scholarly journals An Improved Particle Swarm Optimization for Solving Bilevel Multiobjective Programming Problem

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Tao Zhang ◽  
Tiesong Hu ◽  
Yue Zheng ◽  
Xuning Guo
Keyword(s):  
Particle Swarm Optimization ◽  
Programming Problem ◽  
Multiobjective Programming ◽  
Particle Swarm ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Multiobjective Programming Problem

An improved particle swarm optimization (PSO) algorithm is proposed for solving bilevel multiobjective programming problem (BLMPP). For such problems, the proposed algorithm directly simulates the decision process of bilevel programming, which is different from most traditional algorithms designed for specific versions or based on specific assumptions. The BLMPP is transformed to solve multiobjective optimization problems in the upper level and the lower level interactively by an improved PSO. And a set of approximate Pareto optimal solutions for BLMPP is obtained using the elite strategy. This interactive procedure is repeated until the accurate Pareto optimal solutions of the original problem are found. Finally, some numerical examples are given to illustrate the feasibility of the proposed algorithm.

scholarly journals The Biobjective Bike-Sharing Rebalancing Problem with Balance Intervals: A Multistart Multiobjective Particle Swarm Optimization Algorithm

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Yongji Jia ◽  
Yuanyuan Xu ◽  
Dong Yang ◽  
Jia Li
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Mixed Integer ◽  
Variable Neighbourhood Search ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Swarm Optimization ◽  
Multiobjective Particle Swarm Optimization ◽  
Bike Sharing

The bike-sharing system (BSS), as a sustainable way to deal with the “last mile” problem of mass transit systems, is increasingly popular in recent years. Despite its success, the BSS tends to suffer from the mismatch of bike supply and user demand. BSS operators have to transfer bikes from surplus stations to deficit stations to redistribute them among stations by means of trucks. In this paper, we deal with the bike-sharing rebalancing problem with balance intervals (BRP-BIs), which is a variant of the static bike-sharing rebalancing problem. In this problem, the equilibrium of station is characterized by a balance interval instead of a balance point in the literature. We formulate the BRP-BI as a biobjective mixed-integer programming model with the aim of determining both the minimum cost route for a single capacitated vehicle and the maximum average rebalance utility, an index for the balanced degree of station. Then, a multistart multiobjective particle swarm optimization (MS-MOPSO) algorithm is proposed to solve the model such that the Pareto optimal solutions can be derived. The proposed algorithm is extended with crossover operator and variable neighbourhood search to enhance its exploratory capability. Compared with Hybrid NSGA-II and MOPSO, the computational experimental results demonstrate that our MS-MOPSO can obtain Pareto optimal solutions with higher quality.

scholarly journals Multiobjective Particle Swarm Optimization Based on PAM and Uniform Design

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Xiaoshu Zhu ◽  
Jie Zhang ◽  
Junhong Feng
Keyword(s):  
Particle Swarm Optimization ◽  
Clustering Algorithm ◽  
Uniform Design ◽  
Particle Swarm ◽  
Test Problems ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Swarm Optimization ◽  
Multiobjective Particle Swarm Optimization

In MOPSO (multiobjective particle swarm optimization), to maintain or increase the diversity of the swarm and help an algorithm to jump out of the local optimal solution, PAM (Partitioning Around Medoid) clustering algorithm and uniform design are respectively introduced to maintain the diversity of Pareto optimal solutions and the uniformity of the selected Pareto optimal solutions. In this paper, a novel algorithm, the multiobjective particle swarm optimization based on PAM and uniform design, is proposed. The differences between the proposed algorithm and the others lie in that PAM and uniform design are firstly introduced to MOPSO. The experimental results performing on several test problems illustrate that the proposed algorithm is efficient.

scholarly journals Particle Swarm Optimization Based on Smoothing Approach for Solving a Class of Bi-Level Multiobjective Programming Problem

2017 ◽  
Vol 17 (3) ◽  
pp. 59-74
Author(s):  
Qingping He ◽  
Yibing Lv
Keyword(s):  
Particle Swarm Optimization ◽  
Programming Problem ◽  
Multiobjective Programming ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Pareto Optimal Front ◽  
Lower Level Problem ◽  
Level Problem ◽  
Multiobjective Programming Problem ◽  
Upper Level

Abstract As a metaheuristic, Particle Swarm Optimization (PSO) has been used to solve the Bi-level Multiobjective Programming Problem (BMPP). However, in the existing solving approach based on PSO for the BMPP, the upper level and the lower level problem are solved interactively by PSO. In this paper, we present a different solving approach based on PSO for the BMPP. Firstly, we replace the lower level problem of the BMPP with Kuhn-Tucker optimality conditions and adopt the perturbed Fischer-Burmeister function to smooth the complementary conditions. After that, we adopt PSO approach to solve the smoothed multiobjective programming problem. Numerical results show that our solving approach can obtain the Pareto optimal front of the BMPP efficiently.

scholarly journals Solving Bilevel Multiobjective Programming Problem by Elite Quantum Behaved Particle Swarm Optimization

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Tao Zhang ◽  
Tiesong Hu ◽  
Jia-wei Chen ◽  
Zhongping Wan ◽  
Xuning Guo
Keyword(s):  
Particle Swarm Optimization ◽  
Programming Problem ◽  
Multiobjective Programming ◽  
Particle Swarm ◽  
Test Problems ◽  
Swarm Optimization ◽  
Pareto Optimal Front ◽  
Elite Strategy ◽  
Low Dimension ◽  
Multiobjective Programming Problem

An elite quantum behaved particle swarm optimization (EQPSO) algorithm is proposed, in which an elite strategy is exerted for the global best particle to prevent premature convergence of the swarm. The EQPSO algorithm is employed for solving bilevel multiobjective programming problem (BLMPP) in this study, which has never been reported in other literatures. Finally, we use eight different test problems to measure and evaluate the proposed algorithm, including low dimension and high dimension BLMPPs, as well as attempt to solve the BLMPPs whose theoretical Pareto optimal front is not known. The experimental results show that the proposed algorithm is a feasible and efficient method for solving BLMPPs.

Comprehensive Optimization Management of Engineering Project Based on Multi-Objective Particle Swarm Optimization

2020 ◽  
Vol 13 ◽  
Author(s):  
Yong Xiang ◽  
Huidan Zheng ◽  
Wuwen Cao ◽  
Dong Gong ◽  
Jiazhen Huang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Swarm Optimization ◽  
Traditional Management ◽  
Multi Objective ◽  
Management Objectives ◽  
Optimization Management

: As the construction industry becomes more sustainable in the future, such as green, ecology, and safety, the higher the requirements for the ultimate objectives of the project.The traditional management objectives of investment, duration, and quality can no longer meet the requirements of comprehensive optimization management. Therefore, from the perspective of the project owners, the work introduced the safety and environmental objectives based on traditional management objectives. The thesis analyzes the relationship between the objectives, and builds the equilibrium optimization model. Moreover, this thesis uses multi-objective particle swarm optimization (MOPSO) to solve the problem, and obtains a series of Pareto optimal solutions. Then, according to the specific requirements of project management and the use of the efficacy coefficient method, the best solution is selected from the Pareto optimal solutions. Finally, a Sichuan wind power project is taken as an example. The work used the MOPSO to run 1,000 trails, and calculate the mean and standard deviation. It verified the rationality of model and the practicability of MOPSO.

Multi-Reservoir Ecological Operation Using Multi-Objective Particle Swarm Optimization

2014 ◽  
Vol 641-642 ◽  
pp. 65-69 ◽  
Author(s):  
Wei Lin Liu ◽  
Li Na Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Water Resources ◽  
Particle Swarm ◽  
Main Stream ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
River Ecosystem ◽  
Swarm Optimization ◽  
Multi Objective

Traditional reservoir operation ignores ecological demands of rivers. This would probably lead to degradation of river ecosystem. In order to alleviate the influence of reservoirs on river ecosystem, multi-objective reservoir ecological operation was proposed from perspective of maintaining the river ecosystem health. Multi-objective mathematical model of multi-reservoir ecological operation was established. A multi-objective particle swarm optimization (MOPSO) algorithm was introduced to generate a set of Pareto-optimal solutions. In addition, to facilitate easy implementation for the reservoir operator, a simple but effective decision-making method was presented to choose the desired alternative from a set of Pareto-optimal solutions. Finally, the proposed approach was applied to the ecological operation of the reservoirs at the main stream of Xiuhe river in Poyang Lake basin in China. The results show that the proposed approach is able to offer many alternative policies for the water resources managers, and it is a viable alternative to solve multi-objective water resources and hydrology problems.

scholarly journals Performance evaluation of elitist-mutated multi-objective particle swarm optimization for integrated water resources management

2009 ◽  
Vol 11 (1) ◽  
pp. 79-88 ◽  
Author(s):  
M. Janga Reddy ◽  
D. Nagesh Kumar
Keyword(s):  
Decision Making ◽  
Particle Swarm Optimization ◽  
Resource Management ◽  
Water Resources ◽  
Water Resource Management ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Swarm Optimization ◽  
Multi Objective

Optimal allocation of water resources for various stakeholders often involves considerable complexity with several conflicting goals, which often leads to multi-objective optimization. In aid of effective decision-making to the water managers, apart from developing effective multi-objective mathematical models, there is a greater necessity of providing efficient Pareto optimal solutions to the real world problems. This study proposes a swarm-intelligence-based multi-objective technique, namely the elitist-mutated multi-objective particle swarm optimization technique (EM-MOPSO), for arriving at efficient Pareto optimal solutions to the multi-objective water resource management problems. The EM-MOPSO technique is applied to a case study of the multi-objective reservoir operation problem. The model performance is evaluated by comparing with results of a non-dominated sorting genetic algorithm (NSGA-II) model, and it is found that the EM-MOPSO method results in better performance. The developed method can be used as an effective aid for multi-objective decision-making in integrated water resource management.

scholarly journals Multicriteria Ship Route Planning Method Based on Improved Particle Swarm Optimization–Genetic Algorithm

2021 ◽  
Vol 9 (4) ◽  
pp. 357
Author(s):  
Wei Zhao ◽  
Yan Wang ◽  
Zhanshuo Zhang ◽  
Hongbo Wang
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Fuel Consumption ◽  
Particle Swarm ◽  
Route Planning ◽  
Planning Method ◽  
Pareto Optimal ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Ship Route

With the continuous prosperity and development of the shipping industry, it is necessary and meaningful to plan a safe, green, and efficient route for ships sailing far away. In this study, a hybrid multicriteria ship route planning method based on improved particle swarm optimization–genetic algorithm is presented, which aims to optimize the meteorological risk, fuel consumption, and navigation time associated with a ship. The proposed algorithm not only has the fast convergence of the particle swarm algorithm but also improves the diversity of solutions by applying the crossover operation, selection operation, and multigroup elite selection operation of the genetic algorithm and improving the Pareto optimal frontier distribution. Based on the Pareto optimal solution set obtained by the algorithm, the minimum-navigation-time route, the minimum-fuel-consumption route, the minimum-navigation-risk route, and the recommended route can be obtained. Herein, a simulation experiment is conducted with respect to a container ship, and the optimization route is compared and analyzed. Experimental results show that the proposed algorithm can plan a series of feasible ship routes to ensure safety, greenness, and economy and that it provides route selection references for captains and shipping companies.

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