Pareto optimal allocation of fault current limiter based on immune algorithm considering cost and mitigation effect

This paper presents a multi-objective Pareto optimal method for allocation of fault current limiters based on an immune algorithm, which takes into account two objectives of the cost and fault current mitigation effect. A sensitivity factor calculation method based on the rate of fault current mitigation is proposed to reduce the search space and improve the efficiency of the algorithm.In this approach, the objective functions related to the cost and fault current mitigation effect are established. A modified inversion operator based on equal cost is proposed to converge to global optimal solutions more effectively. The proposed algorithm is tested on the IEEE39-bus system, and obtains the Pareto optimal solutions,from which the user can select the most suitable solutions according to the preferences and relative importance of the objective functions. Simulation results are used to verify the proposed method.

Multi-sensor multi-objective optimization deployment on complex terrain based on Pareto optimal theory

Multiple optimization objectives are often taken into account during the process of sensor deployment.Aiming at the problem of multi-sensor deployment in complex environment,a novel multi-sensor deployment method based on the multi-objective intelligent search algorithm is proposed.First,the complex terrain is modeled by the multi-attribute grid technology to reduce the computational complexity,and a truncation probability sensing model is presented.Two strategies,the local mutation operation and parameter adaptive operation,are introduced to improve the optimization ability of quantum particle swarm optimization(QPSO)algorithm,and then an improved multi-objective intelligent search algorithm based on QPSO is put forward to get the Pareto optimal front.Then,considering the multi-objective deployment requirements,a novel multi-sensor deployment method based on the multi-objective optimization theory is built.Simulation results show that the proposed method can effectively deal with the problem of multi-sensor deployment and provide more deployment schemes at once.Compared with the traditional algorithms,the Pareto optimal fronts achieved by the improved multi-objective search algorithm perform better on both convergence time and solution diversity aspects.

Bi-Objective Optimization: A Pareto Method with Analytical Solutions

Multiple objectives to be optimized simultaneously are prevalent in real-life problems. This paper develops a new Pareto Method for bi-objective optimization which yields analytical solutions. The Pareto optimal front is obtained in closed-form, enabling the derivation of various solutions in a convenient and efficient way. The advantage of analytical solution is the possibility of deriving accurate, exact and well-understood solutions, which is especially useful for policy analysis. An extension of the method to include multiple objectives is provided with the objectives being classified into two types. Such an extension expands the applicability of the developed techniques.

海上风电半潜式基础初步选型 Pareto⁃Optimal 评价

针对半潜漂浮式风电基础初步选型,采用Pareto⁃Optimal评价方法对不同吃水、平台立柱直径、立柱间距和垂荡板直径四个参数的不同组合进行分析比较。基于浮体动力学频域计算方法,采用我国阳江某海域极限波浪条件计算得到叶轮中心水平加速度,同时考虑完整稳性的计算结果。对比分析表明平台吃水和立柱直径宜选择适中的取值,较大的排水量和立柱总体积并不会显著减小叶轮中心水平加速度。垂荡板对于改善平台整体性能是较为敏感的,垂荡板与立柱的直径比存在一定的最佳范围。平台立柱间距是影响平台运动性能最大的因素,增大立柱间距可以有效地降低叶轮中心水平加速度,但立柱间距的增大对立柱间的撑杆结构强度以及平台整体的建造和下水提出了较大的挑战。

Tourism Route Recommendation Based on A Multi-Objective Evolutionary Algorithm Using Two-Stage Decomposition and Pareto Layering

Tourism route planning is widely applied in the smart tourism field.The Pareto-optimal front obtained by the traditional multi-objective evolutionary algorithm exhibits long tails,sharp peaks and disconnected regions problems,which leads to uneven distribution and weak diversity of optimization solutions of tourism routes.Inspired by these limitations,we propose a multi-objective evolutionary algorithm for tourism route recommendation(MOTRR)with two-stage and Pareto layering based on decomposition.The method decomposes the multiobjective problem into several subproblems,and improves the distribution of solutions through a two-stage method.The crowding degree mechanism between extreme and intermediate populations is used in the two-stage method.The neighborhood is determined according to the weight of the subproblem for crossover mutation.Finally,Pareto layering is used to improve the updating efficiency and population diversity of the solution.The two-stage method is combined with the Pareto layering structure,which not only maintains the distribution and diversity of the algorithm,but also avoids the same solutions.Compared with several classical benchmark algorithms,the experimental results demonstrate competitive advantages on five test functions,hypervolume(HV)and inverted generational distance(IGD)metrics.Using the experimental results of real scenic spot datasets from two famous tourism social networking sites with vast amounts of users and large-scale online comments in Beijing,our proposed algorithm shows better distribution.It proves that the tourism routes recommended by our proposed algorithm have better distribution and diversity,so that the recommended routes can better meet the personalized needs of tourists.

A multi-objective optimal PID control for a nonlinear system with time delay

It is generally difficult to design feedback controls of nonlinear systems with time delay to meet time domain specifications such as rise time, overshoot, and tracking error. Furthermore, these time domain specifications tend to be conflicting to each other to make the control design even more challenging. This paper presents a cell mapping method for multi-objective optimal feedback control design in time domain for a nonlinear Duffing system with time delay. We first review the multi-objective optimization problem and its formulation for control design. We then introduce the cell mapping method and a hybrid algorithm for global optimal solutions. Numerical simulations of the PID control are presented to show the features of the multi-objective optimal design. @ 2013 The Chinese Society of Theoretical and Applied Mechanics. [doi：10.1063/2.1306306]

A binary gridding path-planning method for plant-protecting UAVs on irregular fields

The use of plant-protecting unmanned aerial vehicles(UAVs)for pesticide spraying is an essential operation in modern agriculture.The balance between reducing pesticide consumption and energy consumption is a significant focus of current research in the path-planning of plant-protecting UAVs.In this study,we proposed a binarization multi-objective model for the irregular field area,specifically an improved non-dominated sorting genetic algorithm–II based on the knee point and plane measurement(KPPM-NSGA-ii).The binarization multi-objective model is applied to convex polygons,concave polygons and fields with complex terrain.The experiments demonstrated that the proposed KPPM-NSGA-ii can obtain better results than the unplanned path method whether the optimization of pesticide consumption or energy consumption is preferred.Hence,the proposed algorithm can save energy and pesticide usage and improve the efficiency in practical applications.

Optimal Risk Sharing for Maxmin Choquet Expected Utility Model

This article analyzes the Pareto optimal allocations,agreeable trades and agreeable bets under the maxmin Choquet expected utility(MCEU)model.We provide several useful characterizations for Pareto optimal allocations for risk averse agents.We derive the formulation descriptions for non-existence agreeable trades or agreeable bets for risk neutral agents.We build some relationships between ex-ante stage and interim stage on agreeable trades or bets when new information arrives.

Efficiency-based Pareto Optimization of Building Energy Consumption and Thermal Comfort:A Case Study of a Residential Building in Bushehr,Iran

In Iran,the intensity of energy consumption in the building sector is almost 3 times the world average,and due to the consumption of fossil fuels as the main source of energy in this sector,as well as the lack of optimal design of buildings,it has led to excessive release of toxic gases into the environment.This research develops an efficient approach for the simulation-oriented Pareto optimization(SOPO)of building energy efficiency to assist engineers in optimal building design in early design phases.To this end,EnergyPlus,as one of the most powerful and well-known whole-building simulation programs,is combined with the Multi-objective Ant Colony Optimization(MOACO)algorithm through the JAVA programming language.As a result,the capabilities of JAVA programming are added to EnergyPlus without the use of other plugins and third parties.To evaluate the effectiveness of the developed method,it was performed on a residential building located in the hot and semi-arid region of Iran.To obtain the optimum configuration of the building under investigation,the building rotation,window-to-wall ratio,tilt angle of shading device,depth of shading device,color of the external walls,area of solar collector,tilt angle of solar collector,rotation of solar collector,cooling and heating setpoints of heating,ventilation,and air conditioning(HVAC)system are chosen as decision variables.Further,the building energy consumption(BEC),solar collector efficiency(SCE),and predicted percentage of dissatisfied(PPD)index as a measure of the occupants'thermal comfort level are chosen as the objective functions.The single-objective optimization(SO)and Pareto optimization(PO)are performed.The obtained results are compared to the initial values of the basic model.The optimization results depict that the PO provides optimal solutions more reliable than those obtained by the SOs,owing to the lower value of the deviation index.Moreover,the optimal solutions extracted through the PO are depicted in the form of Pareto fronts.Eventually,the Linear Programming Technique for Multidimensional Analysis of Preference(LINMAP)technique as one of the well-known multi-criteria decision-making(MCDM)methods is utilized to adopt the optimum building configuration from the set of Pareto optimal solutions.Further,the results of PO show that although BEC increases from 136 GJ to 140 GJ,PPD significantly decreases from 26%to 8%and SCE significantly increases from 16%to 25%.The introduced SOPO method suggests an effective and practical approach to obtain optimal solutions during the building design phase and provides an opportunity for building engineers to have a better picture of the range of options for decision-making.In addition,the method presented in this study can be applied to different types of buildings in different climates.

Multi-Objective Task Assignment for Maximizing Social Welfare in Spatio-Temporal Crowdsourcing

With the development of the Internet of Things(IoT),spatio-temporal crowdsourcing(mobile crowdsourcing)has become an emerging paradigm for addressing location-based sensing tasks.However,the delay caused by network transmission has led to low data processing efficiency.Fortunately,edge computing can solve this problem,effectively reduce the delay of data transmission,and improve data processing capacity,so that the crowdsourcing platform can make better decisions faster.Therefore,this paper combines spatio-temporal crowdsourcing and edge computing to study the Multi-Objective Optimization Task Assignment(MOO-TA)problem in the edge computing environment.The proposed online incentive mechanism considers the task difficulty attribute to motivate crowd workers to perform sensing tasks in the unpopular area.In this paper,the Weighted and Multi-Objective Particle Swarm Combination(WAMOPSC)algorithm is proposed to maximize both platform’s and crowd workers’utility,so as to maximize social welfare.The algorithm combines the traditional Linear Weighted Summation(LWS)algorithm and Multi-Objective Particle Swarm Optimization(MOPSO)algorithm to find pareto optimal solutions of multi-objective optimization task assignment problem as much as possible for crowdsourcing platform to choose.Through comparison experiments on real data sets,the effectiveness and feasibility of the proposed method are evaluated.

A multiobjective evolutionary optimization method based critical rainfall thresholds for debris flows initiation

At present,most researches on the critical rainfall threshold of debris flow initiation use a linear model obtained through regression.With relatively weak fault tolerance,this method not only ignores nonlinear effects but also is susceptible to singular noise samples,which makes it difficult to characterize the true quantization relationship of the rainfall threshold.Besides,the early warning threshold determined by statistical parameters is susceptible to negative samples(samples where no debris flow has occurred),which leads to uncertainty in the reliability of the early warning results by the regression curve.To overcome the above limitations,this study develops a data-driven multiobjective evolutionary optimization method that combines an artificial neural network(ANN)and a multiobjective evolutionary optimization implemented by particle swarm optimization(PSO).Firstly,the Pareto optimality method is used to represent the nonlinear and conflicting critical thresholds for the rainfall intensity I and the rainfall duration D.An ANN is used to construct a dual-target(dual-task)predictive surrogate model,and then a PSO-based multiobjective evolutionary optimization algorithm is applied to train the ANN and stochastically search the trained ANN for obtaining the Pareto front of the I-D surrogate prediction model,which is intended to overcome the limitations of the existing linear regression-based threshold methods.Finally,a double early warning curve model that can effectively control the false alarm rate and negative alarm rate of hazard warnings are proposed based on the decision space and target space maps.This study provides theoretical guidance for the early warning and forecasting of debris flows and has strong applicability.

Overview of multi-objective optimization methods

To assist readers to have a comprehensive understanding, the classical and intelligent methods roundly based on precursory research achievements are summarized in this paper. First, basic conception and description about multi-objective (MO) optimization are introduced. Then some definitions and related terminologies are given. Furthermore several MO optimization methods including classical and current intelligent methods are discussed one by one succinctly. Finally evaluations on advantages and disadvantages about these methods are made at the end of the paper.

Handling Multiple Objectives with Biogeography-based Optimization

Biogeography-based optimization （BBO） is a new evolutionary optimization method inspired by biogeography. In this paper, BBO is extended to a multi-objective optimization, and a biogeography-based multi-objective optimization （BBMO） is introduced, which uses the cluster attribute of islands to naturally decompose the problem. The proposed algorithm makes use of nondominated sorting approach to improve the convergence ability efficiently. It also combines the crowding distance to guarantee the diversity of Pareto optimal solutions. We compare the BBMO with two representative state-of-the-art evolutionary multi-objective optimization methods, non-dominated sorting genetic algorithm-II （NSGA-II） and archive-based micro genetic algorithm （AMGA） in terms of three metrics. Simulation results indicate that in most cases, the proposed BBMO is able to find much better spread of solutions and converge faster to true Pareto optimal fronts than NSGA-II and AMGA do.

Time variant multi-objective linear fractional interval-valued transportation problem

This paper studies a time-variant multi-objective linear fractional transportation problem. In reality, transported goods should reach in destinations within a specific time. Considering the importance of time, a time-variant multi-objective linear fractional transportation problem is formulated here. We take into account the parameters as cost, supply and demand are interval valued that involved in the proposed model, so we treat the model as a multi-objective linear fractional interval transportation problem. To solve the formulated model, we first convert it into a deterministic form using a new transformation technique and then apply fuzzy programming to solve it. The applicability of our proposed method is shown by considering two numerical examples. At last, conclusions and future research directions regarding our study is included.

Novel electromagnetism-like mechanism method for multiobjective optimization problems

As a new-style stochastic algorithm, the electromagnetism-like mechanism（EM） method gains more and more attention from many researchers in recent years. A novel model based on EM（NMEM） for multiobjective optimization problems is proposed, which regards the charge of all particles as the constraints in the current population and the measure of the uniformity of non-dominated solutions as the objective function. The charge of the particle is evaluated based on the dominated concept, and its magnitude determines the direction of a force between two particles. Numerical studies are carried out on six complex test functions and the experimental results demonstrate that the proposed NMEM algorithm is a very robust method for solving the multiobjective optimization problems.

A self-adaptive linear evolutionary algorithm for solving constrained optimization problems

In many real-world applications of evolutionary algorithms,the fitness of an individual requires a quantitative measure.This paper proposes a self-adaptive linear evolutionary algorithm （ALEA） in which we introduce a novel strategy for evaluating individual＇s relative strengths and weaknesses.Based on this strategy,searching space of constrained optimization problems with high dimensions for design variables is compressed into two-dimensional performance space in which it is possible to quickly identify ＇good＇ individuals of the performance for a multiobjective optimization application,regardless of original space complexity.This is considered as our main contribution.In addition,the proposed new evolutionary algorithm combines two basic operators with modification in reproduction phase,namely,crossover and mutation.Simulation results over a comprehensive set of benchmark functions show that the proposed strategy is feasible and effective,and provides good performance in terms of uniformity and diversity of solutions.

Interactive Fuzzy Approaches for Solving Multiobjective Two-Person Zero-Sum Games

In this paper, we consider multiobjective two-person zero-sum games with vector payoffs and vector fuzzy payoffs. We translate such games into the corresponding multiobjective programming problems and introduce the pessimistic Pareto optimal solution concept by assuming that a player supposes the opponent adopts the most disadvantage strategy for the self. It is shown that any pessimistic Pareto optimal solution can be obtained on the basis of linear programming techniques even if the membership functions for the objective functions are nonlinear. Moreover, we propose interactive algorithms based on the bisection method to obtain a pessimistic compromise solution from among the set of all pessimistic Pareto optimal solutions. In order to show the efficiency of the proposed method, we illustrate interactive processes of an application to a vegetable shipment problem.

Satellite constellation design with genetic algorithms based on system performance

Satellite constellation design for space optical systems is essentially a multiple-objective optimization problem. In this work, to tackle this challenge, we first categorize the performance metrics of the space optical system by taking into account the system tasks（i.e., target detection and tracking）. We then propose a new non-dominated sorting genetic algorithm（NSGA） to maximize the system surveillance performance. Pareto optimal sets are employed to deal with the conflicts due to the presence of multiple cost functions. Simulation results verify the validity and the improved performance of the proposed technique over benchmark methods.

A Dynamic Programming Approach to the Design of Composite Aircraft Wings

A light and reliable aircraft has been the major goal of aircraft designers. It is imperative to design the aircraft wing skins as efficiently as possible since the wing skins comprise more than fifty percent of the structural weight of the aircraft wing. The aircraft wing skin consists of many different types of material and thickness configurations at various locations. Selecting a thickness for each location is perhaps the most significant design task. In this paper, we formulate discrete mathematical programming models to determine the optimal thicknesses for three different criteria: maximize reliability, minimize weight, and achieve a trade-off between maximizing reliability and minimizing weight. These three model formulations are generalized discrete resource-allocation problems, which lend themselves well to the dynamic programming approach. Consequently, we use the dynamic programming method to solve these model formulations. To illustrate our approach, an example is solved in which dynamic programming yields a minimum weight design as well as a trade-off curve for weight versus reliability for an aircraft wing with thirty locations (or panels) and fourteen thickness choices for each location.

Optimization Method for Departure Flight Scheduling Problem Based on Genetic Algorithm

Except for the bad weather or other uncontrollable reasons,a reasonable queue of departure and arrival flights is one of the important methods to reduce the delay on busy airports.Here focusing on the Pareto optimization of departure flights,the take-off sequencing is taken as a single machine scheduling problem with two objective functions,i.e.,the minimum of total weighted delayed number of departure flights and the latest delay time of delayed flight.And the integer programming model is established and solved by multi-objective genetic algorithm.The simulation results show that the method can obtain the better goal,and provide a variety of options for controllers considering the scene situation,thus improving the flexibility and effectivity of flight plan.