Multi-objective Optimal Allocation Research Articles

Dynamic multi-objective optimization of battlefield support resources based on Interactive Genetic Algorithm

With the deterioration of the international environment and the intensification of military conflicts, the scale, type, direction and emphasis of military activities are constantly changing. which requires the corresponding adjustment of maintenance and support resources to ensure that the wartime support tasks are met in various battlefield environments. Based on the background that the support demand and battlefield situation have changed and the original resource support scheme can not meet the needs of wartime support tasks, this paper readjusts the support resources dynamically to ensure the strong support of combat forces, constructs a dynamic multi-objective optimal allocation model of battlefield support resources, formulates corresponding strategies, and presents a heuristic algorithm for solving the model.

Multi-objective optimal allocation of sediment resources based on the subjective trade-off rate method

The rational utilization of the characteristics of sediment resources to achieve the optimal allocation of sediment resources is a key problem that must be solved urgently for ecological governance. On the basis of determining the allocation mode of sediment resources, this study established the multi-objective optimal allocation model of sediment resources, which aimed at the maximum ecological, economic, and social benefits and used the ε-constraint method and Kuhn–Tucker condition to obtain the non-inferior solution. Moreover, the optimal equilibrium solution of the model was obtained by using the subjective trade-off method from the perspective of preference. The Weishan and Bojili irrigation areas in China were then chosen as a case study to verify the feasibility and validity of the model. Results corroborate that, compared with the present situation, the proportions of sediment transport into the field in Weishan and Bojili irrigation areas are significantly increased and that the proportions of the main and branch canal sediment detention in the two irrigation areas are reduced. Compared with the results of the non-inferior solution, ecological and social benefits have been improved, and economic benefit has been decreased. The coordinated optimization of ecological, economic, and social benefits has been realized, instead of blindly pursuing economic benefit.

Multi-objective optimal allocation of distributed generations under uncertainty based on D-S evidence theory and affine arithmetic

Many efforts on distributed generation (DG) allocation are based on deterministic methods in previous studies. However, the influence of stochastic fluctuation characteristics of DGs and loads is enormous. Therefore, affine arithmetic (AA) is used to represent the uncertainty, and a multi-objective uncertainty optimization model for DG allocation with minimum investment cost, highest income, lowest environmental cost, and minimal network loss is built in this paper. Then multi-objective interval decision-making and bi-layer optimization method based on D-S evidence theory (ET) and genetic algorithm (GA) is proposed to achieve the optimal allocation of DGs. In the proposed method, GA is used as an outer layer optimization for multi-point searches to generate different allocation schemes. Moreover, the ET method is employed as the inner layer to evaluate the candidate allocation schemes which from the outer layer optimization. Also, the ET method guides the evolution direction of GA in the outer layer. For validating the effectiveness and performance, the proposed method is applied to a typical 33-bus distribution system. The comparison with the existing TOPSIS method demonstrates the advantages when handling the uncertainty of the proposed method and achieves a more robust optimal solution.

Optimal Water Allocation Based on Water Rights Transaction Models with Administered and Market-Based Systems: A Case Study of Shiyang River Basin, China

Water-rights trade has proved to be an effective method for coping with water shortages through the transfer of water resources between users. The water allocation system is classified into two categories based on information transparency and water rights transaction goals: administered system (AS) and market-based system (MS). A multi-agent and multi-objective optimal allocation model, built on a complex adaptive system, was introduced to direct the distribution of water resources under an AS in the Shiyang River Basin; it was compared with a market-based water rights transaction model using the bulletin-board approach. Ideal economic agent equations played a dominant role in both models. The government and different water users were conceptualized as agents with different behaviors and goals in water allocation. The impact of water-saving cost on optimal water allocation was also considered. The results showed that an agent’s water-saving behavior was incentivized by high transaction prices in the water market. Under the MS, the highest bid in the quotation set had a dominant influence on how trade was conducted. A higher transaction price will, thus, result in a better benefit ratio, and a lower one will result in inactivity in terms of water rights trade. This will significantly impact the economic benefit to the basin.

Risk-Based Multi-Objective Optimization of Distributed Generation Based on GPSO-BFA Algorithm

With the rapid evolution of technology, growing accessibility, and environmental appeal of wind and solar electric systems, distributed generation (DG) has been pushed from the fringe to a mainstream factor in the grid. However, due to the randomness and uncertainty of environmental and operational conditions, DG also brings many risks and may adversely affect the reliability and safety of the power grid when connected to the distribution network. Therefore, it is necessary to introduce the risk theory into the allocation and placement of DGs. This paper establishes a comprehensive set of risk and economic indexes by modeling the randomness and uncertainty of DG outputs. In addition, islanded operation, which is a promising development direction of microgrids, is explicitly studied and the related indexes are modeled. Putting them together, we propose a risk-based multi-objective optimal allocation model to optimize the placement and configuration of DGs and provide a reliable and cost-effective system. We solve the formulated multi-objective optimization problem by combining the gradient particle swarm optimization algorithm and the bacterial foraging algorithm. We demonstrate the validity and rationality of the proposed method through the analyses of the American PG&E 69-node system.

Ant-lion optimizer-based multi-objective optimal simultaneous allocation of distributed generations and synchronous condensers in distribution networks

Ant-lion optimizer-based multi-objective optimal simultaneous allocation of distributed generations and synchronous condensers in distribution networks

Multi-objective Optimal Allocation of Regional Integrated Energy System

The integrated energy system (IES) can promote complementarity and efficient use of different energy by coupling with multiple energy resources, such as electricity, heat and cold. An optimal capacity configuration method of regional integrated energy system considering electricity and heat transaction is proposed in this paper, including the cost of energy interaction between IES and power/heating networks. The optimization objective is a multi-objective function considering the economy, environmental protection, reliability of the system and interacting power fluctuation factors. Based on the energy hub, this paper optimizes the capacity of every energy supply equipment within the region and the genetic algorithm is adopted combining with system economic operation on typical days. Finally, the proposed method is verified by an example of a certain park. The results show that the method can effectively reduce the cost of system and reasonably avoid the limitations caused by the single decision factor in the system planning stage, which provides a reference for the planning and design of actual IESs.

Bilayer Multi-Objective Optimal Allocation and Sizing of Electric Vehicle Parking Garage

The anticipated increase in electric vehicles (EVs) adoption necessitates the need for electrified transportation infrastructure to charge these vehicles. Although the EV parking garage can represent a good investment opportunity, it brings more challenges to the distribution system operator. Therefore, the allocation and sizing of the parking garage should be carefully planned. The planning process should take into consideration the economic aspects of the investor, as well as the technical aspects of the distribution system. In this paper, a bilayer Pareto multiobjective optimization problem is formulated to optimally allocate and size an EV parking garage. The optimization formulation tries to maximize the profits of the EV parking garage investor, as well as minimize the losses and voltage deviations for the distribution system operator. Dealing with these contradicting objectives simultaneously will results in a set of Pareto solutions. The results showed the different tradeoffs that might be induced while dealing with these contradictory objectives. A decision-making criterion based on statistics was used to decide on the optimal location and size of the parking garage. Sensitivity analysis to show the effect of the different objectives on the selection of the optimal size and location was also performed.

The Multi-Objective Space Optimal Allocation of Urban Land Based on Spatial Genetic Algorithm

With the constant increasing scale of urban buildings, the contradiction between supply and demand of land use problems is more prominent. Therefore, the multi-objective space optimal allocation of urban land use based on spatial genetic algorithm was proposed in this paper. Firstly, the present situation of the urban land use resources was expounded; in view of the urban land use planning, a spatial genetic algorithm was proposed; then, the urban land was divided into different functional areas, and the land planning and design method was put forward; finally, taking a city's land space planning as an example, the optimal planning and design were carried out to the geological disasters, low hilly land and land overall utilization; by comparing the land use before and after the planning optimization, the advantages of land optimization design were confirmed.

An Ameliorative Whale Optimization Algorithm for Multi-Objective Optimal Allocation of Water Resources in Handan, China

With the deepening discrepancy between water supply and demand caused by water shortages, alleviating water shortages by optimizing water resource allocation has received extensive attention. How to allocate water resources optimally, rapidly, and effectively has become a challenging problem. Thus, this study employs a meta-heuristic swarm-based algorithm, the whale optimization algorithm (WOA). To overcome drawbacks like relatively low convergence precision and convergence rates, when applying the WOA algorithm to complex optimization problems, logistic mapping is used to initialize swarm location, and inertia weighting is employed to improve the algorithm. The resulting ameliorative whale optimization algorithm (AWOA) shows substantially enhanced convergence rates and precision than the WOA and particle swarm optimization algorithms, demonstrating relatively high reliability and applicability. A water resource allocation optimization model with optimal economic efficiency and least total water shortage volume is established for Handan, China, and solved by the AWOA. The allocation results better reflect actual water usage in Handan. In 2030, the p = 50% total water shortage is forecast as 404.34 × 106 m3 or 14.8%. The shortage is mainly in the primary agricultural sector. The allocation results provide a reference for regional water resources management.

Multi-Objective Optimal Control Allocation for an Over-Actuated Electric Vehicle

The in-wheel-motor-driven electric vehicle is a typical over-actuated system. The actuation flexibility can be utilized to improve operational efficiency and enhance vehicle motion control performance by allocating different torques to four wheels. Various control objectives are emphasized for different working conditions. To trade off energy optimization and driving stability in actual complicated conditions, a unified control allocation law composed of two-step optimization is developed. A pre-allocation law is carried out for energy efficiency optimization with the assumption that no wheel is skidding or slipping, and a control reallocation law is performed using model predictive control to avoid the vehicle from unstability and to enhance the driving performance. Simulation tests are carried out via a professional vehicle dynamics simulating software veDYNA. The controller is verified to improve energy recovery in routine stable driving conditions and also to dynamically modify torques to ensure the vehicle stability on mu-split and low-adhesion road in extreme conditions.

Analysis and Research on the Optimal Allocation of Regional Water Resources

Starting from the basic concept of optimal allocation of water resources, taking the allocation of water resources in Tianjin as an example, the present situation of water resources in Tianjin is analyzed, and the multi-objective optimal allocation model of water resources is used to optimize the allocation of water resources. We use LINGO to solve the model, get the optimal allocation plan that meets the economic and social benefits, and put forward relevant policies and regulations, so as to provide theoretical which is basis for alleviating and solving the problem of water shortage.

汉江中下游地区水资源多目标优化配置

汉江中下游地区水资源多目标优化配置

Neural Network-WPCA Based Method for Multi-Objective Optimal Redundancy Allocation

For systems with multiple redundancies, reliability evaluation in the redundancy allocation problem (RAP) constitutes a computational complexity. It has been demonstrated that neural network training provides an efficient approach to estimate the complex system reliability function. When executing the neural network algorithm, there are many parameters that need to be determined for improving the training performance. Therefore, robust experimental design method can be used to determine the neural network parameters. The traditional robust design methods are intended for a single response variable. However, the application of neural network method includes more than one measurement, such as estimation accuracy and time efficiency. In this paper, utility function is first estimated by neural network training, in which the algorithm parameters are determined by weighted principal component (WPCA)-based multi-response optimization which simultaneously optimizes more than one training performance measurements. Moreover, it is always desirable to simultaneously optimize several objectives in designing a system, such as reliability, cost, etc. Therefore, continuous WPCA-based multi-response design is then applied to obtain the best design of redundancies in RAP, which simultaneously optimize multiple objectives by taking into account the correlations between them.

Capacity planning for mega container terminals with multi-objective and multi-fidelity simulation optimization

ABSTRACTContainer terminals play a significant role as representative logistics facilities for contemporary trades by handling outbound, inbound, and transshipment containers to and from the sea (shipping liners) and the hinterland (consignees). Capacity planning is a fundamental decision process when constructing, expanding, or renovating a container terminal to meet demand, and the outcome of this planning is typically represented in terms of configurations of resources (e.g., the numbers of quay cranes, yard cranes, and vehicles), which enables the container flows to satisfy a high service level for vessels (e.g., berth-on-arrivals). This study presents a decision-making process that optimizes the capacity planning of large-scale container terminals. Advanced simulation-based optimization algorithms, such as Multi-Objective Multi-Fidelity Optimization with Ordinal Transformation and Optimal Sampling (MO-MO2TOS), Multi-Objective Optimal Computing Budget Allocation (MOCBA), and Multi-Objective Convergent Optimization via Most-Promising-Area Stochastic Search (MO-COMPASS), were employed to formulate and optimally solve the large-scale multi-objective problem with multi-fidelity simulation models. Various simulation results are compared with one another in terms of the capacities over different resource configurations to understand the effect of various parameter settings on optimal capacity across the algorithms.

Firefly algorithm for multi-objective optimal allocation of water resource

Firefly algorithm (FA) is a new optimisation technique based on swarm intelligence, which has been successfully applied to various optimisation areas. In this paper, a new FA variant is proposed to solve multi-objective optimal allocation of water resource, in which three objectives, including economic, social, and environmental benefits, are maximised. To obtain the Pareto fronts, the original FA is combined with the fast non-dominated sorting method used in NSGA-II. Simulation results show that our approach can achieve a good spacing of solution points along the Pareto front. According to the preferences, the decision makers can choose different allocation methods from the Pareto front.

Firefly algorithm for multi-objective optimal allocation of water resource

Firefly algorithm for multi-objective optimal allocation of water resource

Multi-Objective Optimal Sizing for Battery Storage of PV-Based Microgrid with Demand Response

In order to solve the influence of uncertain photovoltaic power (PV) on the stable operation of microgrid (MG), demand response (DR) and battery energy storage system (BESS) need to be introduced simultaneously into the operation optimal scheduling of PV-based microgrid (PV-MG). Therefore, it is of great significance for commercial investment decisions of PV-MG to consider the influence of DR on BESS optimal sizing. Under the peak-valley time-of-use (TOU) price, this paper builds cross-time DR models based on price elasticity matrix. Furthermore, through the introduction of DR and BESS into PV-MG scheduling optimization, the MG investment and benefit model is proposed. Considering the constraint condition such as co-ordination of supply and demand, electricity price elasticity and energy loss of storage system, the improved non-dominated sorting genetic algorithm II (NSGA-II) is utilized to solve the multi-objective optimal allocation model of the BESS with the target of maximum PV consumptive rate and annual net profits. The optimization method was applied to a PV-MG in Guangdong. Through the regulation and control effect of demand response and BESS on load distribution, the uncertainties PV power can be suppressed so as to improve the PV system consumptive level, which is of great guiding significance for BESS optimal sizing under this situation.

Multi objective optimal allocation of fault current limiters in power system

Transmission systems, connection of distributed generation to the grid are increased due to increase in power demands. This fact causes the increase in short circuit level of power networks. The occurrence of fault in such networks leads to large short circuit currents throughout the system, which may exceed the rating of existing circuit breakers and can damage system equipment. There are some approaches to reduce this fault current such as power network reinforcement and utilization of fault current limiter (FCLs) in power systems. Power system reinforcement is too difficult if not impractical. Therefore, the utilization of FCLs can provide an effective way to suppress fault currents. The effectiveness of FCL depends on the number of FCLs and their installation location. In this paper, a novel approach is presented to determine the optimal number and location of FCLs to improve the power network reliability and fault current reduction based on different conflicting objective functions. IEEE 39 BUS system and IEEE 57 BUS system are considered to evaluate the effectiveness and feasibility of the proposed method. The objective functions considered for the optimal allocation are reliability of power system, economic impact and short circuit current reduction. Unlike what has been previously done in literature, in this paper Pareto based optimization algorithms, namely non-dominated sorting algorithm, multiobjective particle swarm optimization and multiobjective evolutionary algorithm based on decomposition, are utilized to deal with this problem. The use of these methods made it possible to obtain the Pareto optimal front in which these objective functions are optimized simultaneously.

Dynamic performance-based automatic generation control unit allocation with frequency sensitivity identification

With the alteration of energy structures in power system, the allocation of automatic generation control (AGC) is facing new challenges. The emergence of high penetration of manufacturing sectors and renewable energy sources has increased the demand for faster-ramping resources to participate in the frequency regulation service. Additionally, the current regulation service does not properly arrange the output of the resources considering the actual performance while they follow the AGC allocation signals, which affects the accuracy of frequency regulation. The fast-ramping capacity and response accuracy of AGC units are supposed to be considered in the dispatch. Meanwhile, the power outputs of different governors have different impacts on the system frequency, which has important guiding significance for the AGC dispatch. With the purpose of improving frequency regulation service, this paper proposes a dynamic performance-based dispatch model considering the above issues. We first prove that there is a linear relation between the output of the generators and system frequency, defined as frequency sensitivity. Then, the frequency sensitivity of each generator can be identified using the least square (LS) method. Furthermore, a dynamic multi-objective optimization allocation model is established, which considers the units’ economy, ramping capacity and accuracy. Finally, the proposed identification method and allocation model are simulated in the IEEE-9 bus system, and the simulation results verify their validity and feasibility