Average Fitness Value Research Articles

IMPROVEMENT OF WOLF LEADER IN THE GREY WOLF OPTIMIZATION

The development of optimization algorithms attracts the attention of many analysts as it has advantages such as increasing performance, revenue, and efficiency in various fields, and reducing cost. Swarm-based optimization algorithms, which are among the meta-heuristic methods, are more commonly preferred because they are generally successful. In this study, the alpha wolf class, also called the wolf leader class, in the Grey Wolf Optimization (GWO), has been improved with the Whale Optimization Algorithm (WOA). This improved method is called ILGWO. To evaluate the ILGWO, 23 benchmark test functions, and 10 CEC2019 test functions were used. After running 30 iterations of the suggested algorithm, average fitness and standard deviation values have been acquired; these findings have been compared to the literature. Based on the literature's comparisons of the algorithms, the ILGWO algorithm has achieved the most optimal result in 5 of 7 functions for unimodal benchmark functions, 3 of 6 functions for multimodal benchmark functions, 9 of 10 functions for fixed-dimension multimodal benchmark functions, and 8 of 10 functions for CEC2019 test functions. So the proposed algorithm is generally better than the literature results. It has been found that the suggested ILGWO is encouraging and may be used in a variety of implementations.

Quality evaluation of preschool video games based on RNN

With the rapid development of video multimedia technology, video games have started to be applied in the field of education. A video game quality evaluation method needs to be designed to help teaching staff select video materials. In view of this, a Recurrent Neural Network (RNN)-based video game quality evaluation method for preschool education is proposed. Firstly, an improved attention mechanism null domain weight assignment method is proposed for the video quality evaluation problem. Then a video game quality evaluation model based on RNN is proposed. Finally, performance testing is conducted on the proposed quality evaluation method. The results show that in the comparison of fitness values, when the number of iterations is 4 or 16, the maximum fitness value and the average fitness value of the research method are 97 and 96 respectively. In the prediction of different video datasets, the research methods can achieve prediction accuracy of 0.958 and 0.947 for LIVE and CSIQ videos respectively. In the analysis of practical application effects, all students show significant improvement in language reading, writing, understanding, and spelling. The above results indicate that the research method has a faster convergence speed and prediction accuracy. It can effectively improve students' learning efficiency and provide new references for the education and teaching evaluation of preschool children.

A comparison between chaos theory and Lévy flights in sunflower optimization for feature selection

AbstractFeature selection is a knowledge discovery tool to understand the problem by analysing features. In particular, the application of feature selection in data mining can not only improve the quality of extracted patterns and knowledge but also decrease computational costs. Various techniques have been applied to this complex optimization problem, in which metaheuristics have been validated to be superior. This study introduces a new metaheuristic known for having lean and fast programming, inspired by the sunflower's motions for feature selection for the first time. It is equipped with a v‐shaped transfer function and associated with the KNN classifier to become the binary sunflower optimization (BSFO). A total of 12 variants of BSFO are designed based on the chaos theory and Lévy flights, called improved binary sunflower optimization (IBSFO). A discussion between these improvement theories for feature selection has also not been made yet, and it is performed in this paper using 15 benchmark datasets from the UCI repository. The experimental results show that all variants can advance the fitness value of BSFO, and nine of them considerably decrease the computational costs. Furthermore, the chaotic BSFO with the Chebyshev function, taking replacement to normal rand, has the lowest fitness value (−11.37%) and execution time (−9.31%) than the original BSFO. Further, IBSFO is compared with another eight metaheuristics and outperforms these competitors on average fitness value and execution time. Overall, IBSFO proved to find subsets with reduced dimension and high accuracy with meagre computational cost due to its robust explorative and exploitative capacities.

A novel binary greater cane rat algorithm for feature selection

There is a surge in the application of population-based metaheuristic algorithms to find the optimal feature subset from high dimensional datasets. Many of these approaches cannot properly scale especially as they are expected to maintain two opposing goals: maximizing the accuracy of classification while at the same time minimizing the number of feature subsets selected. In this study, a novel binary greater cane rat algorithm (GCRA), inspired by intelligent nocturnal behavior of the GCR which significantly affects their foraging and mating activities. They leave trails to food sources, shelters, and water as they forage, and this information is kept by the dominant. Also, they split into male and female groups during mating season is during abundant food supply and near water source. This information is modeled into and effective method for selecting the optimal feature subset from high-dimensional datasets using two different approaches. Firstly, five variants of binary GCRA are developed using one each from the family of S-shaped, V-shaped, U-shaped, Z-shaped, and quadratic transfer functions to binarize the GCRA. Secondly, the threshold which maps a variable to 0 or 1 is used to develop a variant of GCRA. The performance of the six (6) variants were evaluated using 12 datasets with different dimensionalities. The experimental results show the stability of all the proposed methods as they generally performed competitively. However, the threshold version known as BGCRA showed better performance in yielding the highest accuracy of classification on 9 of the 12 datasets utilized in the study and performed second in selecting the least number of important feature sets. It also showed superiority over other variants in yielding the least average fitness values in 11 of 12 (91.6%) of the datasets used. Hence, the BGCRA was utilized for further comparative analysis against 5 other popular feature selection (FS) algorithms with outstanding performance in terms of producing the highest mean accuracy of classification on 91.6% (11 of 12) of the datasets, 100% least average fitness values, and 91.6% in selecting the least average number of significant features. The results were also validated by statistical tests which showed that the BGCRA is significantly superior compared to other methods.

An enhanced slime mould algorithm based on adaptive grouping technique for global optimization

When solving global optimization problems by metaheuristic algorithms (MAs), an important issue is how to keep a balance between convergence and diversity. This article develops an enhanced slime mould algorithm called AGSMA to address the above issue. In AGSMA, the entire population is divided into two subpopulations. The size of each subpopulation is dynamically tuned based on the average fitness value. Afterward, different from the search equation in conventional SMA, a new search mechanism is raised to discover more promising regions. Furthermore, an efficient learning operator is devised to discourage premature convergence. The comparison of the raised AGSMA on several benchmark functions and practical engineering problems with selected state-of-the-art methods demonstrates that the developed algorithm performs effectively and competitively. The source code of AGSMA is publicly available at https://github.com/denglingyun123/Enhanced-slime-mould-algorithm.

Profile of Body Mass Index and Physical Fitness of Santri of Al Yumna Qur'an Tahfizh Boarding School

Purpose of study. This study aims to determine the description of body mass index and physical fitness of students of the Al Yumna Banyuresmi Garut Tahfizh Islamic Boarding School, Garut. Materials and methods. The method used was a quantitative descriptive survey, with the technique of the 2019 Department of Education of the Republic of the Philippines physical fitness instrument test. The variables in this study were BMI and Physical Fitness. The sampling technique used a random sampling technique of 57 people consisting of 27 men and 30 women, data collection techniques through the Philippine physical fitness test, BMI with anthropometric measurements, strength with push ups and basic plank, flexibility with zipper test and sit and reach , coordination with juggling test, agility using hexagon agility test, speed with 40 meter sprint test, strength with standing long jump, balance with stork balance test and reaction time speed with stick drop test. Results. The data processing method uses descriptive statistics using Microsoft Excel. For male students, there were 2 people (7.14%) in the underweight category, 5 people (18.52%) in the underweight category, 18 people (66.60%) in the normal category, 1 person (3.70%) in the overweight category, 1 person (3.70%) in the overweight category. As for females, there are 4 people (13.33%) with severe underweight, 2 people (6.66%) with mild underweight, 20 people (66.60%) with normal weight, 1 person (3.33%) with mild overweight and 3 people (10 %) with severe overweight. For men, the physical fitness category is very good as many as 17 people (62.96%), the good category is 10 people (37.03%) and for the category of very good, sufficient and less there are none (0%). Whereas for women the good category was 19 people (63.30%), the sufficient category was 11 people (36.50%). Conclusion. With the largest norm value of 5 for men having an average fitness value of 3.98 and 2.7 for women, the average for men and women is 3.34, this shows that the overall physical fitness of Tahfizh Qur'an Al Yumna Islamic Boarding School students is in the good category.

An efficient multilevel thresholding segmentation method based on improved chimp optimization algorithm

To improve the traditional image segmentation, an efficient multilevel thresholding segmentation method based on improved Chimp Optimization Algorithm (IChOA) is developed in this paper. Kapur entropy is utilized as the objective function. The best threshold values for RGB images’ three channels are found using IChOA. Meanwhile, several strategies are introduced including population initialization strategy combining with Gaussian chaos and opposition-based learning, the position update mechanism of particle swarm algorithm (PSO), the Gaussian-Cauchy mutation and the adaptive nonlinear strategy. These methods enable the IChOA to raise the diversity of the population and enhance both the exploration and exploitation. Additionally, the search ability, accuracy and stability of IChOA have been significantly enhanced. To prove the superiority of the IChOA based multilevel thresholding segmentation method, a comparison experiment is conducted between IChOA and 5 six meta-heuristic algorithms using 12 test functions, which fully demonstrate that IChOA can obtain high-quality solutions and almost does not suffer from premature convergence. Furthermore, by using 10 standard test images the IChOA-based multilevel thresholding image segmentation method is compared with other peers and evaluated the segmentation results using 5 evaluation indicators with the average fitness value, PSNR, SSIM, FSIM and computational time. The experimental results reveal that the presented IChOA-based multilevel thresholding image segmentation method has tremendous potential to be utilized as an image segmentation method for color images because it can be an effective swarm intelligence optimization method that can maintain a delicate balance during the segmentation process of color images.

An improved mayfly algorithm based on Kapur entropy for multilevel thresholding color image segmentation

Multilevel thresholding segmentation of color images plays an important role in many fields. The pivotal procedure of this technique is determining the specific threshold of the images. In this paper, an improved mayfly algorithm (IMA)-based color image segmentation method is proposed. Tent mapping initializes the female mayfly population to increase population diversity. Lévy flight is introduced in the wedding dance iterative formulation to make IMA jump from the local optimal solution quickly. Two nonlinear coefficients were designed to speed up the convergence of the algorithm. To better verify the effectiveness, eight benchmark functions are used to test the performance of IMA. The average fitness value, standard deviation, and Wilcoxon rank sum test are used as evaluation metrics. The results show that IMA outperforms the comparison algorithm in terms of search accuracy. Furthermore, Kapur entropy is used as the fitness function of IMA to determine the segmentation threshold. 10 Berkeley images are segmented. The best fitness value, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and other indexes are used to evaluate the effect of segmented images. The results show that the IMA segmentation method improves the segmentation accuracy of color images and obtains higher quality segmented images.

Neighborhood Search Based Improved Bat Algorithm for Web Service Composition

Web services are provided as reusable software components in the services-oriented architecture. More complicated composite services can be combined from these components to satisfy the user requirements represented as a workflow with specified Quality of Service (QoS) limitations. The workflow consists of tasks where many services can be considered for each task. Searching for optimal services combination and optimizing the overall QoS limitations is a Non-deterministic Polynomial (NP)-hard problem. This work focuses on the Web Service Composition (WSC) problem and proposes a new service composition algorithm based on the micro-bats behavior while hunting the prey. The proposed algorithm determines the optimal combination of the web services to satisfy the complex user needs. It also addresses the Bat Algorithm (BA) shortcomings, such as the tradeoff among exploration and exploitation searching mechanisms, local optima, and convergence rate. The proposed enhancement includes a developed cooperative and adaptive population initialization mechanism. An elitist mechanism is utilized to address the BA convergence rate. The tradeoff between exploration and exploitation is handled through a neighborhood search mechanism. Several benchmark datasets are selected to evaluate the proposed bat algorithm’s performance. The simulation results are estimated using the average fitness value, the standard deviation of the fitness value, and an average of the execution time and compared with four bat-inspired algorithms. It is observed from the simulation results that introduced enhancement obtains significant results.

Hybridizing Artificial Bee Colony with Bat Algorithm for Web Service Composition

In the Internet of Things (IoT), the users have complex needs, and the Web Service Composition (WSC) was introduced to address these needs. The WSC’s main objective is to search for the optimal combination of web services in response to the user needs and the level of Quality of Services (QoS) constraints. The challenge of this problem is the huge number of web services that achieve similar functionality with different levels of QoS constraints. In this paper, we introduce an extension of our previous works on the Artificial Bee Colony (ABC) and Bat Algorithm (BA). A new hybrid algorithm was proposed between the ABC and BA to achieve a better tradeoff between local exploitation and global search. The bat agent is used to improve the solution of exhausted bees after a threshold (limits), and also an Elitist Strategy (ES) is added to BA to increase the convergence rate. The performance and convergence behavior of the proposed hybrid algorithm was tested using extensive comparative experiments with current state-of-the-art nature-inspired algorithms on 12 benchmark datasets using three evaluation criteria (average fitness values, best fitness values, and execution time) that were measured for 30 different runs. These datasets are created from real-world datasets and artificially to form different scale sizes of WSC datasets. The results show that the proposed algorithm enhances the search performance and convergence rate on finding the near-optimal web services combination compared to competitors. The Wilcoxon signed-rank significant test is used where the proposed algorithm results significantly differ from other algorithms on 100% of datasets.

Artificial Bee Colony with Cuckoo Search for Solving Service Composition

In recent years, cloud computing has provided a Software As A Service (SaaS) platform where the software can be reused and applied to fulfill complicated user demands according to specific Quality of Services (QoS) constraints. The user requirements are formulated as a workflow consisting of a set of tasks. However, many services may satisfy the functionality of each task; thus, searching for the composition of the optimal service while maximizing the QoS is formulated as an NP-hard problem. This work will introduce a hybrid Artificial Bee Colony (ABC) with a Cuckoo Search (CS) algorithm to untangle service composition problem. The ABC is a well-known metaheuristic algorithm that can be applied when dealing with different NP-hard problems with an outstanding record of performance. However, the ABC suffers from a slow convergence problem. Therefore, the CS is used to overcome the ABC’s limitations by allowing the abandoned bees to enhance their search and override the local optimum. The proposed hybrid algorithm has been tested on 19 datasets and then compared with two standard algorithms (ABC and CS) and three state-of-the-art swarm-based composition algorithms. In addition, extensive parameter study experiments were conducted to set up the proposed algorithm’s parameters. The results indicate that the proposed algorithm outperforms the standard algorithms in the three comparison criteria (best fitness value, average fitness value, and average execution time) overall datasets in 30 different runs. Furthermore, the proposed algorithm also exhibits better performance than the state–of–the–art algorithms in the three comparison criteria over 30 different runs.

An Improved Whale Optimization Algorithm for Web Service Composition

In the current circumstance, the Web Service Composition (WSC) was introduced to address complex user needs concerning the Quality of Services (QoS). In the WSC problem, the user needs are divided into a set of tasks. The corresponding web services are retrieved from the web services discovery according to the functionality of each task, and have different non-functional constraints, such as QoS. The WSC problem is a multi-objective optimization problem and is classified as an NP-hard problem. The whale optimization algorithm (WOA) is proven to solve complex multi-objective optimization problems, and it has the advantage of easy implementation with few control parameters. In this work, we contribute to improving the WOA algorithm, where different strategies are introduced to enhance its performance and address its shortcomings, namely its slow convergence speed, which produces low solution accuracy for the WSC problem. The proposed algorithm is named Improved Whale Optimization Algorithm (IWOA) and has three different strategies to enhance the performance of the WOA. Firstly, the Sine chaos theory is proposed to initiate the WOA’s population and enhance the initialization diversity. Secondly, a Lévy flight mechanism is proposed to enhance the exploitation and exploration of WOA by maintaining the whales’ diversity. Further, a neighborhood search mechanism is introduced to address the trade-off between exploration and exploitation searching mechanisms. Different experiments are conducted with datasets on 12 different scales (small, medium, and large), and the proposed algorithm is compared with standard WOA and five state-of-the-art swarm-based algorithms on 30 different independent runs. Furthermore, four evaluation criteria are used to validate the comparison: the average fitness value, best fitness values, standard deviation, and average execution time. The results show that the IWOA enhanced the WOA algorithm’s performance, where it got the better average and best fitness values with a low variation on all datasets. However, it ranked second regarding average execution time after the WOA, and sometimes third after the WOA and OABC, which is reasonable because of the proposed strategies.

Binary dwarf mongoose optimizer for solving high-dimensional feature selection problems.

Selecting appropriate feature subsets is a vital task in machine learning. Its main goal is to remove noisy, irrelevant, and redundant feature subsets that could negatively impact the learning model’s accuracy and improve classification performance without information loss. Therefore, more advanced optimization methods have been employed to locate the optimal subset of features. This paper presents a binary version of the dwarf mongoose optimization called the BDMO algorithm to solve the high-dimensional feature selection problem. The effectiveness of this approach was validated using 18 high-dimensional datasets from the Arizona State University feature selection repository and compared the efficacy of the BDMO with other well-known feature selection techniques in the literature. The results show that the BDMO outperforms other methods producing the least average fitness value in 14 out of 18 datasets which means that it achieved 77.77% on the overall best fitness values. The result also shows BDMO demonstrating stability by returning the least standard deviation (SD) value in 13 of 18 datasets (72.22%). Furthermore, the study achieved higher validation accuracy in 15 of the 18 datasets (83.33%) over other methods. The proposed approach also yielded the highest validation accuracy attainable in the COIL20 and Leukemia datasets which vividly portray the superiority of the BDMO.

K-Means-Based Multigroup Differential Evolution Optimization Framework for Design of MIMO Antenna With Decoupling Elements

A novel differential evolution optimization method named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -means-based multigroup differential evolution (KMGDE) framework is proposed for multiple-input–multiple-output (MIMO) antenna design. The KMGDE algorithm divides the population into three groups using the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -means clustering method in each iteration. Different mutation strategies are assigned for each group according to its average fitness value. The groups are then combined for crossover operation with an adaptive crossover rate value to balance local search and global search. The fitness function considering the characteristics of the MIMO antenna is proposed to speed up optimization. The performance of the proposed method is evaluated and compared with six state-of-the-art optimization algorithms using an MIMO antenna example. The optimization results and the experimental results show that the proposed KMGDE algorithm has a faster convergence than state-of-the-art optimization algorithms, which significantly improves the efficiency of MIMO antenna design.

Process Mining using Inductive Miner Algorithm to Determine the actual Business Process Model

At the beginning of 2019, the COVID-19 pandemic entered the country of Indonesia resulting in all learning activities being carried out online in all cities of Indonesia. Likewise, Telkom University concentrates all teaching and learning activities online using the CeLOE Learning Management System. Learning Management System is a system that helps lecturers in managing teaching and learning activities independently in educational institutions. CeLOE is a learning management system of Telkom University developed based on Moodle. In this study, we analyse the CeLOE event log using the process mining method. The goal is to find out the learning patterns of students using CeLOE during the COVID-19 pandemic. This research case study focuses on the activities of students of the Telkom University S1 Informatics study program for the first semester of 2020/2021 in using CeLOE LMS. The analysis of this study conducted a comparison of the performance of three variants of the inductive miner (IM) algorithm through conformance checking values. The results of the analysis obtained are value of conformance checking from the three variants of the inductive miner (IM) algorithm have an average fitness value of up to 1 prove that the inductive miner (IM) algorithm can make a model based on the event log well. Besides that, it has a fairly high precision value with a value range of 0.750-0.850 shows that the inductive miner (IM) makes a process model with relatively many variations of activities outside the event log and the IM process model is "overfit-ting" for all variants of the IM algorithm. Inductive miner (IM) is the best inductive miner (IM) algorithm variant with a fitness value of 1.0, precision value of 0.750, and the generalization value of this algorithm is relatively high (0.984). It is hoped that this research can contribute to the addition of new perspectives related to the implementation of process mining using inductive miner (IM) algorithm in the field of education

New robust hybrid Jarratt-Butterfly optimization algorithm for nonlinear models

The nonlinear system of equations (NSE) is the basis of many engineering and scientific models. However, these models must have accurate solutions to be successful. Several optimization algorithms, including Butterfly Optimization Algorithm (BOA), can be applied to solving NSE problems. However, optimization algorithms lack accurate solutions due to their limitations, including trapping at local optima and divergence problems. In this work, a novel hybrid approach that is called Jarratt Butterfly Optimization Algorithm (JBOA) is presented to solve NSE. The proposed JBOA is developed by combining Jarratt’s iterative method and the BOA. Combining the two methods has enhanced BOA’s search mechanism and convergence speed. It also overcomes Jarratt’s limitations, including selecting initial values, trapping at local optima, and divergence problems. This results in solving NSE in more accurate solutions and fewer iterations. JBOA’s efficiency was evaluated based on eight benchmark systems, two of which represent real-world applications. Further, JBOA was compared to several optimization algorithms, including the original BOA algorithm, Particle Swarm Optimization (PSO), Harris Hawk Optimization (HHO), Equilibrium Optimization (EO), and Ant Lion Optimizer (ALO). Furthermore, the performance of JBOA and Jarratt’s method has been compared. Finally, the experimental results support the superiority of JBOA as it significantly outperformed all other compared optimization algorithms in all benchmark systems in terms of best solutions, average fitness values, stability, and convergence speed.

The Function of Remote Monitoring System of a Robot Inverter Based on PLC and Cloud Platform

In order to improve the communication efficiency of the CAN bus application layer communication protocol of the inverter network monitoring system, this paper proposes a TTCAN scheduling optimization algorithm for the robot inverter remote monitoring system based on PLC and cloud platform. According to the analysis of the communication requirements of the frequency converter monitoring system, this paper designs the application layer protocol of the nodes in the system and establishes the system scheduling matrix. The time triggered can bus protocol (TTCAN) combines the event triggered mechanism with the time triggered mechanism. The hybrid scheduling strategy is used to optimize the system matrix of TTCAN; that is, the hybrid particle swarm optimization algorithm is used for periodic messages. For nonperiodic messages, uniform scheduling strategy and dynamic scheduling algorithm are adopted. The simulation is carried out by MATLAB tools. The simulation results show that the sum of partial minimum transmission time is 1928 and there are multiple optimal individuals through the hybrid particle swarm optimization algorithm. Compared with the traditional genetic algorithm and single particle swarm optimization algorithm, the hybrid algorithm is better than the traditional genetic algorithm and particle swarm optimization algorithm in terms of iteration times and average fitness value. In conclusion, the optimized TTCAN protocol improves the real-time performance, reliability, and bandwidth utilization of the communication network.

Design of College Scheduling Algorithm Based on Improved Genetic Ant Colony Hybrid Optimization

With the gradual expansion of college scale, the professional categories in colleges and universities are becoming more and more complete, and the volume of courses is becoming more and more huge. In the meantime, the number of students is growing by leaps and bounds, and the teaching resources are subject to more and more complicated teaching tasks. The workload and the difficulty of scheduling in teaching management are also on the rise year by year. This paper proposes a design of a college scheduling algorithm based on an improved genetic ant colony hybrid optimization algorithm. Firstly, the fitness-enhanced elimination law is proposed to improve the selection process of traditional genetic algorithms. Subsequently, the gene infection crossover method is proposed to ensure the increase of the average fitness value in the evolutionary process. Next, the unnecessary replication operation in the traditional genetic algorithm is removed to enhance the operation speed of the algorithm. Finally, the parallel mechanism of fuzzy adaptive is introduced to improve the convergence and stability of the algorithm. For the ant colony optimization algorithm, a nonuniform pheromone distribution is used according to the position of the current raster relative to the starting point, which makes the initial pheromone concentration of the dominant raster higher and avoids blind search by ants. The ant movement rules are redefined by the directional neighborhood expansion strategy to further shorten the path. The experimental results indicate that the hybrid optimization algorithm outperforms other algorithms in terms of performance in terms of scheduling success and scheduling time, and it can be applied in practical scheduling because of the high quality of courses schedule.

A Dynamic Multi-Swarm Particle Swarm Optimization With Global Detection Mechanism

To overcome the shortcomings of the standard particle swarm optimization algorithm (PSO), such as premature convergence and low precision, a dynamic multi-swarm PSO with global detection mechanism (DMS-PSO-GD) is proposed. In DMS-PSO-GD, the whole population is divided into two kinds of sub-swarms: several same-sized dynamic sub-swarms and a global sub-swarm. The dynamic sub-swarms achieve information interaction and sharing among themselves through the randomly regrouping strategy. The global sub-swarm evolves independently and learns from the optimal individuals of the dynamic sub-swarm with dominant characteristics. During the evolution process of the population, the variances and average fitness values of dynamic sub-swarms are used for measuring the distribution of the particles, by which the dominant one and the optimal individual can be detected easily. The comparison results among DMS-PSO-GD and other 5 well-known algorithms suggest that it demonstrates superior performance for solving different types of functions.

A simplified non-equidistant grey prediction evolution algorithm for global optimization

Grey prediction evolution algorithm, which is receiving more and more attention, considers the population sequence of evolutionary algorithms as an equidistant time series. In fact, the average fitness value of each generation population decreases at a variable speed during almost any evolution process. Therefore, the population sequence with the property of variable speed evolution should be modeled more properly as a non-equidistant time series. Along this way, this paper proposes a novel evolutionary algorithm based on a non-equidistant grey model, called simplified non-equidistant grey prediction evolution algorithm. The proposed algorithm is identified by its reproduction operator which is developed by the following three steps. Firstly, a non-equidistant grey model based on the average fitness value of each generation population is modeled. Secondly, the interval in the fitting stage of the non-equidistant grey model is simplified to an approximately equidistant time interval. Finally, a simplified reproduction operator which employs a parameter to preserve the non-equidistant nature in the prediction stage of the non-equidistant grey model is developed. The comprehensive performance of the proposed algorithm is evaluated on CEC2014 and CEC2019, as well as six engineering constrained design problems. Experimental results show that the proposed algorithm ranks first in CEC2014 and CECE2019 benchmark functions. The proposed algorithm can achieve a better solution with fewer computational overhead than some state-of-the-art algorithms on six engineering constrained design problems.The Matlab code of this paper is available onhttps://github.com/Zhongbo-Hu/Prediction-Evolutionary-Algorithm-HOMEPAGE.