Bi-level Particle Swarm Optimization Algorithm Research Articles

Research on Large-Scale Bi-Level Particle Swarm Optimization Algorithm

Targeting at the slow convergence and the local optimum problems of particle swarm optimization (PSO), a large-scale bi-level particle swarm optimization algorithm is proposed in this paper, which enlarges the particle swarm scale and enhances the initial population diversity on the basis of multi-particle swarms. On the other hand, this algorithm also improves the running efficiency of the particle swarms by the structural advantages of bi-level particle swarms, for which, the upper-level particle swarm provides decision-making information while the lower level working particle swarms run at the same time, enhancing the operation efficiency of particle swarms. The two levels of particle swarms collaborate and work well with each other. In order to prevent population precocity and slow convergence in the later stage, an accelerated factor based on increasing exponential function is applied at the same time to control the coupling among particle swarms. And the simulation results show that the large-scale bi-level particle swarm optimization algorithm is featured in better superiority and stability.

A Bi-Level Particle Swarm Optimization Algorithm for Solving Unit Commitment Problems with Wind-EVs Coordinated Dispatch

Nowadays, the grid faces much more challenges caused by wind power and the accessing of electric vehicles (EVs). Based on the potentiality of coordinated dispatch, a model of wind-EVs coordinated dispatch was developed. Then, A bi-level particle swarm optimization algorithm for solving the model was proposed in this paper. The application of this algorithm to 10-unit test system carried out that coordinated dispatch can benefit the power system from the following aspects: (1) Reducing operating costs; (2) Improving the utilization of wind power; (3) Stabilizing the peak-valley difference.

A unit commitment-based fuzzy bilevel electricity trading model under load uncertainty

In this study, we establish a bilevel electricity trading model where fuzzy set theory is applied to address future load uncertainty, system reliability as well as human imprecise knowledge. From the literature, there have been some studies focused on this bilevel problem while few of them consider future load uncertainty and unit commitment optimization which handles the collaboration of generation units. Then, our study makes the following contributions: First, the future load uncertainty is characterized by fuzzy set theory, as the various factors that affect the load forecasting are often assessed with some non-statistical uncertainties. Second, the generation costs are obtained by solving complicated unit commitment problems, rather than approximate calculations used in existing studies. Third, this model copes with the optimizations of both the generation companies and the market operator, where the unexpected load risk is particularly analyzed by using fuzzy value-at-risk as a quantitative risk measurement. Forth, a mechanism to encourage the convergence of the bilevel model is proposed based on fuzzy maxmin approach, and a bilevel particle swarm optimization algorithm is developed to solve the problem in a proper runtime. To illustrate the effectiveness of this research, we provide a test system-based numerical example and discuss about the experimental results according to the principle of social welfare maximization. Finally, we also compare the model and algorithm with conventional methods.

PARTICLE SWARM OPTIMIZATION-BASED ALGORITHM FOR BILEVEL JOINT PRICING AND LOT-SIZING DECISIONS IN A SUPPLY CHAIN

This study considers joint pricing and lot-sizing policies in a single-manufacturer–single-retailer system. Because a supply chain is a hierarchical system, we adopt a bilevel programming technique to establish a bilevel joint pricing and lot-sizing model guided by the manufacturer. The objective of the problem here is to respectively maximize the manufacturer's and the retailer's net profits by determining the manufacturer's and retailer's lot size, the wholesale price and the retail price simultaneously. Following the properties of the bilevel programming problem (BLPP), we design a novel bilevel particle swarm optimization algorithm (BPSO), and it can solve BLPP without any assumed conditions of the problem. BPSO shows a good performance on eight benchmark bilevel problems. Then BPSO is employed to solve the proposed bilevel model, and the experimental data are used to analyze the features of the proposed bilevel model, and the results support the finding that BPSO is effective in optimizing BLPP.

Improved particle swarm optimization based approach for bilevel programming problem-an application on supply chain model

Bilevel programming problem (BLPP) is a NP-hard problem and very difficult to be resolved by using the classical method. This paper attempts to develop an efficient method based on improved bilevel particle swarm optimization algorithm (IBPSO) to deal with BLPP. The improved algorithm adopts dynamic self-adapting inertia weight and Cauchy distribution to ensure global search ability and faster convergence speed of IBPSO. IBPSO employs two improved PSO as a main program and a subprogram respectively. According to the interactive iterations of two improved PSO algorithms, IBPSO can solve BLPP without some assumptions of BLPP, such as the gradient information of the objective functions, the convexity of constraint regions, and so on. Twelve bilevel problems in the literatures are employed to illustrate the performance of IBPSO and some comparisons are also given. The results demonstrate that the proposed algorithm IBPSO exhibits higher accuracy than other algorithms. Then IBPSO is adopted to solve two supply chain models proposed in this paper, and some features of the proposed bilevel model are given based on the experimental data. The results support the finding that IBPSO is effective in optimizing BLPP.

Applying Multiobjective Bilevel Optimization under Fuzzy Random Environment to Traffic Assignment Problem: Case Study of a Large-Scale Construction Project

This article presents an optimization method for solving a material flow traffic assignment problem (TAP) in a large-scale construction project considering a hierarchical structure with fuzzy random variables. A multiobjective bilevel decision-making model is established in which the transportation time and cost in each arc are considered as fuzzy random variables. The construction contractor, the leader in the hierarchy, aims to minimize both total direct and transportation time costs. The transportation agency, next in the hierarchy, assesses the target to minimize total transportation cost. To deal with the uncertainties, the expected value operator and chance constraint method are used to transform the uncertain model into a calculable one. Furthermore, a multiobjective bilevel particle swarm optimization algorithm with a fuzzy random simulation-based constraint checking procedure is applied to solve the model. Finally, the Shuibuya Hydropower Project is used as a practical example to demonstrate the practicality and efficiency of the proposed model. Results and a sensitivity analysis are presented to highlight the performance of the optimization method.