Dynamic Optimization Algorithm Research Articles

Research on Dynamic Optimization Algorithm of Warehousing Location Layout Based on Nonlinear Optimization

The paper aims to improve the turnover rate and operation efficiency of goods that are shipped out and replenished in the warehouses of electric power enterprises through big data analysis and optimization algorithms.The data is distributed in diverse locations and data nonlinear optimization algorithms certainly helps to understand the patterns for effective management of warehouses.This article focuses on reducing the delay in the operational processes.A multi-objective optimization (MOO) has been proposed which is aiming at improving the efficiency of transition process of commodities, storage, and overall warehouse operations.The study helps in the optimization of the allocation of cargo spaces with the aid of big data analysis optimization technology which collects and manages data in a distributed environment. A multi-objective cargo space optimization algorithm is proposed along with consideration of dynamic constraints.The algorithm is based on the coefficient of variation adaptive differential evolution algorithm.Individual decoding is performed according to the real-time cargo space availability.The simulation results show that the convergence speed of the algorithm is greatly improved.Meanwhile, the efficiency of warehouse transition process, shelf stability and the classification of commodities are remarkably improved.In nutshell, the multi-objective decision-making with the integration of big data analysis optimization technology assists in the effective organization of warehouse allocation system by considering multiple factors and constraints.

Dynamic Scheduling of Multi-agent Electromechanical Production Lines based on Iterative Algorithms

In response to the optimization scheduling problem in the dyeing production process, the author proposes a hierarchical scheduling method for dyeing vats based on genetic algorithm and multi-agent. In this method, a hierarchical scheduling algorithm is used to decompose production scheduling into static and dynamic strategies. The static strategy adopts a genetic algorithm that supports batch processing of multiple products, non equality of equipment, order delivery time, switching cost, and other constraints: Dynamic strategy is a coordinated dynamic optimization algorithm that uses multi-agent systems to support the running status of dye tanks based on static strategies. By solving the algorithm with multiple constraints and dynamic factors in the production process, the final result of the dyeing tank operation task is obtained. The simulation compared pure genetic algorithm with manual scheduling, and the results showed that the hierarchical dynamic scheduling strategy based on data-driven achieved the goal of optimizing the production scheduling of dyeing vats. The practical application results also demonstrate the feasibility of this method.

Optimization and Performance Evaluation of Artificial Intelligence Algorithms in Game Design

This article aims to optimize artificial intelligence algorithms in game design to enhance game performance and player experience. Therefore, an innovative method called Dynamic Game AI Fitness Optimization Algorithm (DGAFOA) was proposed in the article, and its effectiveness was verified through experiments. In the experimental section, this article applies the DGAFOA algorithm to a typical role-playing game and compares it with traditional fixed parameter AI algorithms. The experimental results show that the DGAFOA algorithm exhibits significant advantages in key indicators such as game task completion rate and player satisfaction. Specifically, game AI using the DGAFOA algorithm can respond more quickly and accurately to player behavior, improving the overall smoothness and fun of the game. In addition, the DGAFOA algorithm also introduces ε- The greedy strategy balances exploration and utilization, effectively avoiding the problem of AI getting stuck in local optima. This enables game AI to maintain stable performance while still possessing the ability to explore new strategies, bringing players more surprises and challenges.

Multi-objective Trust-aware Dynamic Weight Pelican Optimization Algorithm for Secure Cluster Head and Routing Selection in WSN

Wireless Sensor Networks (WSN) is a group of Sensor Nodes (SNs) which are performed to intellect a normal singularity from an environment. The minimum resources and computational power of WSN creates vulnerable to number of security attacks. To addresses this problem, this paper proposes a Multi-objective Trust-aware Dynamic Weight Pelican Optimization Algorithm (M-TDWPOA)-based secure and energy aware multi-hop routing in WSN with the Base Station (BS). The proposed method comprises of two significant steps: Initially, dynamic energy-efficient Cluster Head (CH) selection by utilizing the multi-objective functions such as Distance between neighbor nodes, Distance between BS to CH, Energy, Centrality and Trust Threshold. Then, dynamic secure multi-hop routing is selected by the multi-objective functions such as Distance between BS to CH, Energy, Trust Threshold which is dynamically reducing the network overhead. Furthermore, a security aware multi-hop routing is employed through the different trust classes like direct, indirect, recent and authentication. The proposed M-TDWPOA approach is estimated with various performance metrics like alive nodes, dead nodes, energy consumption, Throughput and so on. The proposed M-TDWPOA achieves the minimum energy consumption of 0.4927J, 0.4851J, 0.4771J, 0.4693J and 0.4617J when compared to the existing methods like Fractional Artificial Lion (FAL).

A Novel Approach for Gender Prediction through Fingerprint Analysis using Fuzzy C-Means Algorithm and Artificial Neural Network

Wireless Sensor Networks (WSN) is a group of Sensor Nodes (SNs) which are performed to intellect a normal singularity from an environment. The minimum resources and computational power of WSN creates vulnerable to number of security attacks. To addresses this problem, this paper proposes a Multi-objective Trust-aware Dynamic Weight Pelican Optimization Algorithm (M-TDWPOA)-based secure and energy aware multi-hop routing in WSN with the Base Station (BS). The proposed method comprises of two significant steps: Initially, dynamic energy-efficient Cluster Head (CH) selection by utilizing the multi-objective functions such as Distance between neighbor nodes, Distance between BS to CH, Energy, Centrality and Trust Threshold. Then, dynamic secure multi-hop routing is selected by the multi-objective functions such as Distance between BS to CH, Energy, Trust Threshold which is dynamically reducing the network overhead. Furthermore, a security aware multi-hop routing is employed through the different trust classes like direct, indirect, recent and authentication. The proposed M-TDWPOA approach is estimated with various performance metrics like alive nodes, dead nodes, energy consumption, Throughput and so on. The proposed M-TDWPOA achieves the minimum energy consumption of 0.4927J, 0.4851J, 0.4771J, 0.4693J and 0.4617J when compared to the existing methods like Fractional Artificial Lion (FAL).

Optimal design of adaptive model predictive control based on improved GWO for autonomous vehicle considering system vision uncertainty

The tuning issue of the parameters and the system uncertainty represent big challenges in most engineering applications. In this regard, a new tuning approach is developed for adaptive model predictive control (AMPC) of autonomous vehicles (AVs). The proposed control strategy tackles the uncertainty issue of the vision system due to the variation of the velocity, road curvature, time delay, and look ahead distance. The proposed AMPC is designed depending on a new improved grey wolf optimizer (IGWO) algorithm with different learning procedures and fitness-distance balance (FDB) approach to equilibrium the exploration and exploitation manners of the original GWO. This development of GWO based on FDB can increase the capability of the GWO for global search and avoid trapping in local states. Furthermore, the proposed IGWO does not require more adjustable parameters that can enhance the convergence to the best global solution rapidly. The new IGWO is compared with recent optimization algorithms such as the mayfly optimization algorithm (MA), chimp optimization algorithm (COA), dynamic arithmetic optimization algorithm (DAOA), whale optimization algorithm (WOA), jaya algorithm (JA), archimedes optimization algorithm (AOA), and equilibrium optimizer (EO) as well as the original GWO. Besides, the performance of the designed AMPC is compared with the fuzzy logic proportional integral (FLPI) controller and the traditional MPC. Various test scenarios are performed to assert the robustness and effectiveness of the designed AMPC against the variation of the velocity, road curvature, time delay, and look ahead distance. The results emphasize that the proposed AMPC can perform the best damping response with an overshoot of around 1.3 m and a settling time of around 1.12 s less than the FLPI controller and the traditional MPC.

Enhancing unconfined compressive strength of stabilized soil with lime and cement prediction through a robust hybrid machine learning approach utilizing Naive Bayes Algorithm

The unconfined compressive strength (UCS) of stabilized soil with lime and cement is a crucial mechanical factor in developing accurate geomechanical models. In the past, determining UCS required laborious laboratory testing of core samples or complex well-log analysis, both of which consumed many resources. This study introduces a novel method for real-time UCS prediction while acknowledging the need for efficiency. This method makes use of Specific Naive Bayes (NB) predictive models that are strengthened by the smell agent optimization (SAO) and the Dynamic Arithmetic Optimization Algorithm (DAOA), two reliable meta-heuristic algorithms. Combining these algorithms improves prediction precision while streamlining the process. By examining UCS samples from various soil types obtained from earlier stabilization tests, these models are validated. This study identifies three different models: NBDA, NBSA, and a single NB. The individual insights each model provides work in concert to increase the overall UCS prediction accuracy. This approach represents a significant advancement in UCS prediction methodologies, revealing a quick and effective method with wide-ranging implications for various geomechanical applications. Meta-heuristic algorithms combined with particular NB models produce promising results, opening up new possibilities for real-time UCS estimation across various geological scenarios. Especially noteworthy are the NBDA model’s impressive performance metrics. The entire dataset achieves an R2 value of 0.992 during testing. The RMSE of 108.69 for the NBDA model during the training phase also shows that it has the best performance overall. It consistently exhibits commendable generalization and predictive abilities that outperform those of the developed NB and NBSA models, highlighting its usefulness and effectiveness in practical applications.

A novel population robustness-based switching response framework for solving dynamic multi-objective problems

In this paper, a novel population robustness-based switching response framework (PR-SRF) is proposed to develop effective dynamic multi-objective optimization algorithm (DMOA), which integrates different response strategies to comprehensively cope with the dynamic behaviors. In particular, the population robustness is described by the quantification of how severely the environmental changes affect current population, which is timely graded as three levels of weak, strong, and normal to enable the adaptive switch of three different responses of diversity-enhancement, diversity-maintenance, and the knowledge-transfer, respectively. In this way, associations between the adopted responses and the changing environments are successfully established, thereby facilitating more intelligent decision when handling the dynamic behaviors. According to the benchmark evaluation results, the proposed PR-SRF-DMOA yields better comprehensive performance than several other DMOAs with popular response strategies, and it also outperforms another three DMOAs with hybrid responses, which demonstrates the great competitiveness of our algorithm. In addition, ablation study proves that the proposed PR-SRF can sufficiently exploit the merits of different responses, which effectively alleviates the negative knowledge transfer in extremely fluctuating environments, thereby providing valuable references for the development of evolutionary transfer optimization (ETO) algorithms.

A memetic dynamic coral reef optimisation algorithm for simultaneous training, design, and optimisation of artificial neural networks

Artificial Neural Networks (ANNs) have been used in a multitude of real-world applications given their predictive capabilities, and algorithms based on gradient descent, such as Backpropagation (BP) and variants, are usually considered for their optimisation. However, these algorithms have been shown to get stuck at local optima, and they require a cautious design of the architecture of the model. This paper proposes a novel memetic training method for simultaneously learning the ANNs structure and weights based on the Coral Reef Optimisation algorithms (CROs), a global-search metaheuristic based on corals’ biology and coral reef formation. Three versions based on the original CRO combined with a Local Search procedure are developed: (1) the basic one, called Memetic CRO; (2) a statistically guided version called Memetic SCRO (M-SCRO) that adjusts the algorithm parameters based on the population fitness; (3) and, finally, an improved Dynamic Statistically-driven version called Memetic Dynamic SCRO (M-DSCRO). M-DSCRO is designed with the idea of improving the M-SCRO version in the evolutionary process, evaluating whether the fitness distribution of the population of ANNs is normal to automatically decide the statistic to be used for assigning the algorithm parameters. Furthermore, all algorithms are adapted to the design of ANNs by means of the most suitable operators. The performance of the different algorithms is evaluated with 40 classification datasets, showing that the proposed M-DSCRO algorithm outperforms the other two versions on most of the datasets. In the final analysis, M-DSCRO is compared against four state-of-the-art methods, demonstrating its superior efficacy in terms of overall accuracy and minority class performance.

Dynamic adaptive quantum approximate optimization algorithm for shallow, noise-resilient circuits

Dynamic adaptive quantum approximate optimization algorithm for shallow, noise-resilient circuits

Intelligent driving model considering vehicular dynamics and heterogeneous road environments

ABSTRACT Understanding vehicles’ movement in complex environments becomes cruical with the fast development of connected automated vehicles (CAVs). Current microscopic traffic flow models lack consideration for vehicle dynamics and complex road topologies. This study develops a model addressing these issues, retrieving vehicles’ maneuvers and predicting vehicles’ two-dimensional motion. It introduces a two-dimensional intelligent driving model utilizing steering angle and acceleration as control inputs. Intricate road topologies are represented with potential fields and a virtual boundary to capture the heterogeneous environment’s complexity. A driving potential field model is also developed for off-ramp areas. Model parameters are optimized with the dynamic time warping (DTW) and particle swarm optimization (PSO) algorithm. Furthermore, model predictive control (MPC) enhances the realism of the model’s output. Field validation results demonstrate that the proposed models can accurately describe vehicles’ two-dimensional movement in intricate environments, offering valuable support for research on heterogeneous traffic flows for CAVs and CVs.

Investigation of mechanical properties of high-performance concrete via optimized neural network approaches

In this paper, an artificial intelligence approach has been employed to analyze the slump and compressive strength (CS) of high-performance concrete (HPC), focusing on its mechanical properties. The importance of assessing these critical concrete characteristics has been widely acknowledged by experts in the field, leading to the development of innovative methods for estimating parameters that typically require laboratory testing. These intelligent techniques improve the accuracy of mechanical property predictions and reduce the resource-intensive and costly nature of experimental work. The radial basis function neural network (RBFNN) is the foundational model for predicting the mechanical attributes of various HPC mixtures. To fine-tune the RBFNN’s performance in replicating the mechanical properties of HPC samples, two optimization algorithms, namely the Golden Eagle Optimizer (GEO) and Dynamic Arithmetic Optimization Algorithm (DAOA), have been employed. In this manner, both RBGE and RBDA models were trained using a dataset comprising 181 HPC samples that included superplasticizers and fly ash. The results show that DAOA has significantly improved the base model’s predictive capability, achieving a higher correlation with a value R2 of 0.936 when estimating slump. Furthermore, RBDA exhibited a more favorable root mean square error (RMSE) in predicting compressive strength compared to RBGE, with a notable 16% difference. Ultimately, both integrated models demonstrated their effectiveness in accurately modeling the mechanical properties of HPC.

Research on data mapping and fusion method of ship production workshop based on digital twins

Aiming at the problems existing in discrete manufacturing workshops of ships, such as the lack of real-time management and control ability of on-site production status, and the long time delay of real-time interaction and fusion between physical entities and virtual entities data, this paper proposes ship production workshop data mapping and fusion method based on digital twins. Taking a ship machining workshop as an application research example, the integrated business process and data mapping and fusion method of the machining workshop based on digital twins are studied. On this basis, the dynamic scheduling optimization algorithm of ship machining workshops based on digital twins is studied. The results show that the data mapping and fusion method in this study can significantly reduce the time delay of data interaction between the virtual and real objects. It can effectively improve the real-time and production flexibility of dynamic and complex tasks such as emergency insertion and task rework. The ability of real-time data interaction and synchronous real-time mapping between physical entities and virtual entities in dynamic scenes such as production line reconstruction and customized production is improved.

Optimization of Installation Position for Complex Space Curve Weldments in Robotic Friction Stir Welding Based on Dynamic Dual Particle Swarm Optimization

Robotic friction stir welding (RFSW), with its wide application range, ample working space, and task flexibility, has emerged as a vital development in friction stir welding (FSW) technology. However, the low stiffness of serial industrial robots can lead to end-effector deviations and vibrations during FSW tasks, adversely affecting the weld quality. This paper proposes a dynamic dual particle swarm optimization (DDPSO) algorithm through a new comprehensive stability index that considers both the stiffness and vibration stability of the robot to optimize the installation position of complex space curve weldments, thereby enhancing the robot’s stability during the FSW process. The algorithm employs two independent particle swarms for exploration and exploitation tasks and dynamically adjusts task allocation and particle numbers based on current results to fully utilize computational resources and enhance search efficiency. Compared to the standard particle swarm optimization (PSO) algorithm, the DDPSO approach demonstrated superior search capabilities and stability of optimization results. The maximum fitness value improved by 4.2%, the average value increased by 12.74%, and the concentration level of optimization results rose by 72.91% on average. The new optimization method pioneers fresh perspectives for optimizing the stability of RFSW, providing significant grounds for the process optimization and offline programming of complex spatial curve weldments.

Optimizing air conditioning loads scheduling: A bi‐objective dynamic optimization algorithm considering life loss impact

AbstractAir conditioning loads (ACLs) can actively contribute to optimizing energy usage and increasing renewable energy utilization rate through scheduling. Existing studies on the participation of ACLs in DR have mainly focused on developing ON/OFF strategies, little consideration has been given to the impact of DR on the life loss of ACLs. In light of this, the present study first introduces an equation for quantifying the life loss of ACLs. Building upon this, an optimizing ACLs scheduling model is established considering life loss impact. As the relationship between life loss and refrigeration power is a complex non‐linear high‐order fractional function, it cannot be solved by conventional commercial solvers. Therefore, a bi‐objective dynamic optimization algorithm is proposed based on non‐linear fractional programming (NFP) to address this problem. To assess the effectiveness of our algorithm, it is used in a microgrid case with three schemes. Scheme 3 is aquatinted to consider the economic cost and the life loss of ACLs simultaneously, aiming to maximize the comprehensive benefit of the scheduling scheme. The results confirm the efficiency and superiority of the proposed algorithm.

A Novel Improved Whale Optimization Algorithm for Global Optimization and Engineering Applications

The Whale Optimization Algorithm (WOA) is a swarm intelligence algorithm based on natural heuristics, which has gained considerable attention from researchers and engineers. However, WOA still has some limitations, including limited global search efficiency and a slow convergence rate. To address these issues, this paper presents an improved whale optimization algorithm with multiple strategies, called Dynamic Gain-Sharing Whale Optimization Algorithm (DGSWOA). Specifically, a Sine–Tent–Cosine map is first adopted to more effectively initialize the population, ensuring a more uniform distribution of individuals across the search space. Then, a gaining–sharing knowledge based algorithm is used to enhance global search capability and avoid falling into a local optimum. Finally, to increase the diversity of solutions, Dynamic Opposition-Based Learning is incorporated for population updating. The effectiveness of our approach is evaluated through comparative experiments on blackbox optimization benchmarking and two engineering application problems. The experimental results suggest that the proposed method is competitive in terms of solution quality and convergence speed in most cases.

Optimal Configuration of Wind–Solar–Thermal-Storage Power Energy Based on Dynamic Inertia Weight Chaotic Particle Swarm

The proposed approach involves a method of joint optimization configuration for wind–solar–thermal-storage (WSTS) power energy bases utilizing a dynamic inertia weight chaotic particle swarm optimization (DIWCPSO) algorithm. The power generated from the combination of wind and solar energy is analyzed quantitatively by using the average complementarity index (ACI) to determine the optimal ratio of wind and solar installations. We constructed a multi-objective optimization configuration model for the WSTS power generation systems, considering the equivalent annual income and the optimal energy consumption level as objective functions of the system. We solved the model using the chaotic particle swarm optimization algorithm with linearly decreasing dynamic inertia weight. To validate the effectiveness of the proposed approach, we conducted a simulation using the 2030 power energy base planning data of a particular region in Inner Mongolia. The results demonstrate that the proposed method significantly improves the annual income, enhances the consumption of wind–solar energy, and boosts the power transmission capacity of the system.

Novel Enriched Indus River Flow Dynamics Optimization Algorithm to solve the Electrical Energy -Active Power Loss Reduction and Voltage Stability Enhancement

Novel Enriched Indus River Flow Dynamics Optimization Algorithm to solve the Electrical Energy -Active Power Loss Reduction and Voltage Stability Enhancement

An Enhanced Particle Swarm Optimization Algorithm via Adaptive Dynamic Inertia Weight and Acceleration Coefficients

The particle swarm optimization (PSO) algorithm counts among the most popular metaheuristic algorithms based on swarm intelligence. Since the publication of the first article on this optimization technique, researchers have developed many PSO variants with some improvement in its performance. The PSO optimization performance hinges on its ability to achieve a good exploration-exploitation balance. The most common method that helps to improve exploration-exploitation balance is modifying the PSO three controlling parameters, namely the inertia weight and two acceleration coefficients. In this paper a PSO variant that combines adaptive dynamic inertia weight and adaptive dynamic acceleration coefficients to enhance the exploration-exploitation balance of the PSO is proposed. The enhanced PSO algorithm called Adaptive Dynamic Inertia Weight and Acceleration Coefficient Optimization (ADIWACO) algorithm is tested on seven well-known standard test functions comprising four unimodal and three multimodal ones. The performance of the PSO is then compared with that of the standard PSO (SPSO) and four existing PSO variants. The experimental results comprising optimum value, runtime, mean value, standard deviation and convergence rate, and confirmed by the results of ranking statistics and Wilcoxon signed rank test conducted on the experimental results, indicate significantly better performance by the proposed PSO algorithm.

Solving Traveling Salesman Problem Using Parallel River Formation Dynamics Optimization Algorithm on Multi-core Architecture Using Apache Spark

According to Moore’s law, computer processing hardware technology performance is doubled every year. To make effective use of this technological development, the algorithmic solutions have to be developed at the same speed. Consequently, it is necessary to design parallel algorithms to be implemented on parallel machines. This helps to exploit the multi-core environment by executing multiple instructions simultaneously on multiple processors. Traveling Salesman (TSP) is a challenging non-deterministic-hard optimization problem that has exponential running time using brute-force methods. TSP is concerned with finding the shortest path starting with a point and returning to that point after visiting the list of points, provided that these points are visited only once. Meta-heuristic optimization algorithms have been used to tackle TSP and find near-optimal solutions in a reasonable time. This paper proposes a parallel River Formation Dynamics Optimization Algorithm (RFD) to solve the TSP problem. The parallelization technique depends on dividing the population into different processors using the Map-Reduce framework in Apache Spark. The experiments are accomplished in three phases. The first phase compares the speedup, running time, and efficiency of RFD on 1 (sequential RFD), 4, 8, and 16 cores. The second phase compares the proposed parallel RFD with three parallel water-based algorithms, namely the Water Flow algorithm, Intelligent Water Drops, and the Water Cycle Algorithm. To achieve fairness, all algorithms are implemented using the same system specifications and the same values for shared parameters. The third phase compares the proposed parallel RFD with the reported results of metaheuristic algorithms that were used to solve TSP in the literature. The results demonstrate that the RFD algorithm has the best performance for the majority of problem instances, achieving the lowest running times across different core counts. Our findings highlight the importance of selecting the most suitable algorithm and core count based on the problem characteristics to achieve optimal performance in parallel optimization.