Application of Multiobjective Particle Swarm Optimization in Rural Credit System

In China’s rural credit system, the problem of credit constraints is prominent. Due to the imperfect credit market, a large number of rural residents have credit constraints. Rural credit constraint is a serious problem restricting China’s rural economic development. Aimed at solving the rural credit constraints, this paper makes an optimization analysis on the rural credit system and loan decision-making. To more reasonably evaluate customers’ borrowing ability, the credit risk based on farmers’ data on the big data platform is evaluated in this paper. The stacked denoising autoencoder network is improved by adopting the deep learning framework to improve the accuracy of credit evaluation. For improving the loan decision-making ability of rural credit system, a loan optimization strategy based on multiobjective particle swarm optimization algorithm is proposed. The simulation results show that the optimization ability, speed, and stability of the proposed algorithm have achieved good results in dealing with the loan portfolio decision-making problem.

A Multiobjective Particle Swarm Optimization Algorithm Based on Grid Technique and Multistrategy

When faced with complex optimization problems with multiple objectives and multiple variables, many multiobjective particle swarm algorithms are prone to premature convergence. To enhance the convergence and diversity of the multiobjective particle swarm algorithm, a multiobjective particle swarm optimization algorithm based on the grid technique and multistrategy (GTMSMOPSO) is proposed. The algorithm randomly uses one of two different evaluation index strategies (convergence evaluation index and distribution evaluation index) combined with the grid technique to enhance the diversity and convergence of the population and improve the probability of particles flying to the real Pareto front. A combination of grid technology and a mixed evaluation index strategy is used to maintain the external archive to avoid removing particles with better convergence based only on particle density, which leads to population degradation and affects the particle exploitation ability. At the same time, a variation operation is proposed to avoid rapid degradation of the population, which enhances the particle search capability. The simulation results show that the proposed algorithm has better convergence and distribution than CMOPSO, NSGAII, MOEAD, MOPSOCD, and NMPSO.

Optimal Placement and Sizing of Distributed Generators Based on Multiobjective Particle Swarm Optimization

To solve the problems of environmental pollution and energy consumption, the development of renewable energy sources becomes the top priority of current energy transformation. Therefore, distributed power generation has received extensive attention from engineers and researchers. However, the output of distributed generation (DG) is generally random and intermittent, which will cause various degrees of impact on the safe and stable operation of power system when connected to different locations, different capacities, and different types of power grids. Thus, the impact of sizing, type, and location needs to be carefully considered when choosing the optimal DG connection scheme to ensure the overall operation safety, stability, reliability, and efficiency of power grid. This work proposes a distinctive objective function that comprehensively considers power loss, voltage profile, pollution emissions, and DG costs, which is then solved by the multiobjective particle swarm optimization (MOPSO). Finally, the effectiveness and feasibility of the proposed algorithm are verified based on the IEEE 33-bus and 69-bus distribution network.

A Dynamic Model for Imputing Missing Medical Data: A Multiobjective Particle Swarm Optimization Algorithm

Missing data occurs in all research, especially in medical studies. Missing data is the situation in which a part of research data has not been reported. This will result in the incompatibility of the sample and the population and misguided conclusions. Missing data is usual in research, and the extent of it will determine how misinterpreted the conclusions will be. All methods of parameter estimation and prediction models are based on the assumption that the data are complete. Extensive missing data will result in false predictions and increased bias. In the present study, a novel method has been proposed for the imputation of medical missing data. The method determines what algorithm is suitable for the imputation of missing data. To do so, a multiobjective particle swarm optimization algorithm was used. The algorithm imputes the missing data in a way that if a prediction model is applied to the data, both specificity and sensitivity will be optimized. Our proposed model was evaluated using real data of gastric cancer and acute T-cell leukemia (ATLL). First, the model was then used to impute the missing data. Then, the missing data were imputed using deletion, average, expectation maximization, MICE, and missForest methods. Finally, the prediction model was applied for both imputed datasets. The accuracy of the prediction model for the first and the second imputation methods was 0.5 and 16.5, respectively. The novel imputation method was more accurate than similar algorithms like expectation maximization and MICE.

Solving the Dynamic Weapon Target Assignment Problem by an Improved Multiobjective Particle Swarm Optimization Algorithm

Dynamic weapon target assignment (DWTA) is an effective method to solve the multi-stage battlefield fire optimization problem, which can reflect the actual combat scenario better than static weapon target assignment (SWTA). In this paper, a meaningful and effective DWTA model is established, which contains two practical and conflicting objectives, namely, maximizing combat benefits and minimizing weapon costs. Moreover, the model contains limited resource constraints, feasibility constraints and fire transfer constraints. The existence of multi-objective and multi-constraint makes DWTA more complicated. To solve this problem, an improved multiobjective particle swarm optimization algorithm (IMOPSO) is proposed in this paper. Various learning strategies are adopted for the dominated and non-dominated solutions of the algorithm, so that the algorithm can learn and evolve in a targeted manner. In order to solve the problem that the algorithm is easy to fall into local optimum, this paper proposes a search strategy based on simulated binary crossover (SBX) and polynomial mutation (PM), which enables elitist information to be shared among external archive and enhances the exploratory capabilities of IMOPSO. In addition, a dynamic archive maintenance strategy is applied to improve the diversity of non-dominated solutions. Finally, this algorithm is compared with three state-of-the-art multiobjective optimization algorithms, including solving benchmark functions and DWTA model in this article. Experimental results show that IMOPSO has better convergence and distribution than the other three multiobjective optimization algorithms. IMOPSO has obvious advantages in solving multiobjective DWTA problems.

Optimization of a Heliostat Field by Multiobjective Particle Swarm Optimization (MOPSO) Algorithm Based on Energy, Exergy, and Economic Point of Views

This research is devoted to the energy, exergy, and economic analyses and optimization of a heliostat field. The model of the heliostat solar receiver includes detailed geometric factors related to the optical and thermal losses and efficiencies throughout the year. The main parameters of the thermal performance of this system consist of energy and exergy efficiencies, and economic parameters are investigated. By computing the energy, exergy, and economic analysis tools, they are applied for the analysis of performance, and viability of the system’s operating in Tehran City, including the detailed information of the environmental conditions of that location. For optimization purposes, 7 design variables related to geometric specification of the heliostat field are selected and the related lower and upper bonds are selected. Two target functions considered for the optimization are heliostat field exergy efficiency and payback period. The economic feasibility results of this study reveal that the net present value is 58.84 million US$, the payback period is 6.76 years, and the internal rate of return is 0.16. By considering the MOPSO algorithml, the annual mean exergy efficiency is increased from the 30.9–34.3% while the heliostat field payback period in reduced from the 6.76 to 4.3 years.

Data-Driven Real-Time Pricing Strategy and Coordinated Optimization of Economic Load Dispatch in Electricity Market

Compared to the step tariff, the real-time pricing (RTP) could be more stimulated for household consumers to change their electricity consumption behaviors. It can reduce the reserve capacity, peak load, and of course the electricity bill, which could achieve the purpose of saving energy. This paper proposes a coordinated optimization algorithm and data-driven RTP strategy in electricity market. First, the electricity price is divided into two parts, basic electricity price and fluctuating price. When the electricity consumption is equal to the average daily electricity consumption, the price is defined as the basic electricity price, which is the clearing electricity price. The consumer electricity data are analyzed. A random forest algorithm is adopted to predict the load data. Optimal adjustment parameters are obtained and the load fluctuation and the fluctuation of the electricity price are further quantified. Secondly, the appliances are modeled. The operation priority is established based on the preferences of customers and the Monte Carlo method is used to form the power load curve. Then, the smart energy planning unit is proposed to optimize the appliances on/off time and running time of residential electrical appliances. An incentive mechanism is used to further standardize the temporary electricity consumption. An improved multiobjective particle swarm optimization (IMOPSO) algorithm is adopted, which adopts the linear weighted evaluation function method to maximize the consumer’s social welfare while minimizing the electricity bill. The simulation proves that the stability of the power grid is improved while obtaining the best power strategy.