Particle swarm optimisation for Pareto optimal solutions in electromagnetic shape design

2004 ◽  
Vol 151 (6) ◽  
pp. 499-502 ◽  
Author(s):  
U. Baumgartner ◽  
W. Renhart ◽  
K. Preis ◽  
C. Magele
Keyword(s):  
Particle Swarm ◽  
Particle Swarm Optimisation ◽  
Shape Design ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions

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 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 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.

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 Optimization Of Multi-Module CrN/CrCN Coatings

2015 ◽  
Vol 60 (2) ◽  
pp. 1037-1043
Author(s):  
Ł. Szparaga ◽  
P. Bartosik ◽  
A. Gilewicz ◽  
J. Ratajski
Keyword(s):  
Optimization Procedure ◽  
Stress And Strain ◽  
Pareto Optimal ◽  
Mutual Distance ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Decision Criteria ◽  
Decision Variables ◽  
42Crmo4 Steel ◽  
The Stability

Abstract In the paper was proposed optimization procedure supporting the prototyping of the geometry of multi-module CrN/CrCN coatings, deposited on substrates from 42CrMo4 steel, in respect of mechanical properties. Adopted decision criteria were the functions of the state of internal stress and strain in the coating and substrate, caused by external mechanical loads. Using developed optimization procedure the set of optimal solutions (Pareto-optimal solutions) of coatings geometry parameters, due to the adopted decision criteria was obtained. For the purposes of analysis of obtained Pareto-optimal solutions, their mutual distance in the space of criteria and decision variables were calculated, which allowed to group solutions in the classes. Also analyzed the number of direct neighbors of Pareto-optimal solutions for the purposes of assessing the stability of solutions.

A DECOMPOSITION-COORDINATION METHOD FOR COMPLEX MULTI-OBJECTIVE SYSTEMS

2009 ◽  
Vol 26 (06) ◽  
pp. 735-757 ◽  
Author(s):  
F. MIGUEL ◽  
T. GÓMEZ ◽  
M. LUQUE ◽  
F. RUIZ ◽  
R. CABALLERO
Keyword(s):  
Information Exchange ◽  
Exchange Process ◽  
Efficient Solutions ◽  
Pareto Optimal ◽  
Relative Importance ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Conflicting Objectives ◽  
Upper Level ◽  
Problem Instances

The generation of Pareto optimal solutions for complex systems with multiple conflicting objectives can be easier if the problem can be decomposed and solved as a set of smaller coordinated subproblems. In this paper, a new decomposition-coordination method is proposed, where the global problem is partitioned into subsystems on the basis of the connection structure of the mathematical model, assigning a relative importance to each of them. In order to obtain Pareto optimal solutions for the global system, the aforementioned subproblems are coordinated taking into account their relative importance. The scheme that has been developed is an iterative one, and the global efficient solutions are found through a continuous information exchange process between the coordination level (upper level) and the subsystem level (lower level). Computational experiments on several randomly generated problem instances show that the suggested algorithm produces efficient solutions within reasonable computational times.

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