Intelligent Evolutionary Algorithm Research Articles

An adaptative differential evolution with enhanced diversity and restart mechanism

Differential Evolution (DE) stands out as an exceptional intelligent evolutionary algorithm, acclaimed for its simplicity in implementation and the ability to optimize without necessitating differentiable conditions. However, a significant pitfall of DE lies in its susceptibility to getting trapped in local minima, leading to algorithmic stagnation and substantial performance impairments. To counteract the shortcoming, the Adaptative Differential Evolution with Diversity Maintenance and Restart Mechanism (ADE-DMRM) is proposed. This approach integrates three primary innovations. Firstly, the algorithm controls the number of generations that individuals stay in the external archive based on the successful evolution rate, thereby optimizing the mutation strategy to effectively maintain population diversity. Secondly, ADE-DMRM proposes a novel restart mechanism in which the current stagnant individuals are identified by combining a stagnation tracker and a diversity assessment indicator and then regenerated using a dimension-learning-based approach. Thirdly, wavelet basis functions and Cauchy distributions are employed for scaling factor implementation across different stages, while the dimension change information of successfully evolved individuals is harnessed to refine the adaptive parameter control scheme. Finally, comprehensive experiments were undertaken on the CEC2013, CEC2014, and CEC2017 test sets, assessing accuracy, convergence speed, the efficacy of each module, and time complexity. The results confirm that ADE-DMRM is an efficient single-objective optimization algorithm that outperforms current advanced variants.

Operation optimization of regional integrated energy systems

AbstractTo study the role of energy system demand‐side regulation capacity on carbon emission reduction, we proposed a regional integrated energy system (RIES) optimal dispatch model considering carbon trading cost and dynamic demand response (DR). First, based on the framework of the RIES, the impact of DR of transferable load, reducible load, and substitutable load demand on system operation, considering the response characteristics of electrical and thermal load, was analyzed. Second, the carbon quota of the system was analyzed, and the effect of carbon emissions and carbon capture technology on system operation was considered. Finally, a low carbon optimized operation model of RIES was established with system operating cost and operating efficiency as the target, and system capacity and energy storage power as the constraints. The effectiveness of the optimized operation of the improved intelligent evolutionary algorithm nondominated sorting genetic algorithm II (NSGA‐II) was verified as a research method. The results show that the energy efficiency of the system can be improved by tuning the DR of load demand with the application of the optimal operation model, and a coordination optimization on the economic and low carbon in RIES operation can also be achieved by the improved NSGA‐II algorithm.

Evolutionary machine learning builds smart education big data platform: Data-driven higher education

The development of machine learning has promoted the construction of smart education platforms. It is of great significance to deeply investigate the usage of machine learning techniques in smart education. In this paper, we explore how to utilize evolutionary algorithms and machine learning algorithms to build a smart education big data platform to promote the intelligent development of higher education and better assist the establishment of the smart education system. We combine evolutionary algorithms and machine learning models to build a personalized course recommendation model for the smart education big data platform, which uses deep belief networks and swarm intelligence evolutionary algorithms to recommend relevant content based on the interests of learners. We take advantage of the feature extraction of deep belief networks and combine supervised learning and unsupervised learning to design an intelligent recommendation model for teaching content. At the same time, we utilize the advantages of evolutionary algorithms to tune the model parameters to obtain the best parametric model. We compare our method with other methods on public dataset to show the model performance.

Rafflesia Optimization Algorithm Applied in the Logistics Distribution Centers Location Problem

<p>Intelligent evolutionary algorithm is an important method to solve optimization problems. Most of their inspiration comes from the laws of nature and biology. This paper proposes a new intelligent evolutionary algorithm based on the life habits of Rafflesia, which is called Rafflesia Optimization Algorithm. It mainly consists of three stages: attracting insects, swallowing insects, and spreading seeds. In the first stage, the ROA algorithm performs the local search to find the optimal solution. In the second stage, it improves execution efficiency and solution accuracy by reducing the number of individuals. In the third stage, it performs the global search to jump out of the local optimal position. In the experimental part, this paper uses numerical functions (the CEC2013 benchmark function set) and practical application problems (the logistics distribution centers location problem) to test the performance of the ROA algorithm, and compares it with seven meta-heuristics algorithms. The experimental results prove the effectiveness and practicability of the ROA algorithm.</p> <p> </p>

Investigation and improvement of intelligent evolutionary algorithms for the energy cost optimization of an industry crude oil pipeline system

Based on the simulation predication of an industry crude oil pipeline system, a novel energy cost optimization model combining discrete grid points and a penalty factor is proposed. Combined with the optimization model, the optimization performance of four representative intelligent evolutionary algorithms is compared and analysed. The comparative results indicate that the improved differential evolution (DE) algorithm obtains a lower energy cost and exhibits better optimization performance than the other three representative algorithms. Compared with the lowest energy cost of the actual field, an energy cost saving of 4.62% can be made. To further improve the performance of intelligent evolutionary algorithms for energy cost optimization, hybrid coding and selection schemes of the algorithms are researched. The hybrid coded DE algorithms can more easily obtain stable optimal energy costs. The modified genetic algorithm with a greedy selection scheme exhibits excellent optimization performance, and can obtain the optimal energy cost more quickly than DE.

Optimal Search Strategy of Low-Altitude Target for Airborne Phased Array Radar Using Digital Elevation Model

The search for low-altitude targets becomes a more challenging task for airborne phased array radar, due to the limited system resources and serious ground clutter. In this paper, an optimal search strategy using the digital elevation model (DEM) is proposed to allow for fast and high-probability search for low-altitude targets. First, a clutter-free detection region is formed by introducing the DEM. Compared with the traditional methods, the detection probability of the low-altitude targets can be improved. Second, the search problem is transformed into a constrained multi-objective optimization problem. The proposed strategy can simultaneously minimize the search time and maximize the target detection probability. Finally, two intelligent evolutionary algorithms are compared to verify the proposed strategy, and the proposed strategy is also testified by utilizing a real DEM data of Dujiangyan, Sichuan, China. Simulated results indicate that the proposed optimal search framework improves the detection probability and reduces time cost for low-altitude targets.

Automated test design using swarm and evolutionary intelligence algorithms

AbstractThe world's increasing dependence on computer‐assisted education systems has raised significant challenges about student assessment methods, such as automated test design. The exam questions should test the students' potential from various aspects, such as their intellectual and cognitive levels, which can be defined as attributes of the questions to assess student knowledge. Test design is challenging when various question attributes, such as category, learning outcomes, difficulty, and so forth, are considered with the exam constraints, such as exam difficulty and duration. In this paper, four contributions are provided to overcome test design challenges for the student assessment. First, a tool is developed to generate a synthetic question pool. Second, an objective function is designed based on the considered attributes. Third, the popular swarm and evolutionary optimization methods, namely particle swarm optimization, genetic algorithm, artificial bee colony, differential search algorithm are comparatively studied with novel methodologies applied to them. Finally, as the state of the art methods, artificial bee colony, and differential search algorithm are further modified to improve the solution of the test design. To perform the proposed algorithms, a dataset of 1000 questions is built with the proposed question attributes of the test design. Algorithms are evaluated in terms of their successes in both minimizing the objective function and running time. Additionally, Friedman's test and Wilcoxon rank‐sum statistical tests are applied to statistically compare the algorithms' performances. The results show that the improved artificial bee colony and the improved differential search provide better results than others in terms of optimization error and running time.

Optimal brick layout of masonry walls based on intelligent evolutionary algorithm and building information modeling

The Masonry wall (MW) is an important element of buildings. Currently, the detailed brick layout of a non-modular MW mostly relies on the experience from a bricklayer. The manual building information modeling (BIM) technique can facilitate the modeling of bricks, but is error-prone and time-consuming. This paper presents a BIM-based framework to generate the layouts and brick take-offs automatically. A computation module is developed to find the optimal brick layout using the three-stage optimization. For a MW, the first-stage determines the vertical position of each course and the second and third stages obtain the horizontal positions and sizes of bricks for odd and even courses respectively. The intelligent algorithm neighborhood field optimization is applied to the computation module. The proposed framework is applied in the automated detailing design of MWs with different bonds in two rooms to demonstrate its practicability and efficiency.

Smart structural stability and NN based intelligent control for nonlinear systems

This paper has proposed an intelligent Evolutionary Bat Algorithm (afterward, EBA) Fuzzy NN (Neural Network) controller used to ensure the asymptotic simulation stability of a mathematics nonlinear system for a smart structure. The smart evolutionary fuzzy NN model adopts an NN numerical model and the linear differential inclusion (LDI) concept. Denotation of the nonlinear dynamics is constructed by transforming the nonlinear model into a multi-rule-based sector nonlinear form of mathematics linear numerical models, and implementing a new sufficient mathematics condition whereby the asymptotic simulation stability of the intelligent structure is guaranteed by the Lyapunov mathematics function, linear matrix inequality (LMI). The high frequency is also injected as an auxiliary to stabilize these nonlinear systems. According to the relaxed method injected with dithered auxiliary, the nonlinear system can be guaranteed stable by appropriately regulating the parameters. Finally, there is a numerical resultant example with simulation results which is designated in order to precisely demonstrate the advantages of the smart intelligent controller and the proposed control scheme compared to previous schemes.

A method of workpiece location based on improved generalized Hough transform

In order to solve the problem that the workpiece is not easy to identify in the complex environment in the industrial processing, sorting and other automatic production links, a workpiece positioning method based on Improved Generalized Hough transform, also known as eif-ght, is proposed. In order to solve the problem of large time consumption and high memory consumption of traditional generalized Hough transform in target detection, this paper uses the rotation invariant feature angle formed by any two edge points in the image as the R table index under the condition of image preprocessing to realize the representation of target shape, and uses particle swarm intelligent evolutionary algorithm to speed up the search The final target is determined by similarity measurement. The experimental results show that when the workpiece rotates and shifts, and in the complex environment with noise, local occlusion, nonlinear illumination and so on, it can achieve stable and efficient positioning.

Optimal tuned direct adaptive controller for seismic protecting of structures

In the past few years, many studies have proved the efficiency of Simple Adaptive Control (SAC) in mitigating earthquakes’ damages to building structures. Nevertheless, the weighting matrices of this controller should be selected after a large number of sensitivity analyses. This step is time-consuming and it will not necessarily yield a controller with optimum performance. In the current study, an innovative method is introduced to tuning the SAC’s weighting matrices, which dispenses with excessive sensitivity analysis. In this regard, we try to define an optimization problem using intelligent evolutionary algorithm and utilized control indices in an objective function. The efficiency of the introduced method is investigated in 6-story building structure equipped with magnetorheological dampers under different seismic actions with and without uncertainty in the model of the proposed structure. The results indicate that the controller designed by the introduced method has a desirable performance under different conditions of uncertainty in the model. Furthermore, it improves the seismic performance of structure as compared to controllers designed through sensitivity analysis.

Forward-Looking Geometric Configuration Optimization Design for Spaceborne-Airborne Multistatic Synthetic Aperture Radar

Spaceborne-airborne multistatic synthetic aperture radar (SA-MuSAR) has the ability to provide high-resolution forward-looking imagery for receivers, but it relies on careful design of the geometric configuration (GC). In this article, a forward-looking GC optimization design method is proposed to obtain a high-quality fused image with limited observation time. First, the relationship between the spatial resolution and GC is illustrated by the wavenumber spectrum distribution of SA-MuSAR. Second, GC evaluators depending on the distribution of multiple wavenumber spectrum data are proposed. The GC design problem of coherent SA-MuSAR is transformed into a constrained multiobjective optimization problem. An intelligent evolutionary algorithm is adopted to optimize the wavenumber spectrum distribution. With the proposed method, high-quality forward-looking imagery can be obtained with a short observation time. Numerical simulations are carried out to verify the effectiveness of the proposed method.

A Survey of Bio Inspired Algorithms for Web Information Extraction and Optimization for Big Data Analytics

Information extraction is systematic process of extracting structured information from documents which has both unstructured and semi structured data set. Data available over the web is unstructured which is processed and delivered that may be challenging due to massive data over web. Bigdata analytics approach is used in the computation field where massive data is managed and processed as information. Data from various sources like industries, institutes are processed using algorithms in efficient means employing web of things or Internet of things used to mine such a large data. Bio inspired algorithms have evolved from application of heuristic approaches to meta-heuristic and hyper-heuristic methodologies. Bio inspired techniques are categorized into human inspired algorithms, Swarm Intelligence algorithms, evolutionary algorithms and ecology based algorithms. Genetic algorithms are purely heuristic in nature and are employed for computation and extracting information and from big data. This improves the computation speed effectively for extracting web related information as evolutionary algorithm resolves information extraction problems. The Ant colony and Particle Swarm Intelligence algorithms are of meta-heuristic in nature. The Cuckoo search, Artificial Bee Colony, Firefly algorithm and Bat algorithms are of hyper heuristic in nature i.e., they employ a combination of methods. Web information extraction using bio inspired concepts and genetic operators increases efficiency, capability to search particular information in massive data in web. Some of the tools that are available for data extraction and mining are DataMelt, Apache Mahout, Weka, Orange and Rapid Miner for enhancing web data extraction efficiency. This survey on bio inspired methodologies can be extended to parameter tuning and controlling is another big strategy that can be implemented, in addition to convergence speed up.

Intelligent Evolutionary Controller for Flexible Robotic Arm

Robotic is one of the key technologies towards Industrial Revolution 4.0. Robotic system, especially robotic arm have received tremendous demand in various fields especially manufacturing industry. Robotic arm is highly needed to enhance production, improve output, reduce human error and the most importantly, earn more profit with fast return on investment. The current industrial robotic arm, not only they are very expensive and required specialist for maintenance, they are also very heavy and difficult to manoeuvre. These facts are the reason why robotic solution are still unaffordable in most small and medium manufacturing industries in developing countries. Despite all the drawbacks, there is still a pressing need to employ robotics solution with the inherent problems of worker-related issues and output quality. Today, work requires a nimble and versatile robot and yet remain reliable. Operating robots should be simpler, where the learning curve is less steep. The user interface should be friendly and intuitive. Recently, there is a growing interest in employing lightweight, stronger and more flexible robotic arm in various fields. However, lightweight robot arm can be more easily influenced by unwanted vibrations, which may lead to problems including fatigue, instability and performance reduction. These problems may eventually cause damage to the highly stressed structures. This research focused on the development of the intelligent evolutionary controller algorithms for controlling flexible robotic arm manipulator. The controller algorithm has been formulated for trajectory planning control and vibration cancelation utilizing intelligent evolutionary algorithms such as Particle Swarm Algorithm and Artificial Bees Colony. The developed evolutionary algorithms have been implemented and experimentally verified using robotic arm manipulator experimental rig. The performances of these intelligent evolutionary controllers were found to be far better than the conventional method in term of input tracking, trajectory control and vibration cancelation.

Computer Simulation Evaluation of Financial Risk Based on Cuckoo Search and SVM Algorithm

With the rapid development of science and technology, there are more and more large-scale transnational enterprises, which also cause many financial risks. The financial risk level of listed companies will directly affect the sound development of the financial market, which requires listed companies to do a good job in financial risk assessment. However, most of the current financial risks rely on financial statements and other forms, which will be difficult to form a good early warning mechanism. Through the cuckoo search algorithm and SVM algorithm, we can form a new intelligent evolutionary algorithm, which can carry out multi group search and adaptive step size. Through the improved method, we can optimize the parameters of SVM model, which will be applied to the company financial risk assessment. Through the improved cuckoo search algorithm, we can improve the accuracy of financial data classification and prediction, which will better improve the financial environment. First of all, this paper analyzes the algorithm based on cuckoo. Then, this paper analyzes the SVM algorithm. Finally, some suggestions are put forward.

Defense against distributed DoS attack detection by using intelligent evolutionary algorithm

ABSTRACT Modern society is prominently dependent on information and communication technology over the last few decades has carried it with more vulnerability to an enormous variety of cyber-attacks. One of the attacks is a Distributed Denial-of-Service (DDoS) that exploits the power of thousands and sometimes hundreds of thousands of compromised computers to attack information-providing services and online commercial sites, often resulting in significant downtime and financial losses and thereby denying services of legitimate clients. The study of DDoS attacks is a significant area of research; there are a number of techniques that have been proposed such as evolutionary algorithm and artificial intelligence in the literature for detecting DDoS attacks. Unfortunately, the modern well-known DDoS detection schemes are deteriorating to validate the objective and prior recognition of DDoS attacks. In order to mitigate denial of service attacks, in this paper, we use grasshopper optimization algorithm (GOA) with machine learning algorithm called GOIDS. This approach is based on creating an intrusion detection system (IDS) to fulfill the requirements of the monitored environment and able to distinguish between normal and attack traffics. Furthermore, GOIDS selects the most relevant features from the original IDS dataset that can help to distinguish typical low-speed DDoS attacks and then, selected features are passed to the classifiers, i.e. support vector machine, decision tree, naïve Bayes, and multilayer perceptron to identify type of attack. The publicly available dataset as KDD Cup 99 and CIC-IDS 2017 are used for our experimental study. From the results of the simulation, it is clear that GOIDS with decision tree acquires high detection and accuracy with a low false–positive rate.

Nonlinear dynamic numerical analysis and prediction of complex system based on bivariate cycling time stochastic differential equation

In order to deal with the problem of low precision and ignoring the influence of some factors on the growth system of cyanobacteria, this paper considers the effects of light, water temperature and other influencing factors to analyze cyanobacteria growth system. The time stochastic differential equation of cyanobacteria growth with double nutrient cycling is established. The numerical analysis and the intelligent evolutionary algorithm are used to optimize the steady parameters in the time stochastic differential equation of cyanobacteria growth. The light, water temperature and other factors is used to build time series modeling. The time stochastic parameters and the prediction of cyanobacteria biomass is realized. The bifurcation theory and the central manifold theory were used to analyze the nonlinear dynamics of cyanobacteria growth with double nutrient cycling system, so that the conditions of cyanobacteria bloom and prediction of cyanobacteria blooms are achieved. Compared with the existing research, the method proposed in this paper not only determines the conditions of cyanobacteria bloom, but also improves the accuracy of water bloom prediction, and provides an effective reference for environmental protection departments.

Resource Leveling Optimization in Construction Projects of High Voltage Substations Using Nature-Inspired Intelligent Evolutionary Algorithms

Resource Leveling Optimization in Construction Projects of High Voltage Substations Using Nature-Inspired Intelligent Evolutionary Algorithms

Simulation-optimization for the management of the transshipment operations at maritime container terminals

Maritime container terminals are complex infrastructures designed specifically to handle a large number of containers, and which play a relevant role in international freight transport. Terminal managers must deal with a wide variety of interrelated logistic problems, and the effectiveness and productivity of the terminal depends on their solution. Management strategies are therefore necessary to increase their effectiveness and productivity, and thereby reducing the costs of these operations. This task is complicated by imprecise data and the need to satisfy several criteria, many of which are subjective, when evaluating the solutions.One of these logistic problems, the quay crane scheduling problem, has attracted the attention of many researchers since quay cranes are one of the most valuable resources in the port. Many proposals based on optimization algorithms have tackled this problem but the vast majority disregard the uncertainty inherent in this kind of systems and the impact of internal delivery vehicles.An intelligent system which integrates Artificial Intelligence techniques and simulation tools is proposed to aid terminal managers. The system combines an intelligent evolutionary algorithm to generate high quality schedules for the cranes with a simulation model that incorporates uncertainty and the impact of internal delivery vehicles. The joint use of these tools provides managers with enhanced information to decide on the quality and robustness of the proposed schedules, resulting in better solutions for everyday situations.Our intelligent system based on the optimization-simulation model provides clear benefits to maritime terminal management. The system efficiently identifies high quality schedules and can be used to evaluate its robustness. It is also flexible and can easily be adapted if other elements need to be introduced, which may affect the goodness of a schedule.

An Improved Squirrel Search Algorithm for Global Function Optimization

An improved squirrel search algorithm (ISSA) is proposed in this paper. The proposed algorithm contains two searching methods, one is the jumping search method, and the other is the progressive search method. The practical method used in the evolutionary process is selected automatically through the linear regression selection strategy, which enhances the robustness of squirrel search algorithm (SSA). For the jumping search method, the ‘escape’ operation develops the search space sufficiently and the ‘death’ operation further explores the developed space, which balances the development and exploration ability of SSA. Concerning the progressive search method, the mutation operation fully preserves the current evolutionary information and pays more attention to maintain the population diversity. Twenty-one benchmark functions are selected to test the performance of ISSA. The experimental results show that the proposed algorithm can improve the convergence accuracy, accelerate the convergence speed as well as maintain the population diversity. The statistical test proves that ISSA has significant advantages compared with SSA. Furthermore, compared with five other intelligence evolutionary algorithms, the experimental results and statistical tests also show that ISSA has obvious advantages on convergence accuracy, convergence speed and robustness.