Set Of Noninferior Solutions Research Articles

辊式枝条捡拾机构运动学仿真与试验分析

Apple tree branches have the physical characteristics of high cellulose content and high elasticity, and the use of traditional soft straw pickers will miss a large number of branches. It is necessary to design a picking mechanism that adapts to the physical characteristics of fruit tree branches. In the treatment of branches in modern orchards, the picking mechanism is an important part of the picking device. The success rate of picking directly affects the processing performance of the whole machine. To improve the picking rate, the roller branch picking mechanism is studied. The trajectory of the roller picking and the stress state of the branches are analyzed, and five main factors affecting the picking effect are obtained, i.e. the position relationship between the rotation center of the picking roller and the feeding roller, inclination angle of the steel teeth, the effective working length, the speed and the forward speed of the machine. A mathematical model is established to obtain the static and dynamic motion trajectories of the roller picking mechanism are obtained by using Matlab. Taking the analysis results of influencing factors as the optimization goal, the picking trajectory of the mechanism is simulated to obtain a set of non-inferior solutions. A roller branch picker is manufactured according to the parameter values and tested in the field, and the picking rate can reach 91%.

An Ecology-Oriented Single–Multi-Objective Optimal Operation Modeling and Decision-Making Method in the Case of the Ganjiang River

Hydro power has provided significant economic benefits to society due to its cleanliness and convenience. As the number of hydropower stations has increased, many serious ecological issues have also emerged. This study uses Wan’an Reservoir as its research object and investigates single–multi-objective optimal operation and decision-making regarding reservoirs for ecology-oriented operation, to meet ecological water demand and seek the optimal operation schemes for energy generation and ecological benefits. The full-process research is conducted based on the “objective-modeling constraint optimization scheme decision-making” framework. The Mann–Kendall test and ordered clustering method were used to diagnose the hydrological variation in the basin. Based on this, a hierarchical and phased ecological flow process was derived. The objectives were defined according to the flow process, and optimal operation models were constructed. The differential evolution algorithm (DE) and improved non-dominated sorting genetic algorithm-II (NSGA-II) were used to solve the models. A non-fitting curve method was used to determine the approximate inflection point of the Pareto front curve, and the curve was fitted linearly according to the approximate inflection point to obtain the conversion formula between the objectives. Based on the coefficient of variation and Mahalanobis distance, a new multi-attribute decision-making method for reservoir operation, CV-ITOPSIS, was constructed by improving the traditional TOPSIS. The results show that: (1) There is a piecewise linear contradiction between energy generation and ecological objectives, and the contradiction intensifies with an increase in incoming water frequency. (2) Before the approximate inflection point, the head significantly influences the conversion rate from the energy generation to ecology, while the discharge flow is the major influencing factor after the inflection point. The inflection point and the formula for the piecewise straight line can reveal the conversion law between the two objectives. (3) CV-ITOPSIS considers the degree of differentiation of index data and fully considers the correlation between indicators while retaining the good evaluation performance of the traditional method. It recommends the optimal benefit scheme for a multi-objective non-inferior solution set. The research results provide a theoretical foundation and decision support for the optimal ecological operation of the Ganjiang River Basin.

An Improved Multi-Objective Brain Storm Optimization Algorithm for Hybrid Microgrid Dispatch

The increasing integration of renewable energy sources into microgrids has led to challenges in achieving daily optimal scheduling for hybrid alternating current/direct current microgrids (HMGs). To solve the problem, this article presents a novel hybrid AC/DC microgrid scheduling method based on an improved brain storm optimization (BSO) algorithm. Firstly, with economic and energy storage device health as the primary objective functions, this paper proposes a dispatch model for AC-DC hybrid microgrids. To overcome the limitations of traditional algorithms, including premature convergence and can only find non-inferior solution sets, this article proposes a multi-objective BSO algorithm that integrates learning and selection strategies. Additionally, a fuzzy decision-making method is employed to achieve optimal daily dispatching and select the most suitable compromise solution. Finally, experiments are conducted to verify the effectiveness of the proposed multi-objective optimal scheduling method and to demonstrate the practicality and effectiveness of the method in real application scenarios.

Research on the location of shared bicycle parking points based on improved NSGA-II algorithm

Reasonable location selection of shared bicycle parking points is an effective way to solve the current problem of shared bicycles. Considering the constraints such as the amount of shared bicycles, the number of parking points, and the capacity of parking points, a dual-objective bicycle-sharing parking point location planning model considering the cost minimization of both bike-sharing enterprises and travelers is established, and the simulated binary crossover operator and polynomial mutation operator are introduced to improve the NSGA-II algorithm to obtain the Pareto non-inferior solution set that satisfies both bike-sharing enterprises and travelers. The effectiveness of the model and the superiority of the improved algorithm are verified by the case. The results show that the model and algorithm can provide a reference for bicycle sharing enterprises in the location of parking points.

Two-Stage Multiobjective Decision-Making Method Based on Agricultural Water-Energy-Food Nexus: Case Study in Hetao Irrigation District, China

Due to the complex feedback cycle relationship among water resources, energy, and food, the agricultural water management mode and concept with a single object or two objects as the core can no longer meet the inherent requirements of the coordinated, sustained, and healthy development of economy and society. A two-stage multiobjective decision-making method based on a θ-dominance evolutionary algorithm (θ-DEA) is proposed for the optimization of agricultural systems with a water-energy-food nexus. First, the Pareto noninferior solution set of the model is obtained by the evolutionary algorithm based on θ-DEA. Then, the economic benefit and carbon emission of each solution are evaluated, and the optimal solution is selected by using the multiobjective decision-making method based on interval-valued hesitant fuzzy sets and the technique for order preference by similarity to ideal solution (TOPSIS). Through a case study of the Hetao Irrigation District (HID), the best optimization results are obtained when the carbon footprint is 4.16×109 kg CO2 eq, and the economic benefit is 8.09×109 yuan. The example shows that the model can well select the optimal allocation solution of water resources, which not only has high economic benefits and low pollution discharge but also meets the subjective wishes of decision-makers. The model has good practicability and stability.

TOD mode: study on land planning and land use optimization for urban rail transit station area

The rational planning of land around rail transit stations in cities can effectively improve the convenience of transportation and economic development of cities. This paper briefly introduced the transit-oriented development (TOD) mode of urban planning. We constructed a hierarchical structure for evaluating the quality of land plan-ning of urban rail transit stations through the analytic hierarchy process (AHP) method. The structure started from three large aspects, i.e., traffic volume, regional environmental quality, and regional economic efficiency, and every large aspect was divided into three small aspects. Then, an optimization model was established for land planning of rail transit stations. The land planning scheme was optimized by a genetic algorithm (GA). To enhance the optimization performance of the GA, it was improved by coevolution, i.e., plural populations iterated inde-pendently, and every population replaced the poor chromosomes in the other populations with its excellent chro-mosomes in the previous process. Finally, the Jinzhonghe street station in Hebei District, Tianjin city, was taken as a subject for analysis. The results suggested that the improved GA obtained a set of non-inferior Pareto solutions when solving a multi-objective optimization problem. The distribution of solutions in the set also indicated that any two objectives among traffic volume, environmental quality, and economic efficiency was improved at the cost of the remaining objectives. The land planning schemes optimized by the particle swarm optimization (PSO) algo-rithm, the traditional GA, and the improved GA, respectively, were superior than the initial scheme, and the opti-mized scheme of the improved GA was more in line with the characteristics of the TOD mode than the traditional one and the PSO algorithm, and the fitness value was also higher. In conclusion, the GA can be used to optimize the planning design of land in rail transit areas under the TOD mode, and the optimization performance of the GA can be improved by means of coevolution.

A Multi-Objective Optimization Method for a Tractor Driveline Based on the Diversity Preservation Strategy of Gradient Crowding

This study presents a multi-objective optimization method for a tractor driveline based on the diversity maintenance strategy of gradient crowding. The objective was to address the trade-off between high power and low fuel consumption rates in a tractor driveline by optimizing the distribution of driveline ratios, aiming to enhance overall driving performance and reduce fuel consumption. This method introduces a strategy for evaluating gradient crowding to reduce non-inferior solution sets during selection to ensure the uniform and wide distribution of solutions while maintaining population diversity. The transmission ratio of a tractor is optimized by varying the input of the transmission ratios in each gear, constraining the theoretical tractor driving rate, common transmission ratio, and drive adhesion limit, and introducing the diversity maintenance strategy of gradient crowding. The goal is to reduce the loss rate of driving power and specific fuel consumption as much as possible. The analysis results demonstrate that the GC_NSGA-II algorithm, incorporating the evaluation strategy of gradient crowding, achieves greater diversity and a more uniform distribution in the front end. After verifying the algorithm, the optimized tractor showed a reduction of 41.62 (±S.D. 0.44)% in the theoretical loss rate of driving power and 62.8 (±S.D. 0.56)% in the loss rate of specific fuel consumption, indicating that the tractor’s drive performance significantly improved, accompanied by a substantial reduction in the fuel consumption rate. These findings affirm the feasibility of the proposed optimization method and provide valuable research insights for enhancing the overall performance of tractors.

Multiobjective optimization of roadheader shovel-plate parameters based on improved particle swarm optimization and grey decision.

The development of tunneling equipment still lags behind, limiting rapid and accurate tunneling and restricting efficient production in coal mines. Thus, improving the reliability and design of roadheaders becomes essential. As the shovel plate is an essential part of a roadheader, improving its parameters can increase the roadheader performance. The parameter optimization of roadheader shovel plate is multi-objective optimization. Because of conventional multiobjective optimization requires strong prior knowledge, often provides low-quality results, and presents vulnerability to initialization and other shortcomings when used in practice. We propose an improved particle swarm optimization (PSO) algorithm that takes the minimum Euclidean distance from a base value as the evaluation criterion for global and individual extreme values. The improved algorithm enables multiobjective parallel optimization by providing a non-inferior solution set. Then, the optimal solution is searched in this set using grey decision to obtain the optimal solution. To validate the proposed method, the multiobjective optimization problem of the shovel-plate parameters is formulated for its solution. Before optimization shovel-plate most important parameters l is the width of the shovel plate = 3.2 m, is the inclination angle of the shovel plate and β = ,19°. When doing optimization, set accelerated factor , population size N = 20, and maximum number of iterations = 100. In addition, speed V was restricted by , and inertia factor W was dynamic and linearly decreasing, , with and . In addition, and were set randomly in [0, 1], while optimization degree η was set to 30%. And then we executed the improved PSO, obtaining 2000 non-inferior solutions. Apply grey decision to find the optimal solution. The optimal roadheader shovel-plate parameters are l = 3.144 m and β = 16.88°. Comparative analysis is made before and after optimization, the optimized parameters were substituted into the model and simulated. Obtained that the optimized parameters of shovel-plate can reduce the mass of the shovel plate decreases by 14.3%, while the propulsive resistance decreases by 6.62%, and the load capacity increases by 3.68%. Thus jointly achieving the optimization goals of reducing the propulsive resistance while increasing the load capacity. The feasibility of the proposed multiobjective optimization method with improved particle swarm optimization and grey decision is verified, and the method can provide convenient multiobjective optimization in engineering practice.

Optimization Synthesis of Hybrid Six-Bar Mechanism With Non-Circular Gear Constraints

AbstractA novel hybrid six-bar mechanism with non-circular gear constraints and its optimal synthesis method for complex multi-pose rigid body guidance tasks are proposed. An innovative optimization module is proposed to optimize the non-circularity and smoothness of non-circular gear pitch curves. The optimization synthesis of this mechanism is divided into two steps. The first step is to conduct multi-objective constraint optimization with pose error and non-circular gear pitch curve as optimization objectives to obtain the required diversified non-inferior solution set. Second, a set of solutions are selected from the solution set to construct the optimization function of non-circular gear pitch curves. The non-circularity and smoothness of the pitch curve are optimized to the maximum extent, and the pose change is ensured to be small. Finally, the multi-pose picking and planting of vegetable pot seedlings were realized using the proposed mechanism and optimization synthesis method. The final optimization design results show that the error between the actual pose and the required pose of the compact hybrid mechanism is small. The optimization effect of non-circularity and smoothness of non-circular gear pitch curves is pronounced. The effectiveness of the mechanism is verified via kinematic simulation.

A multi-period multi-criteria replacement and rejuvenation planning of underground cables in urban distribution networks

This paper presents an approach for determining the best multi-period reliability improvement plan in urban distribution networks by cable replacement and rejuvenation considering network expansions/changes in the planning period. It determines the number and locations of cables in the network that need to be replaced or rejuvenated in each year of the considered planning period to fulfill a decision-maker's goal. The goal considered in this paper is to achieve the desired balance between the improvement of reliability indices (SAIFI, SAIDI, ASIDI) and the costs of replacement and rejuvenation, considering budgetary limitations. This goal is achieved by applying the proposed multi-criteria mixed-integer linear programming (MILP) model, which determines a set of noninferior solutions (replacement and rejuvenation plans) in each of the considered periods. These plans are then used within the proposed multi-criteria dynamic programming (MCDP) algorithm to generate a set of multi-period plans and determine the best multi-period cable replacement and rejuvenation plan. The comparison (ranking) of plans within the proposed MCDP is performed using a multiple-criteria decision making technique for order preference by similarity to ideal solution (TOPSIS). This technique enables the consideration of different preferences of the decision maker regarding the levels of improvement of the reliability indices in the network.

Multi-Objective Optimal Long-Term Operation of Cascade Hydropower for Multi-Market Portfolio and Energy Stored at End of Year

Taking into account both market benefits and power grid demand is one of the main challenges for cascade hydropower stations trading on electricity markets and secluding operation plan. This study develops a multi-objective optimal operation model for the long-term operation of cascade hydropower in different markets. Two objectives were formulated for economics benefits and carryover energy storage. One was to maximize the market utility value based on portfolio theory, for which conditional value at risk (CVaR) was applied to measure the risk of multi-markets. Another was the maximization of energy storage at the end of a year. The model was solved efficiently through a multi-objective particle swarm optimization (MOPSO). Under the framework of the MOPSO, the chaotic mutation search mechanism and elite individual retention mechanism were introduced to diversify and accelerate the non-inferior solution sets. Lastly, a dynamic updating of archives was established to collect the non-inferior solution. The proposed model was implemented on the hydropower plants on the Lancang River, which traded on the Yunnan Electricity Market (YEM). The results demonstrated non-inferior solution sets in wet, normal and dry years. A comparison in solution sets revealed an imbalanced mutual restriction between objectives, such that a 2.65 billion CNY increase in market utility costs a 13.96 billion kWh decrease in energy storage. In addition, the non-inferior solution provided various schemes for actual demands based on other evaluating indexes such as the minimum output, power generation and spillage.

A multi-objective dynamic covering location problem for hierarchical agricultural machinery maintenance facilities

It is essential to design a product service system with the adequate location of maintenance facilities for guaranteeing the fast and dependable response to failures of extremely busy agricultural machinery during harvest time. This paper aims to optimize where to locate/relocate two hierarchical levels of facilities respectively composed of maintenance stations and mobile service fleets over multiple periods of a harvest season to support the safe and stable operation of agricultural machinery. We formulate a multi-objective dynamic covering location problem for these hierarchical facilities that seeks to maximize the total demand covered within some response radii while minimizing the modification to facility locations between the periods. The augmented ɛ-constraint method based on lexicographic optimization is then developed to obtain a set of non-inferior solutions that allow us to evaluate the trade-off between multiple decision objectives. Lastly, a real-life problem is handled to illustrate the effectiveness of our multi-objective decision model and solution method. Computational results recommend the optimal decision of facility locations for implementation, examine the sensitivity analysis of selected parameters, and compare the solution obtained from the proposed model with the period-specific solutions that take no account of the changing locations of facilities between the periods.

Multi-Objective Decision Method for Airport Landside Rapid Transit Network Design.

To better deploy the landside rapid transit network for large airports, this study proposes a multi-objective transit network design model to maximize passenger demand coverage, reduce passenger travel time and minimize operational cost simultaneously. This model is formulated as an equivalent integer programming problem by predefining the transportation corridors and passengers’ OD pairs. A branch-and-cut algorithm is proposed to find a non-inferior solution set. We also conduct trade-off analysis between efficiency, effectiveness and equity under each deployment strategy using the modified Gini coefficient method. The effectiveness of the proposed model and solution algorithm are tested with rapid transit network of the Beijing Capital International Airport. Results show that among the three common network topologies, including star, tree and finger, the passenger demand coverage and travel time reduction per unit cost under star topology outperform the other two topologies. As for finger topology, the performances of the passenger demand coverage and travel time reduction are the best among the three, but the cost is the poorest. In addition, the trade-off analysis shows that the solution whose objective is to maximize passenger demand coverage has a higher efficiency and a lower unit cost than the solution whose objective is to reduce travel time. However, the latter has a higher level of equity, especially for the medium and low-cost solutions. The proposed method in this study can help the decision makers to design effective landside rapid transit networks for large airports to improve the service level.

Multi-period reconfiguration planning considering distribution network automation

This paper presents an approach for multi-period reconfiguration planning of distribution networks where remotely controlled and supervised switches (automated switches) are installed. The proposed approach employs a multi-objective mixed integer linear programming model that explicitly considers the influence of the automated switches on the interruption duration (interruption cost). It enables obtaining a set of noninferior solutions (reconfiguration plans) in each period. These plans are then used within the dynamic programming framework to identify and evaluate a number of high-quality multi-period reconfiguration plans by employing the backward induction. In this way, the proposed approach overcomes two shortcomings of the decomposition network reconfiguration planning methods: i) too short a planning horizon and ii) too few generated and evaluated alternatives. The presented results show the potential of the proposed approach to improve the multi-period reconfiguration planning process in distribution networks with automated switches.

Dynamic reconfiguration of distribution networks considering hosting capacity: A risk-based approach

This paper proposes a risk-based approach for multi-period reconfiguration that considers hosting capacity in distribution networks in the presence of load and generation uncertainty. The proposed approach employs interval RDM (Relative Distance Measure) arithmetic to describe the uncertainty of loads and generation from renewable generators (DGs), considering the correlation between loads and between loads and DGs. An interval RDM arithmetic-based multi-objective mixed-integer linear programming model is then employed to obtain a set of noninferior solutions (reconfiguration plans) in each period. It considers hosting capacity along with the cost of losses, cost of interruptions, and switching cost. The obtained plans are used within the proposed interval-valued dynamic programming framework to identify and evaluate a set of multi-period reconfiguration plans. The multi-period plan that minimizes the risk of significant financial losses is selected by employing the Hurwicz criterion for measuring risk.

Optimal Configuration of the Integrated Charging Station for PV and Hydrogen Storage

This paper designs the integrated charging station of PV and hydrogen storage based on the charging station. The energy storage system includes hydrogen energy storage for hydrogen production, and the charging station can provide services for electric vehicles and hydrogen vehicles at the same time. To improve the independent energy supply capacity of the hybrid charging station and reduce the cost, the components are reasonably configured. To minimize the configuration cost of the integrated charging station and the proportion of power purchase to the demand of the charging station, the energy flow strategy of the integrated charging station is designed, and the optimal configuration model of optical storage capacity is constructed. The NSGA-II algorithm optimizes the non-inferior Pareto solution set, and a fuzzy comprehensive evaluation evaluates the optimal configuration.

Spatiotemporal Multi‐objective Optimization for Competitive Mobile Vendors’ Location and Routing Using De Facto Population Demands

Mobile vendors such as food trucks have become viable players in the food service market as a relatively new business model based on high mobility and flexibility. To remain competitive against typical brick and mortar establishments, it is important for mobile food vendors to operate at sites that will generate sufficient revenue, which may mean navigating to locations of high demand and low competition. This study addresses the optimal locations and routing of mobile vendors along different objectives that could contribute to higher revenue. We develop a multi‐objective spatiotemporal optimization of food trucks by explicitly considering the dynamics of urban population distribution and thus potential customer demand. For an empirical application, we focus on the location and routing problems of food trucks in Seoul, Korea. The results show that several noninferior solution sets are possible for simultaneously maximizing demand capture while reducing market competition. A routing between optimal locations for food trucks is also suggested. This research could provide useful insights and practical solutions that are directly applicable to various mobile vendors.

Optimized Energy Storage System Configuration for Voltage Regulation of Distribution Network With PV Access

With the large-scale integration of renewable energy such as wind power and PV, it is necessary to maintain the voltage stability of power systems while increasing the use of intermittent renewable energy sources. The rapid development of energy storage technologies permits the deployment of energy storage systems (ESS) for voltage regulation support. This paper develops an ESS optimization method to estimate the optimal capacity and locations of distributed ESS supporting the voltage regulation of a distribution network. The electrical elements of the network integrated with PV and ESS are first modelled to simulate the voltage profile of the network. Then an improved multi-objective particle swarm optimization (PSO) algorithm is employed to minimise a weighted sum of the overall nodal voltage deviation from the nominal level across the network and across the time horizon and the energy capacity of ESS reflecting the associated investment. The improved PSO algorithm adaptively adjusts the inertia weight associated with each particle based on its distance from the best known particle of the population and introduces the cross-mutation operation for a small distance to avoid falling into local optimal solutions. Then the dynamic dense distance arrangement is taken to update the non-inferior solution set and indicate potential global optimal solutions so as to keep the scale and uniformity of the optimal Pareto solution set. To mitigate the impact of decision makers’ preference, the information entropy based technique for order of preference by similarity to ideal solution is used to select the optimal combination of the ESS access scheme and capacity from the Pareto solution set. The proposed ESS optimization method is tested based on the IEEE 24-bus system with additional imports from high-voltage power supply. The voltage profile of the network simulated without the ESS or with the random or optimized ESS placement is compared to illustrate the effectiveness of the optimized ESS in performing voltage regulation under normal operation and supporting emergency power supply during high-voltage transmission failures.

Multilevel coverage location model of earthquake relief material storage repository considering distribution time sequence characteristics

The scientific location of earthquake emergency supply warehouses is conducive to the effective distribution of emergency relief resources and improved rescue efficiency in earthquake hazard. Comprehensively considering the regional population as well as coverage quality at the demand points, this paper aims to divide the coverage thresholds of earthquake emergency rescue and logistic supplies according to their time-series features, and to build a location model for supply warehouses according to the variety and amount of stored supplies considering their time-series features, in hope of optimizing the set covering issue of earthquake relief supply warehouses. The solution is approached with two methods: the target deviation rate minimization model and NSGA-II algorithm. The results obtained by solving the target deviation rate minimization model can balance every target. The branch and bound algorithm can find the global optimal solution at a certain calculation scale with high calculation efficiency, but its efficiency decreases significantly when the operation scale increases. The NSGA-II algorithm is more suitable for large-scale solution calculations with high calculation efficiency, and it can output a set of non-inferior solutions for decision makers to select from according to different preference. Taking Aba Prefecture in Sichuan Province as illustration, the feasibility of the model is validated; meanwhile, the effectiveness and benefits of the two approaches in solving the problem of multi-objective set covering of the warehouses are compared and analyzed. • Developing a multi-objective set covering model based on the weights and time-series features. • Computing the model by two methods: (1) the target deviation rate minimization model, (2) NSGA-II algorithm. • Studying a case of distribution of disaster materials in Aba Prefecture.

Comprehensive Evaluation Model for Resettlement Site Selection in Karst Areas Using Multiobjective Particle Swarm Optimization

Resettlement site selection is a systematic project constrained by multiple geographic, social, and economic factors, and site selection in karst mountainous areas is even more complicated. The key step in resettlement site selection is to reduce the uncertainty and inaccuracy of evaluation models. This study constructed an evaluation index system of a model for resettlement site selection in karst mountainous areas from multiple aspects, including karst rocky desertification environment, degree of geological hazard development, resource and environmental carrying capacity, transportation and economic development potential, as well as relocation cost. A hierarchical model was applied to classify the indices, determine the weighted optimal solution of the eigenvectors in matrices, and evaluate the suitability. A new algorithm for model parameter inference was developed based on four objectives, including achieving suitable ecoenvironment capacity, ensuring production and development, controlling relocation cost, and meeting the comprehensive requirements. The objective functions and constraints of the model with four objectives were also established. To address this problem, the multiobjective particle swarm algorithm was applied, and a novel concept of “noninferior solution set” was proposed. The objective functions and constraint conditions of the algorithm were optimized and improved from four aspects: parameter setting, representativeness of particles, noninferior solution set storage, and selection of global noninferior solution set. Guanling County, an area in Guizhou Province with a typical development of karst rocky desertification, was selected as the study site. The latest data for land use mapping were adopted as the smallest evaluation unit, and the improved multiobjective particle swarm algorithm was employed in evaluating the resettlement site selection within this area. The improved algorithm was applicable to a wide range of multiobjective optimization problems.