Fitness Function Value Research Articles

Optimal path finding in 3D environment: Application for the exhibition space through 3D visualization and BIM

The application of 3D visualization technology in building construction has also increased. The study used Revit software to construct a 3D building information model (BIM) for the exhibition space of Chuzhou Higher Education City Development Collaborative Innovation Center to achieve a 3D visualization display; based on the 3D visualization, a particle swarm optimization (PSO) algorithm was used to find the optimal path for the exhibition space, so as to achieve the layout design of the exhibition space. The PSO algorithm was optimized in terms of inertia weight, acceleration coefficient, and initial population to obtain the improved PSO (IPSO) algorithm. The experimental results showed that the optimal path found by the IPSO algorithm was 78.56 meters in distance, 98.2 seconds in time consumption, and 50.11% in smoothness, which were better than the other two algorithms. Meanwhile, the IPSO algorithm had a lower value of particle fitness function, indicating that the IPSO algorithm had the highest performance and the strongest path finding ability among the three algorithms. It is confirmed that it is feasible to use the IPSO algorithm for optimal visit path finding in 3D environment. It is effective to visualize the exhibition space in 3D by constructing a BIM.

Wireless Sensor Network Optimization Using Genetic Algorithm

Wireless Sensor Network (WSN) is a high potential technology used in many fields (agriculture, earth, environmental monitoring, resources union, health, security, military, and transport, IoT technology). The band width of each cluster head is specific, thus, the number of sensors connected to each cluster head is restricted to a maximum limit and exceeding it will weaken the connection service between each sensor and its corresponding cluster head. This will achieve the research objective which refers to reaching the state where the proposed system energy is stable and not consuming further more cost. The main challenge is how to distribute the cluster heads regularly on a specified area, that’s why a solution was supposed in this research implies finding the best distribution of the cluster heads using a genetic algorithm. Where using an optimization algorithm, keeping in mind the cluster heads positions restrictions, is an important scientific contribution in the research field of interest. The novel idea in this paper is the crossover of two-dimensional integer encoded individuals that replacing an opposite region in the parents to produce the children of new generation. The mutation occurs with probability of 0.001, it changes the type of 0.05 sensors found in handled individual. After producing more than 1000 generations, the achieved results showed lower value of fitness function with stable behavior. This indicates the correct path of computations and the accuracy of the obtained results. The genetic algorithm operated well and directed the process towards improving the genes to be the best possible at the last generation. The behavior of the objective function started to be regular gradually throughout the produced generations until reaching the best product in the last generation where it is shown that all the sensors are connected to the nearest cluster head. As a conclusion, the genetic algorithm developed the sensors’ distribution in the WSN model, which confirms the validity of applying of genetic algorithms and the accuracy of the results.

OPTIMIZATION OF CLUSTER HEAD SELECTION AND ENHANCING ENERGY EFFICIENCY IN WIRELESS SENSOR NETWORKS USING NOVEL METAHEURISTIC ALGORITHMS

Clustering-based routing is preferred to support fault tolerance, load balancing, reliable communication, and to prolong the network lifetime in a wireless sensor network (WSN). The low-energy adaptive clustering hierarchy (LEACH) is the most popular routing technique, introduced for the first time for homogeneous WSNs. However, the random selection of cluster heads (CHs) in LEACH protocols results in poor performance in real network deployments due to the faster rate of energy depletion at CHs. The dynamic selection of CHs based on a heuristic approach can minimize the energy consumption at CHs and enhance the network lifetime. In this paper, a metaheuristic algorithm called grey wolf optimization (GWO) and its enhanced versions are proposed in selecting the optimal CH. The fitness function is defined based on sink distance to CH and residual energy at the sensor node. The optimal values of fitness function give an efficient CH selection and cost-effective routing. The primary goal of this paper is to maximize the network lifetime of WSNs by optimal selection of CHs using the improved GWO (IGWO) algorithm. The proposed IGWO-based LEACH protocol confirmed the optimal selection of CH with minimum energy consumption, resolved premature convergence, and enhanced the network lifetime by balancing the number of alive and dead nodes in WSN.

DBSCAN-Based Automatic De-Duplication for Software Quality Inspection Data

Software quality inspection will generate too much data, and removing duplicate data can improve the efficiency of software quality inspection. This paper studies the automatic de-duplication method of software quality inspection data based on density-based spatial clustering of applications with noise (DBSCAN) clustering. Intelligent optimization algorithm is used to generate software quality inspection data by initializing individuals, calculating fitness function value, improving individuals and splitting individuals that meet the conditions. Local linear embedding algorithm is selected to extract software quality inspection data features by searching neighborhood points, calculating reconstruction weight and projection vector. The extracted features are used to select DBSCAN multi-density clustering algorithm of regional division, and the automatic de-duplication of software quality inspection data is realized by grid division, data bin dividing and grid merging. The experimental results show that the precision and recall of this method are higher than 99%, and the resource consumption rate is low, which can effectively improve the efficiency of software quality inspection.

Neutrosophic Adaptive Clustering Optimization in Genetic Algorithm and Its Application in Cubic Assignment Problem

In optimization theory, the adaptive control of the optimization process is an important goal that people pursue. To solve this problem, this study introduces the idea of neutrosophic decision-making into classical heuristic algorithm, and proposes a novel neutrosophic adaptive clustering optimization thought, which is applied in a novel neutrosophic genetic algorithm (NGA), for example. The main feature of NGA is that the NGA treats the crossover effect as a neutrosophic fuzzy set, the variation ratio as a structural parameter, the crossover effect as a benefit parameter and the variation effect as a cost parameter, and then a neutrosophic fitness function value is created. Finally, a high order assignment problem in warehouse management is taken to illustrate the effectiveness of NGA.

WSN Clustering Routing Algorithm Combining Sine Cosine Algorithm and Lévy Mutation

Directed against the disadvantages of relatively short life-cycle and unbalanced energy utilization among nodes in WSN, a clustering routing algorithm combining sine cosine algorithm and Lévy mutation is developed. During the cluster head election stage, the amount of cluster heads is dynamically calculated according to the surviving nodes for keeping it at a reasonable value; taking full account of the current energy of nodes, only nodes with high energy can be candidate cluster heads; the fitness function is constructed according to inter-cluster distance, so that the distribution structure within the cluster are relatively uniform; the Sine Cosine Algorithm with improved step size search factor is used for cluster head election, and Lévy mutation is introduced to realize the variation of population. The group of individuals with the lowest fitness function value is used as final election scheme for current round. In the data transmission phase, for the sake of avoiding long-distance transmission, the relay node is designed to forward data. The proposed algorithm effectively extends network life-cycle and well equilibriums the load of network nodes.

Control Tuning for the Quadcopter Unmanned Aerial Vehicle Based on Genetic Evolutionary Algorithm

The quadcopter is an unmanned aerial vehicle that has been used in civil and military applications because of its relatively low cost, simple design, and ease of maintenance in addition to its unique flying characteristics and vast potential. Six Proportional-Integral-Derivative (PID) controllers are usually built for the position and attitude control of the quadcopter. However, tuning the controllers’ parameters poses a real challenge, as the system is highly coupled and contains two cascaded control loops while being underactuated (only four control inputs for the six degrees of freedom). In this study, a quadcopter model has been implemented by using MATLAB / SIMULINK where the model has been constructed to be integrated with the evolutionary genetic algorithm in addition to a third-party toolbox that is used to simulate fractional order calculus. The gains of the traditional and the fractional-order proportional-integral-derivative controllers have been tuned by using the genetic algorithm against different path shapes. The performance of the manually and the genetic algorithm tuned proportional-integral-derivative controllers have been compared with the genetic algorithm tuned fractional-order proportional-integral-derivative controllers. The genetic algorithm tuned fractional-order proportional-integral-derivative controllers have been superior and more robust in all cases with a lower fitness function value, much smother responses, and higher trajectory tracking ability.

Application of Improved K-Means Algorithm in Collaborative Recommendation System

With the explosive growth of information resources in the age of big data, mankind has gradually fallen into a serious “information overload” situation. In the face of massive data, collaborative filtering algorithm plays an important role in information filtering and information refinement. However, the recommendation quality and efficiency of collaborative filtering recommendation algorithms are low. The research combines the improved artificial bee colony algorithm with K-means algorithm and applies them to the recommendation system to form a collaborative filtering recommendation algorithm. The experimental results show that the MAE value of the new fitness function is 0.767 on average, which has good separation and compactness in clustering effect. It shows high search accuracy and speed. Compared with the original collaborative filtering algorithm, the average absolute error value of this algorithm is low, and the running time is only 50 s. It improves the recommendation quality and ensures the recommendation efficiency, providing a new research path for the improvement of collaborative filtering recommendation algorithm.

A modified bacterial foraging algorithm for improving road networks

One of the challenges that transportation planners face is deciding which improvements to make to existing road networks. A solution methodology for this problem must consider multiple factors, such as motorist travel behaviour and budget constraints. This is commonly represented as a bi-level optimization problem called the Discrete Network Design Problem (DNDP). In this paper, we devise and implement a Modified Bacterial Foraging Optimization (MBFO) algorithm that provides the decision maker with n-optimal solutions to the DNDP. This enables transportation planners to evaluate and compare various improvements before deciding which ones to implement. The use of a binary mapping function facilitates the implementation of Bacterial Foraging Optimization (BFO) to solve the DNDP. The MBFO employs a modified chemotaxis step with a single mutation operation to represent the tumbling motion and a revised swimming operation to ensure a diverse search of the solution space. To rank and eliminate the solutions, a gain index that measures improvement in fitness function value is used. The MBFO is tested on the 24-node, 76-arc Sioux Falls network, and the results show that it can converge to the n-optimal solution.

An Innovative Hunter-Prey-Based Optimization for Electrically Based Single-, Double-, and Triple-Diode Models of Solar Photovoltaic Systems

The derivation of PV model parameters is crucial for the optimization, control, and simulation of PV systems. Although many parameter extraction algorithms have been developed to address this issue, they might have some limitations. This work presents an efficient hybrid optimization approach for reliably and effectively extracting PV parameters based on the hunter–prey optimizer (HPO) technique. The proposed HPO technique is a new population-based optimizer inspired by the behavior of prey and predator animals. In the proposed HPO mechanism, the predator attacks the prey that leaves the prey population. Accordingly, the position of a hunter is adjusted toward this distant prey, while the position of the prey is adjusted towards a secure place. The search agent’s position, which represents the best fitness function value, is considered a secure place. The proposed HPO technique worked as suggested when parameters are extracted from several PV models, including single-, double-, and triple-diode models. Moreover, a statistical error analysis was used to demonstrate the superiority of the proposed method. The proposed HPO technique outperformed other recently reported techniques in terms of convergence speed, dependability, and accuracy, according to simulation data.

Optimal Solution for Frequency and Voltage Control of an Islanded Microgrid Using Square Root Gray Wolf Optimization

Voltage and frequency deviation in the islanded operation of a microgrid (MG), due to the uncertainty and lack of inertia in the selection of optimal proportional integral (PI) controller gain, is a challenging task. Although various optimization algorithms have been proposed to achieve this task, most of them require a large number of iterations and are time intensive, making them inefficient for real-time applications. Gray wolf optimization (GWO), a new meta-heuristic algorithm, addresses these issues and has many advantages, including simplicity due to fewer control parameters, flexibility, and globalism. This paper proposes a simple and efficient modified algorithm, called square root gray wolf optimization (SRGWO) algorithm, to realize superior hunting performance. SRGWO is verified using twenty-three benchmark test functions. The algorithm is applied for optimal voltage and frequency regulation of a photovoltaic-based microgrid system operating in the islanded mode during distributed generation insertion and load change conditions. The voltage and frequency gain parameters of the PI controller are optimized. A comparison of the simulation results of the SRGWO algorithm with those of the original gray wolf algorithm (GWO), particle swarm optimization (PSO), augmented gray wolf optimization (AGWO), enhanced gray wolf optimization (EGWO), and gravitational search algorithm (GSA) reveal that the proposed SRGWO algorithm significantly improves system performance while maintaining its simplicity and easy implementation. Furthermore, the SRGWO algorithm obtains the minimum fitness function value in fewer iterations than other algorithms. Moreover, it improves the power quality of the system with regard to minimum total harmonic distortion.

A two-variable control and optimization method for imbalance of high pressure compressor based on improved genetic algorithm.

To solve the problem of low quality rate for one-time assembly of high-pressure compressors, an improved genetic algorithm (GA) is used to adjust and optimize the imbalance after assembly. This paper takes the post-assembly imbalance of a multi-stage rotor of a high-pressure compressor as the objective function, to reduce the post-assembly imbalance by adjusting the arrangement order of rotor blades and the assembly phase between rotors. We used a four-sector staggered distribution method to generate high-quality initial populations and added an elite retention strategy. The crossover and mutation probabilities are adaptively adjusted according to the fitness function values. The threshold termination condition is added to make the algorithm converge quickly so as to achieve fast, stable, and efficient search. The simulation results show that the imbalance is reduced by 99.46% by using the improved genetic algorithm, which is better than the traditional GA. The experimental results show that the imbalance of the two correction surfaces can be reduced to 640 and 760 g·mm, respectively, which is 86.7% and 87.1% better than the zero-degree assembly.

Low Earth orbit constellation design using a multi-objective genetic algorithm for GNSS reflectometry missions

Spaceborne global navigation satellite system reflectometry (GNSS-R) is an innovative bistatic radar remote sensing technique utilizing low Earth orbit (LEO) based GNSS-R instruments to acquire GNSS L-band opportunistic signals for measuring geophysical parameters. A GNSS-R LEO constellation with an optimization design for its specialized missions is very significant and necessary. However, the constellation design involves multi-parameter and multi-objective optimization, and the classical analytic solution is not capable of such a complicated issue. This study proposes a multi-objective LEO constellation design method with a genetic algorithm (GA) and presents a framework for designing two GNSS-R LEO constellations, termed “lower-latitude constellation” for typhoons and hurricanes observation in the tropics and “global constellation” for global geophysical parameter measurements. Then, the observation capability of both designed constellations is evaluated in terms of the number of reflection points, spatial coverage density, and revisit time to verify the GA efficiency in LEO constellation design. Results show that the two designed LEO constellations with high fitness function values possess optimal orbit parameter set configuration and outperform the existing CyGNSS constellations in observation performance. Compared with CyGNSS, the number of reflection points observed by the lower-latitude constellation and the global constellation increases by 38% and 45%, as well as the spatial coverage density increases by 28% and 36%. The revisit time for the lower-latitude constellation is reduced by 0.29 h, whereas the revisit time for the global constellation increases by one hour.

Inversion of chlorophyll content under the stress of leaf mite for jujube based on model PSO-ELM method.

During the growth season, jujube trees are susceptible to infestation by the leaf mite, which reduces the fruit quality and productivity. Traditional monitoring techniques for mites are time-consuming, difficult, subjective, and result in a time lag. In this study, the method based on a particle swarm optimization (PSO) algorithm extreme learning machine for estimation of leaf chlorophyll content (SPAD) under leaf mite infestation in jujube was proposed. Initially, image data and SPAD values for jujube orchards under four severities of leaf mite infestation were collected for analysis. Six vegetation indices and SPAD value were chosen for correlation analysis to establish the estimation model for SPAD and the vegetation indices. To address the influence of colinearity between spectral bands, the feature band with the highest correlation coefficient was retrieved first using the successive projection algorithm. In the modeling process, the PSO correlation coefficient was initialized with the convergent optimal approximation of the fitness function value; the root mean square error (RMSE) of the predicted and measured values was derived as an indicator of PSO goodness-of-fit to solve the problems of ELM model weights, threshold randomness, and uncertainty of network parameters; and finally, an iterative update method was used to determine the particle fitness value to optimize the minimum error or iteration number. The results reflected that significant differences were observed in the spectral reflectance of the jujube canopy corresponding with the severity of leaf mite infestation, and the infestation severity was negatively correlated with the SPAD value of jujube leaves. The selected vegetation indices NDVI, RVI, PhRI, and MCARI were positively correlated with SPAD, whereas TCARI and GI were negatively correlated with SPAD. The accuracy of the optimized PSO-ELM model (R2 = 0.856, RMSE = 0.796) was superior to that of the ELM model alone (R2 = 0.748, RMSE = 1.689). The PSO-ELM model for remote sensing estimation of relative leaf chlorophyll content of jujube shows high fault tolerance and improved data-processing efficiency. The results provide a reference for the utility of UAV remote sensing for monitoring leaf mite infestation of jujube.

Chaos Particle Swarm Optimization Enhancement Algorithm for UAV Safe Path Planning

For safe path planning of unmanned aerial vehicles (UAVs) in a three-dimensional (3D) environment with multiple threats, first, a cost function is introduced according to the terrain constraints and UAV overall performance constraints of the path planning problem. Then, improved nonlinear dynamic inertia weights (INDIW) are introduced into the particle swarm optimization (PSO) algorithm, and when the particles fall into the local optimum, the velocity is perturbed, and the velocity and improved nonlinear dynamic inertia weight PSO (VAINDIWPSO) algorithm are obtained. The algorithm improves the speed of convergence and fitness function value of the PSO algorithm. However, the impact of flyable path optimization is now not obvious. Therefore, to further enhance the overall performance of the VAINDIWPSO algorithm, the adaptive adjustment of the velocity is introduced, the chaotic initialization is carried out, and the improved logistic chaotic map is introduced into the algorithm, and an improved chaotic-VAINDIWPSO (IC-VAINDIWPSO) algorithm is obtained. Then, the corresponding relationship between the algorithm and constraints is used to efficiently search complicated environments and find paths with excessive security and small cost function. The simulation outcomes exhibit that in a complicated environment the IC-VAINDIWPSO algorithm substantially improves the speed of convergence of the algorithm, reduces the fitness function value of the algorithm and the initialization time of the algorithm, and the acquired path is additionally smoother. A near-optimal solution is obtained.

BFRA: A New Binary Hyper-Heuristics Feature Ranks Algorithm for Feature Selection in High-Dimensional Classification Data

Feature selection is one of the main issues in machine learning algorithms. In this paper, a new binary hyper-heuristics feature ranks algorithm is designed to solve the feature selection problem in high-dimensional classification data called the BFRA algorithm. The initial strong population generation is done by ranking the features based on the initial Laplacian Score (ILR) method. A new operator called AHWF removes the zero-importance or redundant features from the population-based solutions. Another new operator, AHBF, selects the key features in population-based solutions. These two operators are designed to increase the exploitation of the BFRA algorithm. To ensure exploration, we introduced a new operator called BOM, a binary counter-mutation that increases the exploration and escape from the BFRA algorithm’s local trap. Finally, the BFRA algorithm was evaluated on 26 high-dimensional data with different statistical criteria. The BFRA algorithm has been tested with various meta-heuristic algorithms. The experiments’ different dimensions show that the BFRA algorithm works like a robust meta-heuristic algorithm in low dimensions. Nevertheless, by increasing the dataset dimensions, the BFRA performs better than other algorithms in terms of the best fitness function value, accuracy of the classifiers, and the number of selected features compared to different algorithms. However, a case study of sentiment analysis of movie viewers using BFRA proves that BFRA algorithms demonstrate affordable performance.

LCIPA: Lightweight clustering protocol for industry 4.0 enabled precision agriculture

Smart agriculture using the Internet of Things (IoT) is an Industry 4.0 technology used in rural and urban environments. Industry 4.0 is a new class of the Industrial Revolution that focuses heavily on automation, interconnection, real-time data, and artificial intelligence (AI). With the growing demand for agricultural automation to increase farm output while meeting minimal criteria, smart farming has emerged as one of the fundamental applications of Industry 4.0. We propose an AI-based smart farming protocol since AI approaches are essential for the performance improvement of Industry 4.0 standards. We design Lightweight Clustering Protocol for Industry 4.0 Enabled Precision Agriculture (LCIPA) protocol via clustering and routing algorithms. Smart farming such as Industry 4.0, tackles long-distance communications, energy efficiency, computing efficiency, and assured QoS performance. In the clustering phase, we compute the direct and indirect sensor node parameters to produce the integrated fitness function value. The optimal selection of Cluster Head (CH) and reliable data forwarders is obtained using this novel fitness function. We establish the objective function for clustering and routing to reduce energy consumption and communication overhead while maximizing network performance using an integrated fitness function. The proposed fitness function is used to develop the Bacterial Foraging Algorithm (FBA) for optimum CH selection. A lightweight route discovery approach is based on periodic fitness ratings for inter-cluster and intra-cluster data transfer without specific processing. The simulation results reveal the efficiency of the LCIPA protocol compared to underlying methods.

A two-stage hierarchical model for spatial location and evacuation allocation problem of urban earthquake shelters: a case study in Central urban area of Yangbi county, Yunnan province, China

The scientific planning of urban shelters can greatly reduce property losses and casualties, and can improve the efficiency and safety of post-disaster evacuation. Based on the hierarchical characteristics of shelters and the shortcomings of previous studies on evacuation demands and blocked roads, this study proposes a two-stage multi-hierarchy planning method for shelters. In the first stage, the construction cost of shelters is taken as the objective function to complete the spatial configuration of shelters. In the second stage, the evacuation allocation model for refugees is established, which minimizes the total evacuation distance. The planning method also establishes the calculation method for dynamic high-precision refuge demands, and considers the blockage of evacuation roads caused by damaged buildings. The traditional genetic algorithm is improved and hierarchical 0-1 coding is adopted to solve the spatial configuration model of hierarchical shelters. An “exchange” heuristic algorithm is proposed to solve the hierarchical evacuation allocation model. Finally, Yangbi County in Yunnan Province, China, is taken as an empirical case to verify the applicability and accuracy of the proposed method. When the algorithm iterates to 150 rounds, the fitness function value converges to 3040675, indicating that 61 new shelters should be constructed to satisfy the refuge demands, and the minimum construction cost of these shelters is 3,040,675 RMB. The minimum evacuation distances in the first, second, and third evacuation stages are determined to be 51645192, 17351731, and 16136090 m, respectively.

Constructing an optimal portfolio on the Bulgarian stock market using hybrid genetic algorithm for pre- and post-COVID-19 periods

In the aftermath of the COVID-19 pandemic, global financial markets have seen growing uncertainty and volatility and as a consequence, precise prediction of stock price trend has emerged to be extremely challenging. In this background, we propose two time frameworks wherein the Hybrid genetic algorithm (HGA) is used to set up an optimal portfolio included ten stocks traded in Bulgarian stock market during pre and post COVID-19 periods. The fitness function values of constructed HGA during pre- and post-COVID-19 periods were −7.194e[Formula: see text] and −7.014e[Formula: see text], respectively. The estimated nonzero portfolio weights during pre-COVID-19 period were ALCM (0.025), HNVK (0.253), HVAR (0.378), MSH (0.204), NEOH (0.038), and SFT (0.102) while during post-COVID-19 period were AGH (0.003), ALCM (0.015), HNVK (0.272), HVAR (0.460), MSH (0.142), NEOH (0.057), SFT (0.031), and SPH (0.021). The corresponding expected portfolio return and portfolio risk during pre-COVID-19 period were 9.825e[Formula: see text] and 7.163e[Formula: see text] while during post-COVID-19 period were 9.656e[Formula: see text] and 6.895e[Formula: see text], respectively.

Particle swarm optimization for procedural content generation in an endless platform game

Given the ever-increasing competition in the digital gaming industry, induced by a market of an exponentially growing gamer population, the production of creative, coherent, and appealing games has become inherently more complex. Creating game content by hand is both costly and time-consuming. By automating or assisting programmers and designers in their tasks, the techniques of procedural content generation (PCG) for games may address these challenges. PCG is not new, being active for several decades. However, the more traditional (and popular) form of PCG is somehow limited. It relies on some basic techniques ranging from simple pseudo-random number generators, generative grammars, image filtering, and spatial algorithms. The most advanced forms of PCG may utilize the modeling and simulation of complex systems and techniques from Artificial Intelligence (AI). In this context, this paper proposes a novel PCG approach, which is based on an optimization algorithm, known as Particle Swarm Optimization (PSO). Our approach is tested on a 2D endless platform runner game. The game structure (based on the Godot Game Engine) and a previous solution using Genetic Algorithms (GA) were first proposed and evaluated in an earlier work. In this paper, besides proposing the novel solution using PSO, we performed a comparative evaluation between the novel and previous solutions. The fitness function, utilized by both algorithms, takes into account the game’s environment aesthetics, physics, and some rules associated with gameplay, so that the generated environments are both enjoyable and playable. Our experiments evaluated time viability for in-game real-time generation and convergence to high/stable fitness values. We discovered hyper parameter ranges that yielded viable solutions. In the end, PSO has proven to be better suited to the investigated PCG task than the GA, since it presented faster convergence time and higher fitness function values.