Adaptive Crossover Operator Research Articles

Scheduling optimization model for linear projects with resource transfer in diverse construction scenarios

Studies on linear engineering projects often overlook complex construction scenarios, neglecting global resource quantities, transfer time and cost during resource deliberation. This study addresses these gaps by employing a linear scheduling method and a multi-mode resource-constrained project model. The decision variables of the model include the construction mode, construction direction, activity priority and global resource amounts, considering linear, bar and block construction scenarios. A hybrid algorithm combining the non-dominated sorting genetic algorithm-III with differential evolution is proposed for model resolution, featuring hierarchical, multi-strategy, adaptive crossover and mutation operators. Sensitivity analysis and case studies demonstrate the effectiveness and superiority of the model and algorithm. According to the study, the unit cost of resource transfer significantly influences the optimization result more than the unit time; the optimizing cost has an upper bound; and linear projects scheduling optimization with resource transfer are more realistic and provide a better trade-off between time and cost.

Probabilistic Chain-Enhanced Parallel Genetic Algorithm for UAV Reconnaissance Task Assignment

With the increasing diversity and complexity of tasks assigned to unmanned aerial vehicles (UAVs), the demands on task assignment and sequencing technologies have grown significantly, particularly for large UAV tasks such as multi-target reconnaissance area surveillance. While the current exhaustive methods offer thorough solutions, they encounter substantial challenges in addressing large-scale task assignments due to their extensive computational demands. Conversely, while heuristic algorithms are capable of delivering satisfactory solutions with limited computational resources, they frequently struggle with converging on locally optimal solutions and are characterized by low iteration rates. In response to these limitations, this paper presents a novel approach: the probabilistic chain-enhanced parallel genetic algorithm (PC-EPGA). The PC-EPGA combines probabilistic chains with genetic algorithms to significantly enhance the quality of solutions. In our approach, each UAV flight is considered a Dubins vehicle, incorporating kinematic constraints. In addition, it integrates parallel genetic algorithms to improve hardware performance and processing speed. In our study, we represent task points as chromosome nodes and construct probabilistic connection chains between these nodes. This structure is specifically designed to influence the genetic algorithm’s crossover and mutation processes by taking into account both the quantity of tasks assigned to UAVs and the associated costs of inter-task flights. In addition, we propose a fitness-based adaptive crossover operator to circumvent local optima more effectively. To optimize the parameters of the PC-EPGA, Bayesian networks are utilized, which improves the efficiency of the whole parameter search process. The experimental results show that compared to the traditional heuristic algorithms, the probabilistic chain algorithm significantly improves the quality of solutions and computational efficiency.

Handling dynamic capacitated vehicle routing problems based on adaptive genetic algorithm with elastic strategy

With the development of e-commerce platforms, the logistics industry is also booming. The transportation process is at the core of the logistics industry, which influences the timeliness and rationality of logistics distribution. In the real world, there are many dynamic demands in logistics distribution, so logistics distribution should deal with dynamic demands quickly to realize more effective route planning. This paper proposes an adaptive genetic algorithm (AGA) with elastic strategy (AGA-ES) to solve the dynamic capacitated vehicle routing problems (DCVRPs). Firstly, AGA designs an adaptive local search operator, which can adaptively adjust the quantity of nodes that need local search, according to the fitness value of different individuals. At the same time, AGA develops adaptive crossover and mutation operators, which can automatically adjust the crossover and mutation probability based on the different fitness values. The experimental results verify the better performance of AGA on the traditional capacitated vehicle routing problems (CVRPs). After that, AGA combines elastic strategy (AGA-ES) to solve DCVRPs. This paper mainly concerns three types of actual demand information in DCVRPs: distribution nodes increase, distribution nodes decrease, and distribution road conditions change, according to the actual background. AGA-ES could interact with demand information and judge the types of different demands, and then adaptively plan new and reasonable routes after receiving one or more of the above demand information. The verification of benchmark data sets shows that AGA-ES can effectively deal with DCVRPs. Furthermore, this paper collects the longitude and latitude coordinates of 100 SF express stations in Xi’an, constructs the distribution information of these express stations, and verifies that AGA-ES can solve the DCVRPs under the actual background.

A novel method for calculating harmonic contribution based on difference recurrence estimation

AbstractDetermining the harmonic contribution of each harmonic source is conducive to improving the harmonic management of the power system. The main problem with harmonic contribution calculation is determining the equivalent system harmonic impedance. The previous harmonic impedance calculation method can only calculate the impedance value when the harmonic fluctuation is significant, and the calculation fails when the harmonic fluctuation is small. Therefore, a new method called difference recurrence estimation is proposed to construct the objective function from impedance parameters. The advantages of the proposed method are low data requirement, high estimation accuracy, and excellent tracking performance. An improved genetic algorithm (GA) is presented by integrating an adaptive crossover operator and population intervention into the standard GA to improve the global searching impedance parameter ability. Based on these parameters, harmonic impedance and harmonic contribution are calculated accurately. The superiority of the proposed model is verified through simulation and field cases.

Task Scheduling Optimization in Cloud-Fog-MCS Environment Using Genetic Algorithm and Game Theory

Fog-cloud computing is a promising platform for processing Mobile Crowdsensing (MCS) tasks that come with different requirements. A fog environment is more suitable for processing time-sensitive tasks due to its proximity to the MCS layer. On the other hand. the cloud environment provides powerful resources to handle large tasks. However, due to the heterogeneity of the computing nodes, scheduling MCS tasks in a fog-cloud environment is a challenging issue. This paper presents a non-cooperative game theoretical model for the task scheduling problem of MCS tasks in the fog-cloud environment. Then, the paper presents an improved genetic algorithm to efficiently solve the problem of task scheduling game model with main enhancements including a new strategy to generate a diverse initial population, incorporating the utility function of the game theoretical model with system fitness function, and finally, the paper introduces a new strategy for population sorting and grouping with applying adaptive crossover operator to meet the specific needs of each group. This improves the exploration of the unseen regions of the search space, as well as exploiting the already-found promising solutions, ultimately leading to a faster convergence toward the optimal solution. The experimental results demonstrate that the proposed approach has better performance in terms of reducing the makespan by 26%, decreasing the energy consumption by 32.4%, decreasing total system cost by 28%, and decreasing the degree of imbalance by 21.53% as compared with other scheduling approaches such as Discrete Non-dominated Sorting Genetic Algorithm II (DNSGA-II), Grasshopper Optimization Algorithm (GOA), Grey Wolf Optimization (GWO, Time-Cost Aware Scheduling (TCaS), Moth Flame Optimization (MFO), and Bees Life Algorithm (BLA).

Optimization design of hybrid energy storage capacity configuration for electric ship

Electric propulsion has gained significant popularity in the marine vessel industry due to its numerous advantages. One of the key challenges faced in this domain is effectively managing the fluctuation of power grid caused by sudden load changes. While energy storage systems offer a viable solution, striking the right balance between cost and benefit remains a complex task. To address this issue, establish an optimization model and constraint conditions for capacity configuration of hybrid energy storage systems, and propose a decision-making method based on NSGA-II algorithm and cost-effectiveness method. To address the issue of NSGA-II algorithm being prone to local optima and premature convergence, chaotic thinking and adaptive crossover operators are introduced to improve the NSGA-II algorithm; In order to obtain the optimal configuration scheme from the obtained Pareto optimal solution set, a decision method based on cost-effectiveness was adopted to further analyze the bias between the Pareto optimal solution set and each objective. By comparing the decision results with the ideal solution (TOPSIS), the optimal capacity configuration scheme was obtained.

Path Planning of an Electric Vehicle for Logistics Distribution Considering Carbon Emissions and Green Power Trading

As environmental awareness continues to grow and government policies provide incentives, electric vehicles (EVs) are becoming more widely used in logistics distribution. Considering green power trading and carbon emissions, this paper addresses the green vehicle routing problem (GVRP) and constructs an electric vehicle path model with time windows to minimize the total cost. To solve the model, a hybrid adaptive genetic algorithm (HAGA) is proposed. An improved nearest-neighbor algorithm is adopted to improve the quality of the initial population, and the adaptive crossover and mutation operators are introduced to achieve the better solution. In addition, based on the Schneider case, HAGA is used to solve the models with and without considering green power trading separately, and the results show that considering green power trading can reduce the total cost by 3.22% and carbon emissions by 23.38 kg. Finally, the experimental simulations further prove that with the increase in case size, HAGA can effectively reduce total cost. And it is beneficial for the popularization of electric vehicles in logistics distribution.

Resilience Importance Measure and Optimization Considering the Stepwise Recovery of System Performance

Effective recovery after disruptions is essential to improve system resilience. For many distributed systems such as offshore wind farms and communication networks, spatial location characteristics and limited maintenance resources lead to discontinuous changes in performance during system recovery. However, the stepwise performance is frequently ignored. The coupling relationship between the system and maintenance teams makes it difficult to improve the system resilience from the recovery perspective. In this article, a new method that combines the resilience importance measure and enhanced pigeon-inspired optimization (PIO) is presented to optimize the system resilience under stepwise recovery conditions. First, this study proposes a resilience-oriented importance measure that evaluates the recovery priority for each component. Second, an optimization model is established to minimize the system resilience loss by joint optimization of recovery sequence and task assignment under the conditions of multiple maintenance teams. Third, the resilience importance measure is combined with roulette wheel selection to form a high-quality intitle swarm of PIO. Fourth, an importance measure-based PIO with a Gaussian mutation and adaptive crossover operator is designed to find an optimization solution for system resilience. Finally, the recovery of a network system consisting of 32 nodes and 71 edges is studied. Compared with the stochastic method and traditional PIO, the proposed method in this study reduces the system resilience loss by 48.87 and 15.54%, respectively.

Improved Genetic Algorithm for Solving Green Path Models of Concrete Trucks

In this paper, for the problem of high total fuel consumption of distribution trucks when multiple concrete-mixing plants distribute concrete together, we established a green fuel consumption model for distribution trucks and solved the model with an improved genetic algorithm to obtain a green distribution scheme for trucks. Firstly, the fuel consumption model is established for the characteristics of commercial concrete tankers; secondly, the adaptive elite retention strategy, adaptive crossover, mutation operator, and immune operation are added to the genetic algorithm to improve it; and finally, the model is solved to obtain the green distribution scheme. The total fuel consumption in this experiment was 189.6 L when the green distribution scheme was used; compared to the total fuel consumption under the original scheme (240 L), the total fuel consumption was reduced by 21.25%. The experimental results show that the total fuel consumption of delivery trucks can be significantly reduced based on the established green fuel consumption model, and the improved genetic algorithm can effectively solve the model.

An efficient genetic algorithm for multi AGV scheduling problem about intelligent warehouse

PurposeThis study aims to address the challenge of automatic guided vehicle (AGV) scheduling for parcel storage and retrieval in an intelligent warehouse.Design/methodology/approachThis study presents a scheduling solution that aims to minimize the maximum completion time for the AGV scheduling problem in an intelligent warehouse. First, a mixed-integer linear programming model is established, followed by the proposal of a novel genetic algorithm to solve the scheduling problem of multiple AGVs. The improved algorithm includes operations such as the initial population optimization of picking up goods based on the principle of the nearest distance, adaptive crossover operation evolving with iteration, mutation operation of equivalent exchange and an algorithm restart strategy to expand search ability and avoid falling into a local optimal solution. Moreover, the routing rules of AGV are described.FindingsBy conducting a series of comparative experiments based on the actual package flow situation of an intelligent warehouse, the results demonstrate that the proposed genetic algorithm in this study outperforms existing algorithms, and can produce better solutions for the AGV scheduling problem.Originality/valueThis paper optimizes the different iterative steps of the genetic algorithm and designs an improved genetic algorithm, which is more suitable for solving the AGV scheduling problem in the warehouse. In addition, a path collision avoidance strategy that matches the algorithm is proposed, making this research more applicable to real-world scheduling environments.

An Improved Strength Pareto Evolutionary Algorithm 2 with Adaptive Crossover Operator for Bi-Objective Distributed Unmanned Aerial Vehicle Delivery

With the development of the e-commerce industry, using UAVs (unmanned aerial vehicles) to deliver goods has become more popular in transportation systems. This delivery method can reduce labor costs and improve the distribution efficiency, and UAVs can reach places that are difficult for humans to reach. Because some goods are perishable, the quality of the delivery will have an impact on the customer satisfaction. At the same time, the delivery time should also meet the needs of customers as much as possible. Therefore, this paper takes the distribution distance and customer satisfaction as the objective functions, establishes a bi-objective dynamic programming model, and proposes an improved SPEA2 (strength Pareto evolutionary algorithm 2). The improved algorithm introduces the local search strategy, on the basis of the original algorithm. It conducts a local search for the better non-dominated solutions obtained in each iteration. The new dominated solutions and non-dominated solutions are determined, and the crossover operator is improved, so that the local search ability is improved, on the basis of ensuring its global search ability. The numerical experiment results show that the improved algorithm achieves an excellent performance in three aspects: the Pareto front, generation distance, and spacing, and would have a high application value in UAV cargo delivery and other MOPs (multi-objective optimization problems). The average spacing value of the improved algorithm is more than 20% smaller than SPEA2 + SDE (strength Pareto evolution algorithm 2–shift-based density estimation), which is the second-best algorithm. In the comparison of the average generation distance value, this number reaches 30%.

Innovative Design Method of Hydro-Pneumatic Suspension for Large High-Clearance Sprayer Based on Improved NSGA-II Algorithm

Large high-clearance sprayers are widely used in the field of plant protection due to their high work efficiency. Influenced by the characteristics of a large ground clearance, fast driving speed and constantly changing sprung mass, how to solve the contradiction between the vibration reduction performance of a large sprayer and the friendliness of farmland roads has become a current research hotspot. In order to improve the driving performance of the sprayers, the design, optimization and verification scheme of the hydro-pneumatic suspension of a large sprayer based on the improved NSGA-II algorithm was completely constructed in this study. The hydro-pneumatic suspension system of a sprayer was mainly designed and a real-time time-varying model under field road excitation was established. The NSGA-II algorithm was improved by introducing the adaptive crossover operator and DE mutation operator, and a real-time interactive interface between the time-varying model was established for multi-objective optimization. Finally, system simulation analysis was conducted and a vibration test bench was built for experimental verification. The results show that vibration reduction indicators improved by 19.4%, 10.7% and 4.0%, respectively, compared with those before optimization. The performance of the designed hydro-pneumatic suspension was better than that of the ordinary suspension.

Bio-Inspired Feature Selection in Brain Disease Detection via an Improved Sparrow Search Algorithm

The timely diagnosis and treatment of brain diseases have always been an essential part of saving patients with encephalopathy. At the same time, medical image analysis is a significant step in diagnosing brain diseases. This article proposes a brain disease classification approach based on an improved sparrow search algorithm (ISSA). Specifically, a set of image features is first extracted to compose a pool of features for selection from five kinds of brain hemorrhage image datasets and one of the brain tumor image dataset. Second, we design an objective function to jointly reduce the number of the selected features and improve the classification accuracy. Finally, the proposed ISSA is utilized to solve the objective function. ISSA introduces three improvement mechanisms: the tent chaotic initialization, a novel local search strategy, and adaptive crossover operation to enhance the performance of conventional SSA. Moreover, a binary operator is used to solve the discrete feature selection problems. Besides, four machine learning algorithms, which are the K-nearest neighbor (KNN), support vector machine, decision tree, and random forest, are used to calculate the classification accuracy. Experimental results show that the proposed ISSA with KNN classifier eliminates about 65.9% of useless features, while the classification accuracy rate reaches more than 85%, which indicates that the proposed algorithm is more suitable for brain disease analysis than other comparison algorithms and other classifiers. Moreover, the effectiveness of the proposed improved mechanisms and the portability of different classifiers are evaluated by tests, and convolutional neural networks are compared with ISSA to classify the brain computed tomography (CT) images.

Multi-sample learning particle swarm optimization with adaptive crossover operation

Particle swarm optimization (PSO) is a well-known optimization method used for solving various optimization problems. However, PSO suffers from premature convergence and is ineffective in balancing exploration and exploitation when solving complex optimization problems. To overcome these drawbacks of PSO, a multi-sample learning particle swarm optimization with adaptive crossover operation (MLPSO) is proposed. In MLPSO, two novel strategies, multi-sample selecting strategy (MSS) and adaptive sample crossover strategy (ASC), are used to select proper learning samples for the whole population. Firstly, in MSS, two sample pools, namely elite pool and improver pool, are used to save elites and improvers. Elites are the particles with preferable fitness, while improvers denote the particles whose fitness have been improved largely in recent consecutive generations. In each generation, two particles are randomly selected from the two sample pools respectively to breed a learning sample through crossover operation for the whole population. Therefore, the generated learning sample by MSS strategy contains more diversity information. Secondly, in ASC, various crossover operations are conducted for breeding a learning sample according to the evolutionary states. Therefore, the ASC strategy proposed in this paper can realize a better trade-off between exploration and exploitation. Finally, the performance of MLPSO is evaluated using CEC2013, CEC2017 test suites and three engineering optimization problems. Experimental results show that MLPSO outperforms compared seven competitive PSO variants and 19 meta-heuristics algorithms in most functions.

Parameter identification method of J–A hysteresis model based on improved sparrow search algorithm

Aiming at the weak capacity of the parameter identification methods of Jiles–Atherton (J–A) hysteresis model, such as slow convergence speed and low convergence accuracy, an improved sparrow search algorithm (IM-SSA) is proposed based on mutation crossover operator and T-distribution perturbation strategy in this paper to improve the convergence speed and accuracy of parameter identification method. Firstly, the initial population of the sparrow search algorithm is enriched by Tent chaotic sequence, which expand the search area. Then, the adaptive crossover and mutation operator is introduced into the finders population to enrich the diversity of the finders population and balance the global and local search ability of the algorithm. Secondly, the T-distribution perturbation or differential mutation is used to perturb the population after each iteration according to the individual characteristics, which can avoid the population singularity in the later stage of the algorithm and enhance the ability of jump out of the local optimal value of the algorithm. Finally, the proposed parameter identification method is used to identify the parameters of the J–A hysteresis model, which are used to simulate the magnetic properties of Non-oriented silicon steel sheets with different magnetic induction intensity. The calculation result is compared with the experiment ones. The results proved that the proposed method has faster convergence speed and higher convergence accuracy.

Fault feature extraction for planetary bearing of CRF pump in nuclear power plant based on TFDC-QPSO-optimised MOMEDA

To address the issue of weak fault features and difficulty in feature extraction for planetary gearbox bearings of the circulation water pump (CRF pump) unit in the conventional island of a nuclear power plant, a deconvolution method, named the multipoint optimal minimum entropy deconvolution adjusted (MOMEDA), is optimised by a mixing operator improved quantum behaviour particle swarm optimisation (QPSO) algorithm, to extract the periodic fault impulse of planetary gearbox bearings. In the mixing operator, a differential evolution operator is introduced to improve particle swarms’ diversity and enhance the algorithm’s global optimisation capability. Meanwhile, a proposed adaptive CrossOver operator is incorporated into the algorithm to increase its convergence speed. Then, combining these two types of operators can construct a parameter optimisation algorithm displaying both global optimisation capability and high algorithm execution efficiency. Consider capturing the periodic impulsive features relating to bearing faults. A fitness function based on the characteristic information of the deconvolution signal in the time and frequency domain is proposed. This function serves as the objective function to facilitate the parameter optimisation on MOMEDA. Thereby, it enhances the optimisation capability of our feature extraction method. Experiments were conducted by adopting the proposed method on the signals collected from the inner- and outer-race faulty planetary bearing in a test bed, in which favourable feature extraction results are obtained. After the comparative analysis, we observed that both significances of features extracted by this method and the execution efficiency of the algorithm are superior compared to other methods. The on-site feature extraction results of the input shaft bearing in the planetary gearbox of a CRF pump unit in a nuclear power plant also demonstrated the engineering practicability of the method proposed in this work.

An adaptive immune‐following algorithm for intelligent optimal schedule of multiregional agricultural machinery

Aiming at low efficiency of agricultural machinery scheduling, this paper proposes an adaptive immune-following algorithm (AIFA) based on immune algorithm and artificial fish swarm algorithm. The adaptive crossover operator is used to accelerate convergence, and adaptive mutation operator ensures good diversity of population. After the adaptive evolution operations are performed, the following operator based on the following behavior of artificial fish swarm algorithm is embedded into the algorithm, which improves the convergence precision and obtains the promising optimization results. Experiments on scheduling considering the breakdown of agricultural machinery are performed based on multiple regions and multiple agricultural machineries. Compared with the immune algorithm and genetic algorithm, the simulation results demonstrate that AIFA can converge faster and achieve a better optimal solution.

A multi-objective decomposition evolutionary algorithm based on the double-faced mirror boundary for a milk-run material feeding scheduling optimization problem

With the development of personalized and differentiated customization demands, mixed-model assembly lines have been increasingly adopted by manufacturing enterprises due to their low-cost and high-efficiency. However, this mode has brought great challenges to the material-feeding process which is highly coupled with the production of automobile assembly lines. This paper, therefore, proposes a milk-run material-feeding scheduling problem based on two-level logistics network for modern mixed-model assembly lines, focusing on the milk-run material-feeding process between the central warehouse and line-integrated supermarkets for the first time. An integer programming mathematical model is established with the objective of simultaneously minimizing the number of electric vehicles put into use and the maximum travelling distance of the electric vehicles. Then, a double-faced mirror boundary-based multi-objective decomposition evolutionary algorithm (MOEA/D-DFMB) is provided to solve the problem. The introduction of the double-faced mirror boundary theory helps overcome the uneven distribution of Pareto solutions. Besides, the adaptive crossover operator based on evolutionary potential judgment, the adaptive adjustment strategy of mutation probability, and the local search optimization operator are applied to the proposed algorithm, so as to enhance the search ability and the convergence speed. Finally, computational experiments of the proposed algorithm are carried out, and the feasibility and effectiveness of the proposed algorithm are evaluated by comparing with the non-dominated sorting genetic algorithm-II (NSGA-II), the strength Pareto evolutionary algorithm-II (SPEA-II), the multi-objective decomposition evolutionary algorithm based on differential evolution (MOEA/D-DE), the multi-objective red deer algorithm (MORD), and the multi-objective social engineering algorithm (MOSE). The results indicate that the proposed MOEA/D-DFMB outperforms the comparison algorithms both in solution quality and convergence rate when solving the milk-run material-feeding scheduling problem.

Path Planning of Mobile Robots Based on Improved Genetic Algoritm

With the development of intelligent manufacturing, whether from the consideration of capacity, efficiency, or convenience, the requirements for mobile robots are increasing, reasonable regional path planning is one of the most critical needs, and a genetic algorithm is the best way to solve this problem, but in some complex working environments, traditional genetic algorithms will cause some problems, such as the path is not smooth, the steering angle is too large, the number of turns is large, etc. In this paper, an improved genetic algorithm is utilized to optimize the path-planning problem of mobile robots to circumvent the common issues arising from other approaches. The Improved Genetic Algorithm (IGA) has emerged as a significant advancement in the field of optimization techniques. By incorporating adaptive features, this refined approach yields enhanced performance and accuracy when compared to traditional genetic algorithms. Building on the foundational principles of evolutionary computation, the IGA employs innovative strategies, such as adaptive crossover and mutation operators, to navigate complex solution spaces effectively. It can also reduce computation time and increase efficiency by considering various considerations, such as environmental constraints and avoiding obstacle.

Structural parameters identification for industrial robot using a hybrid algorithm

To improve the precision and reduce the movement uncertainty of the industrial robot, a novel hybrid optimization algorithm which combines adaptive genetic algorithm with simulated annealing algorithm is proposed in this article. First, for the sake of increasing the global exploring ability of relevant individuals, the adaptive crossover and mutation operator are used in the phase of adaptive genetic algorithm. If the population optimized by adaptive genetic algorithm is trapped in the local optimal area and simultaneously meets the transformation rule, then it is consequently optimized by simulated annealing to enhance the population diversity and hunt for a better solution so that the probability of finding the global optimal solution is greatly increased. Then, corresponding experiments based on single point repeatability are conducted to acquire data and identify the structural parameters of the industrial robot. Moreover, the single point repeatability test and length test are all implemented at the same time to verify the effectiveness of the proposed method. At last, the result reveals that the proposed method is effective to identify the real structural parameters of the industrial robot, thus enormously decreasing the single point repeatability and length deviation at the same time, which extremely increases the precision and decreases the movement uncertainty of the industrial robot.