Optimization of plant location problem in interval domain via particle swarm optimization

2021 ◽  
Author(s):  
Laxminarayan Sahoo ◽  
Asoke Kumar Bhunia
Keyword(s):  
Particle Swarm Optimization ◽  
Location Problem ◽  
Particle Swarm ◽  
Plant Location ◽  
Swarm Optimization

scholarly journals A Discrete Particle Swarm Optimization Algorithm for Uncapacitated Facility Location Problem

2008 ◽  
Vol 2008 ◽  
pp. 1-9 ◽  
Author(s):  
Ali R. Guner ◽  
Mehmet Sevkli
Keyword(s):  
Particle Swarm Optimization ◽  
Facility Location ◽  
Location Problem ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Facility Location Problem ◽  
Discrete Version ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Better Than

A discrete version of particle swarm optimization (DPSO) is employed to solve uncapacitated facility location (UFL) problem which is one of the most widely studied in combinatorial optimization. In addition, a hybrid version with a local search is defined to get more efficient results. The results are compared with a continuous particle swarm optimization (CPSO) algorithm and two other metaheuristics studies, namely, genetic algorithm (GA) and evolutionary simulated annealing (ESA). To make a reasonable comparison, we applied to same benchmark suites that are collected from OR-library. In conclusion, the results showed that DPSO algorithm is slightly better than CPSO algorithm and competitive with GA and ESA.

Optimal Sensor Placement Based on Particle Swarm Optimization

2011 ◽  
Vol 271-273 ◽  
pp. 1108-1113 ◽  
Author(s):  
Bo Ying Qin ◽  
Xian Kun Lin
Keyword(s):  
Particle Swarm Optimization ◽  
Location Problem ◽  
Dynamic Testing ◽  
Information Matrix ◽  
Particle Swarm ◽  
Optimal Method ◽  
Swarm Optimization ◽  
Optimization Criteria ◽  
Optimal Sensor Location ◽  
Sensor Locations

In order to dispose sensors to reasonable freedom degrees, and reflect adequately the dynamic characteristics of tested structure, the sensor locations of dynamic testing must be optimized. In this paper, taking MAC matrix, Fisher information matrix (FIM), and their combinations as optimization criteria respectively, the particle swarm optimization (PSO) was applied to the optimal sensor location problem (OSLP). The effect of optimization criteria and optimal method to optimal sensor locations were discussed. According to the optimized results, we can arrived at the following conclusions: using MAC and FIM as optimal criteria, introducing the PSO into the OSLP, the optimal sensor locations can ensure the better linear independence of the mode shape vectors and the better estimation of the experimental modal parameters.

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