Hybrid particle swarm optimization for multiobjective resource allocation *

An effective genetic algorithm approach to multiobjective resource allocation problems (MORAPs)

In order to solve the problem that the particle swarm algorithm does not have high search ability in the late iteration and the particles tend to fall into the local optimum when it is introduced into a nonlinear financial risk model (objective), a financial investment risk control model based on an improved particle swarm algorithm is proposed. Based on the optimization of inertia weights and the variation of individual position of each particle, an improved particle swarm algorithm is proposed. The particle swarm algorithm is used to select the optimal control parameters to minimize the total risk value of the financial system. The simulation results show that: in the traditional algorithm, the beginning stage drops faster, and the local optimal phenomenon appears soon, but the improved algorithm makes the particles jump out of the local optimal trap soon by improving the inertia weights and other values, so as to reach the optimal faster, and the convergence of the improved algorithm is advanced more than 5 generations. Conclusion: The improved particle swarm algorithm is better than the traditional particle swarm algorithm in terms of global optimization and search speed.

This paper uses the data envelopment analysis method to construct an emerging resource allocation optimization algorithm to deeply analyze and study preschool education resource allocation. This paper combines information related to distributed systems and database application practices, and HBase, a distributed data storage system, is selected as the research object. The relationship between hardware configuration parameters and the throughput and response latency of HBase is modeled using the random forest algorithm, an improved particle swarm algorithm is designed and implemented to optimize the mathematical model of the relationship between resource allocation and capital cost, and the optimization results are verified in a real environment to realize the optimization of HBase resource allocation scheme. In this paper, duplicate solution sets are removed based on the objective function in the resource allocation model. Finally, NSGA-II is compared with SPEA2, MOEA/D, and IBEA in this model for simulation experiments to verify the efficient solution of NSGA-II to the constructed model through subjectivity presentation as well as objective performance analysis and to provide a decision solution with a theoretical support for resource allocation optimization. This study provides countermeasure suggestions for each kindergarten to improve the efficiency of resource management, optimize the scale of operation, and increase technological progress, which is based on the findings of the empirical analysis and field interviews, and thus can effectively help kindergartens to improve the efficiency of resource allocation.

We consider the Stackelberg problem composed of a single leader and several followers which are interacting but cooperative mutually. Then the followers choose an unique solution from the noninferior solution set of the multi-objective optimization problem by mutual agreement. On the other hand, under uncertainties regarding the followers' agreement, the leader makes an optimal decision against the case where the worst noninferior solution for leader's objective function might be chosen from the noninferior solution set of the followers. That is to say, let the leader adopt the min-max criterion.In this paper, we call such a decision “the min-max type Stackelberg strategy for the multiple objective problem”, and the problem solving such a decision is formulated. And then, in the case where the min-max type Stackelberg strategy is adopted in the multiple objective resource allocation problem, we propose an algorithm to obtain the strategy as the optimal resource allocation.

An improved particle swarm algorithm (PSA) is proposed based on the universal framework of the intelligence optimization algorithms (IOA). Regarded as increment to the standard PSA, the improved PSA takes the essence of the standard PSA, and additionally utilizes various heuristic factors and different empirical operators introduced by multiple other IOA. The capability of global and local optimization can be strengthened by the free search, the differential evolution and the quantum rotation comprehensively. The genetic variation is adopted to avoid premature convergence, while the greed strategy is adopted to guarantee monotonous convergence. The numerical simulation indicates that the improved PSA achieves better performance than the standard PSA.

Multi-objective optimization has been applied in many fields of science, including engineering, economics and logistics where optimal decisions need to be taken in the presence of trade-offs between two or more conflicting objectives. One approach to optimize a multi-objective mathematical model is to employ utility functions for the objectives. Recent studies on utility-based multi-objective optimization concentrates on considering just one utility function for each objective. But, in reality, it is not reasonable to have a unique utility function corresponding to each objective function. Here, a constrained multi-objective mathematical model is considered in which several utility functions are associated for each objective. All of these utility functions are uncertain and in fuzzy form, so a fuzzy probabilistic approach is incorporated to investigate the uncertainty of the utility functions for each objective. Meanwhile, the total utility function of the problem will be a fuzzy nonlinear mathematical model. Since there are not any conventional approaches to solve such a model, a defuzzification method to change the total utility function to a crisp nonlinear model is employed. Also, a maximum technique is applied to defuzzify the conditional utility functions. This action results in changing the total utility function to a crisp single objective nonlinear model and will simplify the optimization process of the total utility function. The effectiveness of the proposed approach is shown by solving a test problem.

Single-pipe ring-type mixing water oil-gathering process is often used in the oil-gas gathering and transportation system of low permeability oilfield. In order to improve the economic benefits of exploitation and construction of low permeability oilfield, multi-objective parameters optimization mathematical model of single-pipe ring-type mixing water oil-gathering process is built with the targets of minimization of pipeline construction investment, thermal energy and power energy consumption. The model belongs to the mixed-discrete variable nonlinear optimization problem. According to the structure characteristics of the model, multi-objective parameters optimization mathematical model is changed into the single-objective optimization by using linear weighting method. Then the single-objective optimization is solved by hybrid genetic algorithm. Network optimized design of a block of oil-gas gathering and transportation system is conducted by the model mentioned in this paper, and the result is that pipeline construction investment reduces 12.4 percent, thermal energy consumption reduces 14.4 percent and power energy consumption reduces 12.4 percent.

As an important part of s mart grid, microgrid(M G) is a new form of s mart grid in the future. Microgrid( MG ) technology can effectively integrate the advantages of new energy and renewable energy generation and provide a novel way for large-scale applicat ions of new energy and renewable energy connecting to grid. This paper deals with the problem o f economic operation of microsources in the microgrid, such as micro -turbine(MT), fuel cell(FC), diesel generator(DG), photovoltaic cell(PV), wind turbine(WT), and battery storage. The proposed problem is formulated as a nonlinear constrained optimization problem. The paper takes into consideration the operation cost as well as the emission reduction of NO x , SO 2 , and CO2. So a mathematical optimal model is built to optimize operation of microgrid(M G) system, based on the characteristics of various microsources, the restraint of microgrid system and the predicting output of the next 24hours’ wind turbine and photovoltaic cell and load demand. An improved particle swarm algorith m is emp loyed to minimu m the co mprehensive benefit cost of microgrid operation including economic and environmental benefits which realizes the mu lti-objective optimization operation. Besides, this paper focuses on the effect of electricity price between microgrid and the main grid on system operation costs. The results demonstrate the efficiency of the proposed approach to satisfy the load and to reduce the operation cost and the emissions.

Bearings are among the most widely used core components in mechanical equipment. Their failure creates the potential for serious accidents and economic losses. Vibration signature analyses are the most common approach to assess the viability of bearings due to its ease of measurement and high correlation with structural dynamics. However, the collected vibration signals of rolling bearings are usually nonstationary and are inevitably accompanied by noise interference. This makes it difficult to extract the feature frequency for the failed bearing and affects the diagnosis accuracy. The majorization-minimization-based total variation (TV-MM) denoising algorithm effectively removes the noise interference from the signal and highlights the related feature information. The value of its main parameter λ determines the quality of the denoising effect. However, manually selecting parameters requires professional experience in a process that it is time-consuming and laborious, while the use of genetic algorithms is cumbersome. Therefore, an improved particle swarm algorithm (IPSO) is used to find the optimal solution of λ. The IPSO utilises the mutation concept in genetic algorithms to reinitialise the particles with a certain probability after each update. In addition, the empirical wavelet transform (EWT) is an adaptive signal processing method suitable for processing nonlinear and nonstationary signals. Therefore, this paper presents an ensemble analysis method that combines the IPSO, TV-MM, and EWT. First, IPSO is used to optimise the denoising parameter λ. The TV-MM under this parameter effectively removes the background noise interference and improves the accuracy of the subsequent modal decomposition. Then, the EWT is used for the adaptive division to produce a set of sequences. Finally, Hilbert envelope demodulation is performed on each component to realise fault diagnosis. The results from simulations and signals received from defective bearings with outer race fault, inner race fault, and rolling element fault demonstrate the effectiveness of the proposed method for fault diagnosis of rolling bearings.

A train timetable is the key to organizing railway traffic and determining the inbound and outbound times of trains. The goals for optimizing a timetable are to improve stability (the most important index) and to offer more feasibility for train rescheduling when disruptions occur, with high railway capacity utilization as a prerequisite. Train rescheduling must ensure traffic order and efficiency and adjusts train movements to be consistent with the schedule as much as possible. The two problems have the same basic solution method: to adjust the inbound and outbound times of trains at stations. Timetable stability is defined by focusing on the buffer time distribution for trains in sections and at stations. A timetable optimizing model that takes stability as the optimizing goal and a rescheduling model with minimal summary time as the destination are presented. The particle swarm algorithm is improved and is applied in problem solving as a time-adjusting tool. The algorithm is illustrated by two examples from the Guangzhou-to-Shenzhen section in China. The improved particle swarm algorithm is proved to have real-time adjusting ability, showing its high convergent speed. It is concluded that the algorithm has great global searching ability. The described new method can be embedded in the decision support tool for timetable designers and train dispatchers.

Customer loyalty means that customers identify with products and services and have the performance of repeated purchase, which is presented through behavior and emotion. To achieve higher customer loyalty, specific analysis of the factors affecting customer loyalty is needed. Currently, most scholars believe that the decisive influencing factors on customer loyalty are: customer satisfaction, customer conversion cost, trust, service quality, and corporate image. In this paper, the [Formula: see text]-nearest neighbors (KNN) clustering evaluation model optimized by improved particle swarm algorithm is used for customer loyalty evaluation. The paper particleizes the eigenvalues and thresholds of the factors influencing loyalty, realizes the dynamic change of inertia weights by introducing particle swarm evolution degree and particle swarm aggregation degree, and utilizes the global searchable feature particles and threshold updates of particle swarm algorithm to enhance the algorithm’s ability to deal with nonlinear problems and accelerate the model. The results show that our model can evaluate customer loyalty using multiple parameters, which is more comprehensive, reasonable, and accurate than other evaluation methods, and can consider all parameters and their interrelationships, so as to obtain more reasonable evaluation conclusions. Experimental results demonstrate a 3.35% average error, surpassing KNN (5.28%) in loyalty prediction accuracy.

Image contrast enhancement uses the object intensity transformation function to maximize the amount of information to enhance an image. In this paper, the image enhancement problem is regarded as an optimization problem, and the particle swarm algorithm is used to obtain the optimal solution. First, an improved particle swarm optimization algorithm is proposed. In this algorithm, individual optimization, local optimization, and global optimization are used to adjust the particle's flight direction. In local optimization, the topology is used to induce comparison and communication between particles. The sparse penalty term in speed update formula is added to adjust the sparsity of the algorithm and the size of the solution space. Second, the three channels of the color images R, G, and B are represented by a quaternion matrix, and an improved particle swarm algorithm is used to optimize the transformation parameters. Finally, contrast and brightness elements are added to the fitness function. The fitness function is used to guide the particle swarm optimization algorithm to optimize the parameters in the transformation function. This paper verifies via two experiments. First, improved particle swarm algorithm is simulated and tested. By comparing the average values of the four algorithms under the three types of 6 test functions, the average value is increased by at least 15 times in the single-peak 2 test functions: in the multi-peak and multi-peak fixed-dimension 4 test functions, this paper can always search for the global optimal solution, and the average value is either the same or at least 1.3 times higher. Second, the proposed algorithm is compared with other evolutionary algorithms to optimize contrast enhancement, select images in two different data sets, and calculate various evaluation indicators of different algorithms under different images. The optimal value is the algorithm in this paper, and the performance indicators are at least a 5% increase and a minimum 15% increase in algorithm running time. Final results show that the effects the proposed algorithm have obvious advantages in both subjective and qualitative aspects.

A novel multi-objective meta-heuristic model for solving cross-docking scheduling problems

In order to improve the machining precision, the carriage of the gantry machine tool must have high anti-vibration performance, and its mass should be as light as possible. Based on the finite element modal analysis, a precision gantry machine tool's carriage was taken as the study object, and a multi-objective optimization mathematical model of the carriage was built by taking the mass and the first four order natural frequencies of the carriage as the objective functions, and the thickness of stiffener plates as the design variables. The second-order response surface model for carriage's multi-objective optimization was established through orthogonal experimental design. The accuracy of the response surface model was verified by comparing the response surface model with finite element model. The optimal solution of carriage multi-objective optimization was obtained by solving the response surface optimization model, which made carriage's mass decreasing 9.63%, the first four natural frequencies increasing 6.48%, 7.41%, 8.36% and 10.32% respectively. Finally, the correctness of carriage's optimization design method proposed in this paper was verified by the static and dynamic experiments.

This study proposes a new multi-objective mathematical model in pharmaceutical supply chain for natural disaster response considering quality, green concepts. The proposed model includes three objective functions. The first minimizes total manufacturing costs including production costs, purchasing costs, opening manufacturing plant costs, opening distribution centers costs, transportation costs and cost of poor quality (appraisal and prevention costs). The second minimizes environmental effects of products and transportations. The third maximizes humanitarian forces. Before disaster occurrence, to efficiently predict the objective functions values, we apply the back propagation (BP)—neural network, hybrid genetic algorithm (GA)—artificial neural network and particle swarm optimization (PSO). Finally, the effectiveness of the proposed solution shows the proposed multi objective optimization technique and its feasibility to be adopted as suitable methodology. The obtained results illustrate that the BP had high performance, which its R 2 was 0.99. Managerial implications of this research focus on improving the efficiency and effectiveness of the healthcare supply chain for natural disaster response: saving time, minimizing costs, minimizing environmental impact, utilizing resources more effectively (e.g. financial, human, technical, assets, transportation), showing social responsibility for communities affected by the disaster and continuously improving healthcare supply chain management.