, a new optimization technique, the backtracking search algorithm (BSA), is proposed to solve the hydrothermal scheduling problem. The BSA has mainly unique five steps: (i) Initializ...

Application of the Evolutionary Programming for Indirect Evaluation of Blood Pressure based on Heart Rate Variability Parameters

Abstract: The article presents results of testing the hypothesis that it is possible to predicting the blood pressure using features of the heart rate variability. The hypothesis is based on the previous results associated with the task of arterial hypertension diagnostics by means of heart rate variability. For data, one of the PhysioNet collection is used, which contains simultaneously recorded biomedical signals. The evolutionary programming algorithm, which was successfully used in previous tasks, is updated to solve regression task. The presented results show that evolutionary programming can find such set of heart rate variability parameters that can be used for prediction of blood pressure, taking into account calibration data. Comparison with the state-of-art has been done. It was noted that using heart rate variability is a distinctive feature of the proposed approach. It is discussed that some additional data, such as gender, age, anthropomorphic features, as well as parameters of the raw electrocardiography signals can be used to improve results. The future work plans are discussed.

Hybridization of Evolutionary and Swarm Intelligence Algorithms for improved performance: A case study with TSP problem

Abstract: This paper conducts the hybridization of Swarm intelligence and Evolutionary Algorithm for Continuous and Discrete optimization. Optimization is the process of selecting the best element by following some rules and criteria from some set of available alternatives. Function optimization means finding the best available value of some given objective function in a defined domain. In this work we have proposed an innovative approach, by hybridizing Genetic Algorithm (GA) and Swarm Intelligence Algorithm (SIA). In this paper work we have implemented one evolutionary programming based algorithm - Improved First Evolutionary Programming (IFEP) and one swarm intelligence algorithm - Ant Colony Optimization (ACO). We have also used Travelling Salesman Problem (TSP) as a discrete problem. We have implemented both GA and ACO also to solve the Travelling Salesman Problem. We have compared the result produced by IFEP and ACO for Continuous Optimization. From the comparative study we have found that ACO is the better among the two. We also have compared the result produced by GA and ACO for Discrete Optimization and from the comparative study we have found that ACO often works better. We have conducted some experiments to optimize the parameters of ACO and GA and the amount of exploration and exploitation needed for ACO to produce the best result. using the best found parameter we have implemented a hybrid of Genetic Algorithm and Swarm Intelligence Algorithm and tested it with different strategies. Then we have conducted a comparative study between the hybrid and two other conventional Genetic and Swarm Intelligence Algorithms to observe the performance of our proposed hybrid algorithm. In some cases we have observed better performance from our proposed hybrid algorithm.

Tap changer optimisation using embedded differential evolutionary programming technique for loss control in power system

Abstract: Over-compensation and under-compensation phenomena are two undesirable results in power system compensation. This will be not a good option in power system planning and operation. The non-optimal values of the compensating parameters subjected to a power system have contributed to these phenomena. Thus, a reliable optimization technique is mandatory to alleviate this issue. This paper presents a stochastic optimization technique used to fix the power loss control in a high demand power system due to the load increase, which causes the voltage decay problems leading to current increase and system loss increment. A new optimization technique termed as embedded differential evolutionary programming (EDEP) is proposed, which integrates the traditional differential evolution (DE) and evolutionary programming (EP). Consequently, EDEP was for solving optimizations problem in power system through the tap changer optimizations scheme. Results obtained from this study are significantly superior compared to the traditional EP with implementation on the IEEE 30-bus reliability test system (RTS) for the loss minimization scheme.

Modified sub-gradient based combined objective technique and evolutionary programming approach for economic dispatch involving valve-point loading, enhanced prohibited zones and ramp rate constraints

Abstract: A security constrained non-convex power dispatch problem with prohibited operation zones and ramp rates is formulated and solved using an iterative solution method based on the feasible modified sub-gradient algorithm (FMSG). Since the cost function, all equality and inequality constraints in the nonlinear optimization model are written in terms of the bus voltage magnitudes, phase angles, off-nominal tap settings, and the Susceptance values of static VAR (SVAR) systems, they can be taken as independent variables. The actual power system loss is included in the current approach and the load flow equations are inserted into the model as the equality constraints. The proposed modified sub gradient based combined objective technique and evolutionary programming approach (MSGBCAEP) with as decision variable and cost function as fitness function is tested on the IEEE 30-bus 6 generator test case system. The absence of crossover operation and adoption of fast judicious modifications in initialization of parent population, offspring generation and normal distribution curve selection in EP enables the proposed MSGBCAEP approach to ascertain global optimal solution for cost of generation and emission level shown in Table 6 and displayed in Figure 2 and Figure 3 respectively.

Exploitation and Exploration Analysis of Elitist Evolutionary Algorithms: A Case Study

Abstract: Known as two cornerstones of problem solving by search, exploitation and exploration are extensively discussed for implementation and application of evolutionary algorithms (EAs). However, only a few researches focus on evaluation and theoretical estimation of exploitation and exploration. Considering that exploitation and exploration are two issues regarding global search and local search, this paper proposes to evaluate them via the success probability and the one-step improvement rate computed in different domains of integration. Then, case studies are performed by analyzing performances of (1+1) random univariate search and (1+1) evolutionary programming on the sphere function and the cheating problem. By rigorous theoretical analysis, we demonstrate that both exploitation and exploration of the investigated elitist EAs degenerate exponentially with the problem dimension $n$. Meanwhile, it is also shown that maximization of exploitation and exploration can be achieved by setting an appropriate value for the standard deviation $\sigma$ of Gaussian mutation, which is positively related to the distance from the present solution to the center of the promising region.

Multi-Objective Evolutionary Programming for Developing Recommender Systems based on Collaborative Filtering

Abstract: In the era of internet, several online platforms offer many items to users. Users could spend a lot of time to find (or not) some items they are interested, sometimes, they will probably not find the desired items. An effective strategy to overcome this problem is a recommender system, one of the most popular applications of machine learning. Recommender systems select most appropriate items to an specific user based on previous information between items and users, and they are developed using diffeent approaches. One of the most successful approach for developing recommender systems is collaborative filtering, which can filter out items that a user might like based on reactions of users with similar profiles. Often, traditional recommender systems only consider precision as evaluation metric of performance, however, others metrics (like recall, diversity, novelty, etc) are also important. Unfortunately, some metrics are conflicting, e.g., precision impacts negatively on other metrics. This paper presents a multi-objective evolutionary programming method for developing a recommender system, which is based on a new collaborative filtering technique, while maximizes the recall for a given precision, The new collaborative filtering technique uses three components for recommending an item to a user: 1) clustering of users; 2) a previous memory-based prediction; and 3) five decimal parameters (threshold average clustering, threshold penalty, threshold incentive, weight attached to average clustering and weight attached to Pearson correlation). The multiobjective evolutionary programming optimizes the clustering of users and the five decimal parameters, while, it searches maximizes both similarity precision and recall objectives. A comparison between the proposed method and a previous nonevolutionary method shows that the proposed method improves precision and recall metric on a benchmark database.

New Advances in Artificial Intelligence for the New Generation

Abstract: Journal of Swarm Intelligence and Evolutionary Computation is a world forum for the publication of papers in Artificial Intelligence; Robotics; Modeling & Analysis of swarm particle optimization; Swarm Intelligence; Evolutionary programming and Evolutionary Genetics; Genetic Algorithm & Genetic Programming; Ant colony Optimization; Bacterial Forging; Artificial Life & Digital Organisms; Bioinformatics; Evolutionary Computation; Artificial Immune System; Computing; Nano computing; Computational intelligence, etc. Swarm Intelligence journals are at higher echelons that enhance the intelligence and knowledge dissemination on topics closely associated with Swarm Intelligence. Computational methods in synthetic biology plays a major role in this journal.

Evolutionary Programming Based Deep Feature and Model Selection for Visual Data Classification

Abstract: Deep Learning (DL) has made significant changes to a large number of research areas in recent decades. For example, several astonishing Convolutional Neural Network (CNN) models have been built by researchers to fulfill image classification needs using large-scale visual datasets successfully. Transfer Learning (TL) makes use of those pre-trained models to ease the feature learning process for other target domains that contain a smaller amount of training data. Currently, there are numerous ways to utilize features generated by transfer learning. Pre-trained CNN models prepare mid-/high-level features to work for different targeting problem domains. In this paper, a DL feature and model selection framework based on evolutionary programming is proposed to solve the challenges in visual data classification. It automates the process of discovering and obtaining the most representative features generated by the pre-trained DL models for different classification tasks.

Numerical Simulation of Optimal Power Flow in IEEE-118 Bus System Using Improved Cuckoo Search Algorithm

Abstract: OPF algorithms are a major research issue for the efficient monitoring and preparing of the power systems. To minimise the objective function, optimum power flow is carried out. It can be a single value objective or multiple objective functions. This objective function. In current research, the optimum power flow has been implemented to minimize fuel costs while fulfilling constraints including voltages, generator power outputs maintained in accordance with the prescribed limit. Any other target could be used based on interest and needs of the utility. Various OPF researchers, such as linear programming, non-linear programming, quadratic programming, newton-based techniques, Parametric and interior points approaches, have in the past incorporated a number of simplified network models. The drawbacks of conventional algorithms such as the use of soft computing technologies to perform the optimization solution have attracted attention. Thus, soft computer-based optimization techniques are essential to overcome these drawbacks. A wide range of advanced techniques for optimization are proposed in literature for solving OPF problem, e.g. evolutionary programming, genetic algorithm, PSO algorithm. In this study, we have introduced improved cuckoo search algorithm to reduce costs while maintaining limitations. Applied to the IEEE-118 bus system is the proposed algorithm. Significant improvement is seen after the application of proposed system.