Evolutionary Computation Techniques Research Articles

Particle swarm optimization for performance enhancement of cadmium telluride thin film solar cells by embedded nano-grating structures with a plasmonic metal coating layer

As the demand for energy in world is increasing rapidly and there is pursuit for renewable energy sources which is cheap, easy to generate and requires low maintenance, solar energy is a top contender. The world is in dire need of photovoltaic solar cells that can aid in keeping up with the hiking energy demands. However, thin film solar cells (TFSCs) which are developed for cost effectivity, suffer in performance due to reduced thickness. Therefore, this computational study uses Finite-Difference Time-Domain (FDTD) and delves into the scope of using a 1-D nano-grating structure embedded into absorbing layer of cadmium telluride TFSC to enhance the optoelectronic performance. A unique nano-grating structure with SiO2 at its core and aluminium coating aims to enhance the performance of CdTe TFSC with cost effectivity and ease of fabricability as the main motifs. Additionally, an evolutionary computational technique, Particle Swarm Optimization (PSO), was used to determine the optimal parameters of nano-gratings on CdTe TFSCs, i.e., nano-grating height, angle, duty cycle, aluminium coating thickness and grating substrate thickness. The PSO algorithm resulted in a nano-grating structure that yielded an efficiency increase of 52.86% and increase in short-circuit current density of 25.45% with respect to bare CdTe TFSCs. Subsequently, the performance of the optimal nano-grating structure was also observed to be insensitive to variation of wide range of incidence angle of light, a key feature preferred for versatile application of TFSCs.

Particle Swarm Optimization for Efficiently Evolving Deep Convolutional Neural Networks Using an Autoencoder-Based Encoding Strategy

Deep convolutional neural networks (DCNNs) have achieved surpassing success in the field of computer vision, and a number of elaborately designed networks refresh the performance records in benchmark datasets. Recently, evolutionary neural architecture search (ENAS) has become an emerging area, which could employ an evolutionary computation (EC) technique to automatically construct promising network architectures without human intervention. However, existing algorithms still have limitations: most standard EC approaches cannot process flexible-length architecture representations directly, and most fitness evaluation processes suffer from the exhibitive computational cost and the unreliable prediction results. To overcome these shortcomings, we propose an efficient particle swarm optimization (PSO)-based neural architecture search algorithm to search for appropriate dense blocks on image classification tasks, named EAEPSO. EAEPSO addresses the first limitation by designing an autoencoder to encode variable-length network representations as fixed-length latent vectors, which converts the original search space to a latent space that can facilitate the downstream search. Besides, an efficient and effective hierarchical fitness evaluation method is designed to guide the search process to address the second limitation. The experimental results show that EAEPSO is a very competitive ENAS algorithm that achieves an error rate of 2.74% on CIFAR-10 and 16.17% on CIFAR-100, and reduces the computational cost from hundreds or thousands of GPU-days to only 2.2 and 4 GPU-days, respectively. Further analyses investigate the reduced training data’s effect and confirm the effectiveness of both the proposed autoencoder and the proposed hierarchical fitness evaluation method.

A Two-Stage Robust Optimization for Reliable Logistics Network Design via Evolutionary Computation

This paper presents a novel two-stage robust optimization model for designing a dependable logistics network that integrates evolutionary computation techniques. The proposed model considers both the normal and disrupted states of the logistics network and seeks to reduce the overall network cost and operating time in different disruption situations. The challenge is a multi-objective optimization problem addressed using a hybrid evolutionary method that combines the advantages of the non-dominated sorting genetic algorithm with the large neighborhood search heuristic. Numerical experiments are conducted on various test instances to demonstrate the effectiveness and efficiency of the proposed model and algorithm. The results show that the proposed algorithm can generate robust and reliable logistics network designs resilient to disruptions and uncertainties, leading to significant improvements in logistics performance and cost savings compared to traditional methods.

Research on concept drift algorithm based on evolutionary computation

Concept drift (CD) in data streams can significantly impact the performance and stability of data stream classification algorithms, diminishing the generalization capabilities of integrated learning models. This paper addresses CD issues in dichotomous data streams by introducing a novel modeling approach that leverages evolutionary computation techniques. The method entails grouping base learners based on their performance within a sliding window and then evolving the base learning periods using evolutionary techniques. Furthermore, the concept of “gene flow” is introduced to enhance diversity among base learners and improve CD prediction performance. Experimental results on real and artificial datasets demonstrate the superior comprehensive performance of the proposed method. Specifically, the BCDECA algorithm outperforms other similar methods, excelling in accuracy, diversity, convergence rate, and robustness on a range of datasets.

A Global and Local Surrogate-Assisted Genetic Programming Approach to Image Classification

Genetic programming (GP) has achieved promising performance in image classification. However, GP-based methods usually require a long computation time for fitness evaluations, posing a challenge to real-world applications. Surrogate models can be efficiently computable approximations of expensive fitness evaluations. However, most existing surrogate methods are designed for evolutionary computation techniques with a vector-based representation consisting of numerical values, thus cannot be directly used for GP with a tree-based representation consisting of functions/operators. The variable sizes of GP trees further increase the difficulty of building the surrogate model for fitness approximations. To address these limitations, we propose a new surrogate-assisted GP approach including global and local surrogate models, which can accelerate the evolutionary learning process and achieve competitive classification performance simultaneously. The global surrogate model can assist GP in exploring the entire search space, while the local surrogate model can speed up convergence and further improve performance. Furthermore, a new surrogate training set is constructed to assist in establishing the relationship between the GP tree and its fitness, and effective surrogate models can be built accordingly. Experimental results on ten datasets of varying difficulty show that the new approach significantly reduces the computational cost of the GP-based method without sacrificing the classification accuracy. The comparisons with other state-of-the-art methods also demonstrate the effectiveness of the new approach. Further analysis reveals the significance of the global and local surrogates and the new surrogate training set on improving or maintaining the performance of the proposed approach while reducing the computational cost.

Generation Control of Multi-Area Multi-Source Power System using Fire-Fly Algorithm

This article presents automatic generation control (AGC) of an interconnected three area power system having different production sources such as hydropower, thermal and gas power plants. The control areas are provided with single reheat turbine and generation rate constraints of 3%/min. An 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 robust and powerful evolutionary computational technique Firefly Algorithm (FFA). 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. The system dynamic performances are studied with 1% and 2% step load perturbation in Area1. Sensitivity analysis reveals that the FFA 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.

An automated design framework for composite mechanical metamaterials and its application to 2D pentamode materials

Until relatively recently most mechanical metamaterial classes being studied have been composed of a single solid constituent phase and design has focused almost exclusively on structural geometry. Additional design dimensions can be introduced by accepting heterogeneity and varying materiality, i.e., allowing mechanical properties to vary across the metamaterial's unit cells or even from cell to cell in the metamaterial domain, creating composite metamaterials. This higher dimensionality significantly expands the effective property envelope, but the additional complexity also presents a significant hurdle. To overcome the design challenge, an automated design framework is proposed that leverages modern evolutionary computation techniques, combined with finite element analysis for fitness evaluation, to a discretized or voxelated design domain. However, this approach introduces stochastic and statistical aspects to the design process, which requires additional processing to successfully extract useful solutions. A case study is presented in which the proposed automated design framework is used to generate 2D structures that exhibit pentamode-like behavior. Pentamode metamaterials, which are best known for extreme bulk-to-shear modulus ratios (B/G), offer unique control over effective elastic properties and make for a particularly interesting test case. The evolutionary objective was defined as maximizing B/G over a voxelated square 2D domain. It was found that the evolutionary process converges to a solution relatively rapidly, generally in less that a hundred generations. B/G ratio values of 10,000 and more were obtained, largely exceeding those commonly found in the literature for experimental pentamode metamaterials. These generated designs feature reduced stress concentrations due to the elimination of point-like connections between lattice struts, which addresses a key practical limitation of diamond lattice pentamodes. It was observed that whatever the initial variety of elastic moduli values across the voxels of the design domain, as evolution progressed this variety collapsed to a much smaller number, most often a binary composite of very stiff voxels with a limited number of much softer voxels at key locations that acted as hinges.

COMPARATIVE ANALYSIS OF FAULT DIAGNOSIS IN POWER SYSTEM USING EVOLUTIONARY COMPUTATION AND MACHINE LEARNING

This paper provides a comparative analysis of fault diagnosis in power systems using evolutionary computation and machine learning techniques. Power system faults, including line-to-ground, line-to-line, and three-phase faults, pose significant challenges to the stability and reliability of electrical grids. Efficient and accurate fault diagnosis is crucial for minimizing downtime and maintaining optimal power delivery. This research evaluates the performance of various evolutionary computation algorithms such as genetic algorithms, particle swarm optimization and differential evolution alongside machine learning methods including decision trees, support vector machines, and k-nearest neighbor, in diagnosing power system faults. The study compares the accuracy, speed, and reliability of these methods in detecting and classifying faults under different system conditions. The findings highlight the strengths and limitations of EC and ML-based fault diagnosis techniques in power systems, providing valuable insights for researchers and practitioners in the field. Key Words: Fault diagnosis, Power system, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution(DE) , Support Vector Machine (SVM) , Decision Tree (DT), K-Nearest Neighbors (KNN).

A Survey on Unbalanced Classification: How Can Evolutionary Computation Help?

Unbalanced classification is an essential machine learning task, which has attracted widespread attention from both the academic and industrial communities due mainly to its broad applications. Evolutionary computation (EC) has contributed greatly to unbalanced classification. However, to the best of our knowledge, there have not been any comprehensive investigations on the strengths and weaknesses of alternative EC methods in addressing various challenging problems in unbalanced classification. This paper reviews the literature which utilize EC techniques for unbalanced classification, with the aim of revealing the contributions of EC to unbalanced classification, providing an overview of recent advances, and identifying limitations of existing works. In addition, we present a series of real-world applications, and identify open challenges as well as possible research directions for the future.

Hyperscale data analysis oriented optimization mechanisms for higher education management systems platforms with evolutionary intelligence

Higher education institutions face vast amounts of complex data from various sources. Extracting meaningful insights from this data can improve student outcomes and institutional performance. This paper proposes novel evolutionary optimization mechanisms for higher education management systems to enable hyperscale data analysis. An integrated framework is developed, combining educational data mining, learning analytics, and evolutionary computation techniques. The methodology employs a multi-objective evolutionary algorithm with dynamic resource allocation to optimize multiple objectives simultaneously. Adaptive learning control is incorporated to balance exploration and exploitation. Theoretical analyses provide convergence proofs for the proposed algorithms. Comprehensive experiments on real-world and synthetic datasets demonstrate the effectiveness of the proposed mechanisms compared to state-of-the-art approaches. The results show significant performance gains regarding solution quality, scalability, and computational efficiency. The proposed techniques can be a foundation for developing the next generation of intelligent higher education management systems.

Automated design of action advising trigger conditions for multiagent reinforcement learning: A genetic programming-based approach

Action advising is a popular and effective approach to accelerating independent multiagent reinforcement learning (MARL), especially for the learning systems that all the agents learn from scratch and the roles of them (advisors or advisees) cannot be predefined. The key component of action advising is the trigger condition, which answers the question of when to advise. Previous works mainly focus on the design of novel trigger conditions manually; however, since those conditions are often designed heuristically, the performance may be affected by the preference of the designers. To this end, this paper tries to solve the action advising problem automatically using genetic programming (GP), an evolutionary computation technique. A framework incorporating GP to action advising is provided, together with a novel population initialization method to enhance the performance. Empirical studies are provided to demonstrate the effectiveness of the proposed framework. More importantly, thanks to the high transparency of GP, comprehensive analysis is also conducted based on the results. Interesting and inspiring insights to the action advising problem are condensed from the discussions, which may provide guidance to future works.

Analyzing predictors of in-hospital mortality in patients with acute ST-segment elevation myocardial infarction using an evolved machine learning approach

Acute ST-segment elevation myocardial infarction (STEMI) is a severe cardiac ailment characterized by the sudden complete blockage of a portion of the coronary artery, leading to the interruption of blood supply to the myocardium. This study examines the medical records of 3205 STEMI patients admitted to the coronary care unit of the First Affiliated Hospital of Wenzhou Medical University from January 2014 to December 2021. In this research, a novel predictive framework for STEMI is proposed, incorporating evolutionary computational methods and machine learning techniques. A variant algorithm, AGCOSCA, is introduced by integrating crossover operation and observation bee strategy into the original Sine Cosine Algorithm (SCA). The effectiveness of AGCOSCA is initially validated using IEEE CEC 2017 benchmark functions, demonstrating its ability to mitigate the deficiency in local mining after SCA random perturbation. Building upon this foundation, the AGCOSCA approach has been paired with Support Vector Machine (SVM) to forge the predictive framework referred to as AGCOSCA-SVM. Specifically, AGCOSCA is employed to refine the selection of predictors from a substantial feature set before SVM is utilized to forecast the occurrence of STEMI. In our analysis, we observed that SVM excels at managing nonlinear data relationships, a strength that becomes particularly prominent in smaller datasets of STEMI patients. To assess the effectiveness of AGCOSCA-SVM, diagnostic experiments were conducted based on the STEMI sample data. Results indicate that AGCOSCA-SVM outperforms traditional machine learning methods, achieving superior Accuracy, Sensitivity, and Specificity values of 97.83 %, 93.75 %, and 96.67 %, respectively. The selected features, such as acute kidney injury (AKI) stage, fibrinogen, mean platelet volume (MPV), free triiodothyronine (FT3), diuretics, and Killip class during hospitalization, are identified as crucial for predicting STEMI. In conclusion, AGCOSCA-SVM emerges as a promising model framework for supporting the diagnostic process of STEMI, showcasing potential applications in clinical settings.

Application of evolutionary computation techniques in formulating short term tradings decisions in stock market

Application of evolutionary computation techniques in formulating short term tradings decisions in stock market

Multi-and many-objective optimization: present and future in de novo drug design.

de novo Drug Design (dnDD) aims to create new molecules that satisfy multiple conflicting objectives. Since several desired properties can be considered in the optimization process, dnDD is naturally categorized as a many-objective optimization problem (ManyOOP), where more than three objectives must be simultaneously optimized. However, a large number of objectives typically pose several challenges that affect the choice and the design of optimization methodologies. Herein, we cover the application of multi- and many-objective optimization methods, particularly those based on Evolutionary Computation and Machine Learning techniques, to enlighten their potential application in dnDD. Additionally, we comprehensively analyze how molecular properties used in the optimization process are applied as either objectives or constraints to the problem. Finally, we discuss future research in many-objective optimization for dnDD, highlighting two important possible impacts: i) its integration with the development of multi-target approaches to accelerate the discovery of innovative and more efficacious drug therapies and ii) its role as a catalyst for new developments in more fundamental and general methodological frameworks in the field.

Evolutionary Computation Techniques for Path Planning Problems in Industrial Robotics: A State-of-the-Art Review

The significance of robot manipulators in engineering applications and scientific research has increased substantially in recent years. The utilization of robot manipulators to save labor and increase production accuracy is becoming a common practice in industry. Evolutionary computation (EC) techniques are optimization methods that have found their use in diverse engineering fields. This state-of-the-art review focuses on recent developments and progress in their applications for industrial robotics, especially for path planning problems that need to satisfy various constraints that are implied by both the geometry of the robot and its surroundings. We discuss the most-used EC method and the modifications that suit this particular purpose, as well as the different simulation environments that are used for their development. Lastly, we outline the possible research gaps and the expected directions future research in this area will entail.

Real-Time Implementation of BLDC Motor-Based Intelligent Tracking Control Fed from PV-Array for E-Bike Applications

The essential goal of this research is designing and modeling a speed and position tracking system for driving an electric bike (e-bike) motordrive. This motor is a brushless DC (BLDC) motor as a high-performance drive. It is supplied from twin electric sources to drive it and charge the storage elements (i.e., batteries, super-capacitors, etc.). The first one is a renewable, neat, and clean source photovoltaic (PV) module and the second one is a pedal generator driven by the rider. The submitted design of the controllers is optimized to improve the system’s dynamic stability. The artificial bee colony (ABC) as an artificial intelligent (AI) algorithm is suggested for searching the optimal gains of the proposed proportional-integral-derivative (PID) controllers by reducing the error of its fitness function. The system behavior is studied with that controller when directly feeding from the PV array with and without batteries. The response of the proposed technique - against dynamic troubles and PV oscillations such as irradiance- is also verified. Other evolutionary computational techniques - such as ant colony optimization (ACO) and genetic algorithm (GA)- have been compared with the behavior of the proposed controller to ensure high efficiency in optimized tuning of PID gains. Then, the proposed controller that gives a high performance will be executed in real-time by using OPAL-RT 4510 RT-simulator and rapid control prototyping.

A tutorial-based survey on feature selection: Recent advancements on feature selection

Curse of dimensionality is known as big challenges in data mining, pattern recognition, computer vison and machine learning in recent years. Feature selection and feature extraction are two main approaches to circumvent this challenge. The main objective in feature selection is to remove the redundant features and preserve the relevant features in order to improve the learning algorithm performance. This survey provides a comprehensive overview of state-of-art feature selection techniques including mathematical formulas and fundamental algorithm to facilitate understanding. This survey encompasses different approaches of feature selection which can be categorized to five domains including: A) subspace learning which involves matrix factorization and matrix projection, B) sparse representation learning which includes compressed sensing and dictionary learning, C) information theory which covers multi-label neighborhood entropy, symmetrical uncertainty, Monte Carlo and Markov blanket, D) evolutionary computational algorithms including Genetic algorithm (GA), particle swarm optimization (PSO), Ant colony (AC) and Grey wolf optimization (GWO), and E) reinforcement learning techniques. This survey can be helpful for researchers to acquire deep understanding of feature selection techniques and choose a proper feature selection technique. Moreover, researcher can choose one of the A, B, C, D and E domains to become deep in this field for future study. A potential avenue for future research could involve exploring methods to reduce computational complexity while simultaneously maintaining performance efficiency. This would involve investigating ways to achieve a more efficient balance between computational resources and overall performance. For matrix-based techniques, the main limitation of these techniques lies in the need to tune the coefficients of the regularization terms, as this process can be challenging and time-consuming. For evolutionary computational techniques, getting stuck in local minimum and finding an appropriate objective function are two main limitations.

Contributions to Dynamic Analysis of Differential Evolution Algorithms.

The Differential Evolution (DE) algorithm is one of the most successful evolutionary computation techniques. However, its structure is not trivially translatable in terms of mathematical transformations that describe its population dynamics. In this work, analytical expressions are developed for the probability of enhancement of individuals after each application of a mutation operator followed by a crossover operation, assuming a population distributed radially around the optimum for the sphere objective function, considering the DE/rand/1/bin and the DE/rand/1/exp algorithm versions. These expressions are validated by numerical experiments. Considering quadratic functions given by f(x)=xTDTDx and populations distributed according to the linear transformation D-1 of a radially distributed population, it is also shown that the expressions still hold in the cases when f(x) is separable (D is diagonal) and when D is any nonsingular matrix and the crossover rate is Cr=1.0. The expressions are employed for the analysis of DE population dynamics. The analysis is extended to more complex situations, reaching rather precise predictions of the effect of problem dimension and of the choice of algorithm parameters.

A new modified bat algorithm for global optimization

Bat Algorithm, is an evolutionary computation technique based on the echolocation behaviour of microbats while looking for their prey. It is used to perform global optimization. It was developed by Xin-She Yang in 2010. Since then, it has extensively been applied in various optimization problems because of its simple structure and robust performance. Continuous, discrete, or binary, many variants were proposed over the last few years, with applications to solve real-world cases in different fields. Yet, it has one major drawback: its premature convergence due to a lack in its exploration ability. In this paper, we introduce a selection-based improvement and three other modifications to the standard version of this metaheuristic in order to enhance the diversification and intensification capabilities of the algorithm. The newly proposed method has been then tested on 20 standard benchmark functions and the CEC2005 benchmark suit. Some non-parametric statistical tests were also used to compare the New Bat algorithm with other algorithms, and results indicate that the new method is very competitive and outperforms some of the state-of-the-art algorithms.

Performance evaluation of a non linear PID controller using chaotic gravitational search algorithm for a twin rotor system

AbstractA novel strategy using a chaotic gravitational search algorithm (CGSA) based nonlinear PID control scheme, which is validated through a laboratory helicopter model called the twin rotor system, is presented in this paper. In this work, CGSA is used as a stochastic based global optimization algorithm for controller design in the twin rotor system adopted. The fine chaotic search process used in CGSA obtains the optimal solution in the iterative process based on the current best solution. The goal of the controller design in this paper is to stabilize the twin rotor system with considerable cross couplings to reach the selected position and follow the desired trajectory effectively. The addition of nonlinear functions to the PID controller structure initiates better error tracking and facilitates smooth output under changing input conditions. The design objective is to implement a nonlinear PID control scheme for the angular displacements of the twin rotor system with minimization of the integral square error (ISE) as the fitness function in the algorithm. The statistical performance of the controller is analyzed by considering the best, worst, mean, and standard deviations of ISE. In this work, simultaneous control of pitch and yaw angles is considered to get rid of the coupling effect between the two rotors. From the simulation results it is observed that the proposed work shows better performance than the other evolutionary computation techniques. The results also indicate the advantage of the proposed CGSA based tuning for the two degree of freedom MIMO control with standard reference trajectories as per the TRMS330‐10 model.