Elite Retention Strategy Research Articles

The route problem of multimodal transportation with timetable: stochastic multi-objective optimization model and data-driven simheuristic approach

PurposeDistribution of long-haul goods could be managed via multimodal transportation networks where decision-maker has to consider these factors including the uncertainty of transportation time and cost, the timetable limitation of selected modes and the storage cost incurred in advance or delay arriving of the goods. Considering the above factors comprehensively, this paper establishes a multimodal multi-objective route optimization model which aims to minimize total transportation duration and cost. This study could be used as a reference for decision-maker to transportation plans.Design/methodology/approachMonte Carlo (MC) simulation is introduced to deal with transportation uncertainty and the NSGA-II algorithm with an external archival elite retention strategy is designed. An efficient transformation method based on data-drive to overcome the high time-consuming problem brought by MC simulation. Other contribution of this study is developed a scheme risk assessment method for the non-absolutely optimal Pareto frontier solution set obtained by the NSGA-II algorithm.FindingsNumerical examples verify the effectiveness of the proposed algorithm as it is able to find a high-quality solution and the risk assessment method proposed in this paper can provide support for the route decision.Originality/valueThe impact of timetable on transportation duration is analyzed and making a detailed description in the mathematical model. The uncertain transportation duration and cost are represented by random number that obeys a certain distribution and designed NSGA-II with MC simulation to solve the proposed problem. The data-driven strategy is adopted to reduce the computational time caused by the combination of evolutionary algorithm and MC simulation. The elite retention strategy with external archiving is created to improve the quality of solutions. A risk assessment approach is proposed for the solution scheme and in the numerical simulation experiment.

Emergency logistics network design based on space–time resource configuration

The occurrence of natural disasters or accidents causes the obstruction or interruption of road traffic connectivity and affects the transportation of essential materials, especially for cross-regional delivery under emergency situations. Affected by COVID-19, government administrators establish cross-regional quarantine roadblocks to reduce the risk of virus transmission caused by cross-regional transportation. In this study, we propose an emergency logistics network design problem with resource sharing under collaborative alliances. We construct a state–space–time network-based bi-objective mixed integer programming model to optimize the vehicle routes in order to meet customer demands for essential materials with the lowest cost and highest emergency response speed under limited transportation resources. A two-stage hybrid heuristic algorithm is then proposed to find good-quality solutions for the problem. Clustering results are obtained using a 3D k-means clustering algorithm with the consideration of time and space indices. The optimization of the initial population generated by the improved Clarke and Wright saving method and improved nondominated sorting genetic algorithm-II with elite retention strategy provides stable and excellent performance for the searching of Pareto frontier. The cost difference of the entire emergency logistics network before and after collaboration, i.e., the profit, is fairly allocated to the participants (i.e., logistics service providers) through the Shapley value method. A real-world case in Chongqing City, China is used to validate the effectiveness of the proposed model and algorithm. This study contributes to smart transportation and logistics system in emergency planning and has particular implications for the optimal response of existing logistics system to the current COVID-19 pandemic.

Economic scheduling of compressed natural gas main station considering critical peak pricing

Compressed natural gas has been proven to be more advantageous than gasoline and diesel in terms of emission cleanliness and equipment wear. The replacement of gasoline and diesel by CNG can be accelerated through the economic scheduling of CNG fueling stations. Due to the highly electrified equipment in the station, electricity cost is an important part of CNG operation costs. The critical peak pricing mechanism being implemented by the grid company provides opportunities for the economic scheduling of CNG fueling stations. This paper presents an improved operation model for a CNG main station, which considers the pre-system for the first time, including the dehydration device and buffer tank. Then, considering critical peak pricing, an economic scheduling model for the CNG main station is proposed. This optimal scheduling model is searched by an improved multi-population genetic algorithm combined with the elite retention strategy and repair operator. The results show that the electricity operating costs were effectively reduced by 34.62%, and the switching frequency of the compressor was decreased by 62.50%.

Study on the friction resistance calculation method of a flexible shaft of wire rope based on genetic algorithm

This study aims to finding out the calculation formula of friction resistance of steel wire's flexible shaft in bending push-pull state. The operation principle is based on the adhesive friction theory and Newton's classical friction formula. The friction force f is measured by experiment under the conditions of same length L, different diameter D and different curvature K, and the curve trajectory equation is obtained by curve fitting with mathematical tools. Then, according to the experimental data, the greater the curvature of flexible shaft is (that's, the smaller the bending radius is), the greater the positive pressure between shaft core and the sheath will be. The positive pressure N between the shaft core and the sheath is equal to the curvature K direct ratio. Therefore, it can be concluded that the positive pressure N of the shaft core and sheath is proportional to the curvature K, and the proportional coefficient β is introduced. The experimental results show that the functional relationship between the friction force F and the curvature K of the flexible shaft can be derived, the diameter D and curvature K of the mandrel have a great influence on the friction resistance of the flexible shaft, that is, the empirical friction resistance formula. In order to reduce the relative error and improve the accuracy of the formula. By combining elite retention strategy with roulette method and replacing the current worst genetic algorithm with historical optimum, the parameter β in the formula is optimized. After optimization, the calculation formula of friction resistance of four flexible shafts of steel wire under bending push-pull state is obtained. The author's pioneer work has contributed to the calculation of friction resistance under bending push-pull, and the average calculation error is 5%, which meets the requirements of calculation accuracy.

Evolutionary Algorithm-Based and Network Architecture Search-Enabled Multiobjective Traffic Classification

Network traffic classification technology plays an important role in network security management. However, the inherent limitations of traditional methods have become increasingly obvious, and they cannot address existing traffic classification tasks. Very recently, neural architecture search (NAS) has aroused widespread interest as a tool to automate the manual architecture construction process. To this end, this paper proposes NAS based on multiobjective evolutionary algorithms (MOEAs) to classify malicious network traffic. The main purpose is to simplify the search space by reducing the spatial ratio and number of channels of the model. In addition, the search strategy is changed in the effective search space, and the utilized strategies include EAs with the nondominated sorting genetic algorithm with the elite retention strategy (NSGA-II), strength Pareto evolutionary algorithm (SPEA-II) and multiobjective particle swarm optimization (MOPSO) to solve the formulated multiobjective NAS. Through comprehensive comparison of the population convergence times, model accuracies, Pareto optimality sets, model complexities and running speeds of the strategies, it is concluded that the model based on NSGA-II search has the best performance. The experimental results of the current machine learning algorithms and artificial learning methods based on the network are compared, showing that our method achieved better classification performance on two public datasets with a lower computational complexity, as mainly measured by FLOPs. Our approach is able to achieve 99.806% and 99.369% F1-score with 11.501 MB and 4.718 MB FLOPs on both IDS2012 and ISCX VPN dataset respectively.

Steel logistics park layout based on market basket analysis and accessibility

In this study, we used market basket analysis and land accessibility for the layout of MG Steel Logistics Park. Due to the unreasonable cargo space layout, the outbound trucks in the MG Steel Logistics Park detour and transfer, which caused congestion. Using market basket analysis, this research took outbound trucks as the research object to measure the degree of correlation between cargo locations. Then, we measured the convenience of cargo locations by the accessibility and matched it with the steel outbound frequency. We established a model and got the layout allocation plan with the maximum degree of relevance and the minimum difference in cargo-location matching as the target. In the solution algorithm, we use roulette method and the elite retention strategy to improve the genetic algorithm. Finally, we compared the results before and after optimization. We found that the number of truck transfers was reduced by 33%, and the truck staying time was reduced by 21%.

Sustainable scheduling optimization of mixed-line production for large marine power components

The manufacturing of large marine power component including cutting, welding, shrink fitting, heat treatment is a typical industry with high-material consumption, high-energy consumption and high-environmental pollution. It is noted that green manufacturing is gradually emerging. Comprehensive optimization of energy consumption for multiple processes is an effective way to promote a cleaner production industry. To reduce carbon emission from the plant-wide production processes, a new integrated sustainable scheduling approach of mixed-line production with heterogeneous manufacturing technologies is presented in this paper. In this approach, three carbon emission formulations related to cutting, welding and heat treatment are established respectively by analyzing energy consumption and its composition. An optimization mathematical model with the goal of minimizing the total emission of carbon dioxide and the total makespan is then established. In order to solve the model, the genetic algorithm is adaptively improved. The niche and elite retention strategy are introduced in the selection operator, and the dynamic crossover operator is set to adjust the genetic parameters with its iteration and adaptive function values to improve the efficiency of the algorithm. Finally, the effectiveness of the proposed approach is validated by comparing with two types of models considering the makespan and carbon emission respectively. The results indicate that the proposed method satisfactorily tackles the mixed-line production scheduling problem of large marine power component manufacturing.

Research on rapid planning of intelligent aircraft trajectory under multiple constraints

Intelligent aircraft trajectory planning is an important support to ensure the successful completion of the mission of the aircraft, and it is also a key component of the intelligent aircraft mission planning system. Due to system performance constraints and environmental constraints, the positioning system of the intelligent aircraft cannot be accurately positioned during flight, which will cause flight errors. Excessive flight errors will lead to the failure of the mission. Therefore, error correction plays an increasingly important role in trajectory planning. This paper studies the rapid trajectory planning of intelligent aircraft under the limitation of system positioning accuracy. Based on the optimization theory, with the error generation rules and correction rules of the intelligent aircraft in flight as constraints, establishes the optimization model, and uses the fast non-dominated multi-objective optimization algorithm (NSGA-II) with elite retention strategy. This paper corrects the vertical and horizontal errors during the flight, and finally designs a reasonable intelligent aircraft trajectory that meets the minimum trajectory length and the minimum number of corrections during the flight.

An integrated processing energy modeling and optimization of automated robotic polishing system

The automated robotic polishing system (ARPS) consisting of several robotic polishing cells (RPCs) is widely adopted in polishing industry to replace manual labor. Recently, energy-saving becomes a hotspot issue in manufacturing industry because of the increase in energy costs and requirement of environmental protection. Traditionally, robot motion planning and task scheduling are carried out separately and sequentially, which constrain the potential for energy-saving. In this paper, a task energy characteristic model is proposed as a polynomial function of the feedrate override to forecast the energy consumption of the polishing process of RPC, in which the designed parameters of the RPC and the polishing process parameters are encapsulated into the polynomial coefficients based on experimental data. Furthermore, an optimization model is proposed for an ARPS with mass tasks to minimize the energy consumption, in which the robot motion planning and the task scheduling are considered integratedly. An adaptive genetic algorithm with elite retention strategy is adopted to solve the optimization model. A case study is introduced to verify the proposed approach, which demonstrates the forecast error of task energy is less than 7%, and the proposed optimization approach can reduce the energy consumption of ARPS by more than 18% compared with the original processing scheme.

Selective ensemble learning method for belief-rule-base classification system based on PAES

Traditional Belief-Rule-Based (BRB) ensemble learning methods integrate all of the trained sub-BRB systems to obtain better results than a single belief-rule-based system. However, as the number of BRB systems participating in ensemble learning increases, a large amount of redundant sub-BRB systems are generated because of the diminishing difference between subsystems. This drastically decreases the prediction speed and increases the storage requirements for BRB systems. In order to solve these problems, this paper proposes BRBCS-PAES: a selective ensemble learning approach for BRB Classification Systems (BRBCS) based on Pareto-Archived Evolutionary Strategy (PAES) multi-objective optimization. This system employs the improved Bagging algorithm to train the base classifier. For the purpose of increasing the degree of difference in the integration of the base classifier, the training set is constructed by the repeated sampling of data. In the base classifier selection stage, the trained base classifier is binary coded, and the number of base classifiers participating in integration and generalization error of the base classifier is used as the objective function for multi-objective optimization. Finally, the elite retention strategy and the adaptive mesh algorithm are adopted to produce the PAES optimal solution set. Three experimental studies on classification problems are performed to verify the effectiveness of the proposed method. The comparison results demonstrate that the proposed method can effectively reduce the number of base classifiers participating in the integration and improve the accuracy of BRBCS.

Train Operation Strategy Optimization Based on a Double-Population Genetic Particle Swarm Optimization Algorithm

Train operation strategy optimization is a multi-objective optimization problem affected by multiple conditions and parameters, and it is difficult to solve it by using general optimization methods. In this paper, the parallel structure and double-population strategy are used to improve the general optimization algorithm. One population evolves by genetic algorithm (GA), and the other population evolves by particle swarm optimization (PSO). In order to make these two populations complement each other, an immigrant strategy is proposed, which can give full play to the overall advantages of parallel structure. In addition, GA and PSO is also improved, respectively. For GA, its convergence speed is improved by adjusting the selection pressure adaptively based on the current iteration number. Elite retention strategy (ERS) is introduced into GA, so that the best individual in each iteration can be saved and enter the next iteration process. In addition, the opposition-based learning (OBL) can produce the opposition population to maintain the diversity of the population and avoid the algorithm falling into local convergence as much as possible. For PSO, linear decreasing inertia weight (LDIW) is presented to better balance the global search ability and local search ability. Both MATLAB simulation results and hardware-in-the-loop (HIL) simulation results show that the proposed double-population genetic particle swarm optimization (DP-GAPSO) algorithm can solve the train operation strategy optimization problem quickly and effectively.

Biclustering of Gene Expression Data Using Cuckoo Search and Genetic Algorithm

Biclustering analysis of gene expression data can reveal a large number of biologically significant local gene expression patterns. Therefore, a large number of biclustering algorithms apply meta-heuristic algorithms such as genetic algorithm (GA) and cuckoo search (CS) to analyze the biclusters. However, different meta-heuristic algorithms have different applicability and characteristics. For example, the CS algorithm can obtain high-quality bicluster and strong global search ability, but its local search ability is relatively poor. In contrast to the CS algorithm, the GA has strong local search ability, but its global search ability is poor. In order to not only improve the global search ability of a bicluster and its coverage, but also improve the local search ability of the bicluster and its quality, this paper proposed a meta-heuristic algorithm based on GA and CS algorithm (GA-CS Biclustering, Georgia Association of Community Service Boards (GACSB)) to solve the problem of gene expression data clustering. The algorithm uses the CS algorithm as the main framework, and uses the tournament strategy and the elite retention strategy based on the GA to generate the next generation of the population. Compared with the experimental results of common biclustering analysis algorithms such as correlated correspondence (CC), fast, local clustering (FLOC), interior search algorithm (ISA), Securities Exchange Board of India (SEBI), sum of squares between (SSB) and coordinated scheduling/beamforming (CSB), the GACSB algorithm can not only obtain biclusters of high quality, but also obtain biclusters of high-biologic significance. In addition, we also use different bicluster evaluation indicators, such as Average Correlation Value (ACV), Mean-Squared Residue (MSR) and Virtual Error (VE), and verify that the GACSB algorithm has a strong scalability.

Application of Real-Coded Genetic Algorithm in Ship Weather Routing

Solving the problem of ship weather routing has been always a goal of nautical navigation research and has been investigated by many scientists. The operation schedule of an oceangoing ship can be influenced by wave or wind disturbances, which complicate route planning. In this paper, we present a real-coded genetic algorithm to determine the minimum voyage route time for point-to-point problems in a dynamic environment. A fitness assignment method based on an individual's position in the sorted population is presented, which greatly simplifies the calculation of fitness value. A hybrid mutation operator is proposed to enhance the search for the optimal solution and maintain population diversity. Multi-population techniques and an elite retention strategy are employed to increase population diversity and accelerate convergence rates. The effectiveness of the algorithm is demonstrated by numerical simulation experiments.

Modified Chaos Particle Swarm Optimization-Based Optimized Operation Model for Stand-Alone CCHP Microgrid

The optimized dispatch of different distributed generations (DGs) in stand-alone microgrid (MG) is of great significance to the operation’s reliability and economy, especially for energy crisis and environmental pollution. Based on controllable load (CL) and combined cooling-heating-power (CCHP) model of micro-gas turbine (MT), a multi-objective optimization model with relevant constraints to optimize the generation cost, load cut compensation and environmental benefit is proposed in this paper. The MG studied in this paper consists of photovoltaic (PV), wind turbine (WT), fuel cell (FC), diesel engine (DE), MT and energy storage (ES). Four typical scenarios were designed according to different day types (work day or weekend) and weather conditions (sunny or rainy) in view of the uncertainty of renewable energy in variable situations and load fluctuation. A modified dispatch strategy for CCHP is presented to further improve the operation economy without reducing the consumers’ comfort feeling. Chaotic optimization and elite retention strategy are introduced into basic particle swarm optimization (PSO) to propose modified chaos particle swarm optimization (MCPSO) whose search capability and convergence speed are improved greatly. Simulation results validate the correctness of the proposed model and the effectiveness of MCPSO algorithm in the optimized operation application of stand-alone MG.