Flexible Synthesis of Heat Exchanger Network with Particle Swarm Optimization Algorithm

2011 ◽  
Vol 214 ◽  
pp. 569-572 ◽  
Author(s):  
Xio Ling Zhang ◽  
Hong Chao Yin ◽  
Zhao Yi Huo
Keyword(s):  
Particle Swarm Optimization ◽  
Heat Exchanger ◽  
Operating Cost ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Nonlinear Program ◽  
Heat Exchanger Network ◽  
Swarm Optimization

In this paper, the flexible synthesis problem for heat exchanger network(HEN) is formulated to a mixed integer nonlinear program(MINLP) model. The objection function of the model consists of two components: First, a candidate HEN structure has to satisfy flexible criterion during input span. Second, a minimized annual cost consisting of investment cost and operating cost is investigated. The solution strategy based on particle swarm optimization(PSO) algorithm is proposed to obtain the optimal solution of the presented model. Finally, a four streams example is investigated to show the advantage of the whole proposed optimization approach.

Optimal Operational Planning of Utility Systems in Petrochemical Plants Using an Improved Particle Swarm Optimization Algorithm

2010 ◽  
Vol 143-144 ◽  
pp. 1364-1369
Author(s):  
Wen Zhi Dai ◽  
Zhao Yi Huo ◽  
Hong Chao Yin ◽  
Hai Feng Liang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Mixed Integer Linear Program ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Utility Systems ◽  
New Algorithms

In this paper, the operation optimization problem for utility systems is formulated and a mixed integer linear program (MILP) model is presented. The objective function of the model is to minimize the operational cost of utility systems during the whole operational period. In order to obtain the optimal solution of the foregoing model, an improved particle swarm optimization is proposed. Finally, a case with quantitive results presented is considered for illustrating the advantage of proposed optimization approach. Results show that the new algorithms are much more efficient than some existing particle swarm optimization algorithms.

Optimal Heat Exchanger Network Synthesis via Particle Swarm Optimization

2011 ◽  
Vol 148-149 ◽  
pp. 1468-1472
Author(s):  
Ping Wang ◽  
Jun Liang Xu ◽  
Tao Lu
Keyword(s):  
Particle Swarm Optimization ◽  
Heat Exchanger ◽  
Particle Swarm ◽  
Network Synthesis ◽  
Heat Exchanger Network ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Solution Accuracy ◽  
Operation Cost ◽  
Heat Exchanger Network Synthesis

On the basis of superstructure of heat exchanger network (HEN), we established a particle swarm optimization (PSO) model of HEN with no splits, with the target of minimizing investment and operation cost. A typical HEN was solved via a modified particle swarm optimization (PSO). Through comparative of the optimization result, we could know that this method could reach better solution accuracy.

Hybridization of Biogeography-Based

2013 ◽  
Vol 2 (3) ◽  
pp. 86-101 ◽  
Author(s):  
Provas Kumar Roy ◽  
Dharmadas Mandal
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Optimal Power Flow ◽  
Particle Swarm ◽  
Population Based ◽  
Optimization Techniques ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Swarm Optimization

The aim of this paper is to evaluate a hybrid biogeography-based optimization approach based on the hybridization of biogeography-based optimization with differential evolution to solve the optimal power flow problem. The proposed method combines the exploration of differential evolution with the exploitation of biogeography-based optimization effectively to generate the promising candidate solutions. Simulation experiments are carried on standard 26-bus and IEEE 30-bus systems to illustrate the efficacy of the proposed approach. Results demonstrated that the proposed approach converged to promising solutions in terms of quality and convergence rate when compared with the original biogeography-based optimization and other population based optimization techniques like simple genetic algorithm, mixed integer genetic algorithm, particle swarm optimization and craziness based particle swarm optimization.

scholarly journals Particle Swarm Optimization for Load Balancing in Distributed Computing Systems – A Survey

2021 ◽  
Vol 12 (1S) ◽  
pp. 257-265
Author(s):  
Vidya S. Handur, Et. al.
Keyword(s):  
Particle Swarm Optimization ◽  
Distributed Computing ◽  
Load Balancing ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Computing System ◽  
Optimization Approach ◽  
Distributed Computing Systems ◽  
Computing Systems ◽  
Swarm Optimization

Development of technology like Cloud Computing and its widespread usage has given rise to exponential increase in the volume of traffic. With this increase in huge traffic the resources in the network would either be insufficient to handle the traffic or the situation may cause some of the resources to be over utilized or underutilized. This condition leads to reduced performance of the system. To improve the performance of the system the traffic requires to be regulated such that all the resources are utilized conferring to their capacity which is known as load balancing. Load balancing has been one of the concerns in the distributed computing systems where the computing nodes do not have a global view of the network. There have been constant efforts to provide an efficient solution for load balancing through the approaches like game theory, fuzzy logic, heuristics and metaheuristics. Even though various solutions exist for balancing the load, the issue is challenging as there does not exist one best fit solution. The paper aims at the study of how Particle Swarm Optimization approach is used to achieve an optimal solution for load balancing in distributed computing system.

scholarly journals Particle swarm optimization for airlines fleet assignment

2021 ◽  
Vol 22 (1) ◽  
pp. 427
Author(s):  
Abdallah A. Abouzeid ◽  
Mostafa Mohei Eldin ◽  
Mohammed Abdel Razek
Keyword(s):  
Particle Swarm Optimization ◽  
Integer Programming ◽  
Mixed Integer Programming ◽  
Operating Cost ◽  
Particle Swarm ◽  
Mixed Integer ◽  
Fleet Assignment ◽  
Solution Quality ◽  
Swarm Optimization ◽  
Airline Fleet

Airline fleet assignment is the process of assigning aircraft types to scheduled flight legs in order to minimize operating cost and achieve maximize revenue, while satisfying a set of constraints. This paper formulate the fleet assignment problem for airlines that optimization goal is to minimize the total assignment cost. Particle swarm optimization proposed to solve this model. The model successfully applied to Egyptair airline dataset using the particle swarm optimization and mixed integer programming. The proposed method compared with mixed integer programming and current Egyptair assignment methodology. The results showed that the particle swarm optimization is the best method for the Egyptair fleet assignment process. The solution quality is better than mixed integer programming and Egyptair assignment methodology where we saw a daily cost reduction with a percentage of 14.6% and 19.3% respectively.

Solving Large-Scale Heat Exchanger Network Synthesis Problems Using Particle Swarm Optimization

2011 ◽  
Vol 148-149 ◽  
pp. 636-640 ◽  
Author(s):  
Zhao Yi Huo ◽  
Liang Zhao ◽  
Hong Chao Yin
Keyword(s):  
Particle Swarm Optimization ◽  
Heat Exchanger ◽  
Large Scale ◽  
Particle Swarm ◽  
Network Synthesis ◽  
Heat Exchanger Network ◽  
Swarm Optimization ◽  
Optimal Network ◽  
Simultaneous Synthesis ◽  
Heat Exchanger Network Synthesis

Heat exchanger network synthesis has been one of the most popular subjects in process design over the last 50 years. Various studies and optimization techniques have been proposed for designing optimal network with minimum total annual cost. Simultaneous synthesis approach via mathematical programming aims to find the optimal network without decomposition, which has been paid more attentions on the research recently. However, these methods might be not solvable or inefficient for large-scale problems. This paper makes an attempt to construct simultaneous synthesis model with split streams and to develop an efficient optimization framework based on particle swarm optimization for large-scale heat exchanger network synthesis problems. One example including 20 process streams is solved to give an illustration of the method.

Optimal heat exchanger network synthesis using particle swarm optimization

2009 ◽  
Vol 11 (3) ◽  
pp. 459-470 ◽  
Author(s):  
Aline P. Silva ◽  
Mauro A. S. S. Ravagnani ◽  
Evaristo C. Biscaia ◽  
Jose A. Caballero
Keyword(s):  
Particle Swarm Optimization ◽  
Heat Exchanger ◽  
Particle Swarm ◽  
Network Synthesis ◽  
Heat Exchanger Network ◽  
Swarm Optimization ◽  
Optimal Heat ◽  
Heat Exchanger Network Synthesis

A Self-Learning Particle Swarm Optimization for Robust Multi-Echelon Capacitated Location–Allocation–Inventory Problem

2019 ◽  
Vol 18 (04) ◽  
pp. 677-694 ◽  
Author(s):  
Erfan Babaee Tirkolaee ◽  
Javad Mahmoodkhani ◽  
Mehdi Ranjbar Bourani ◽  
Reza Tavakkoli-Moghaddam
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Upper And Lower Bounds ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Inventory Problem ◽  
Swarm Optimization ◽  
Location Allocation ◽  
Self Learning ◽  
Capacitated Location

This paper addresses a multi-echelon capacitated location–allocation–inventory problem under uncertainty by providing a robust mixed integer linear programming (MILP) model considering production plants at level one, central warehouses at level two, and the retailers at level three in order to design an optimal supply chain network. In this model, the retailer’s demand parameter is uncertain and just its upper and lower bounds within an interval are known. In order to deal with this uncertainty, a robust optimization approach is used. Then, a self-learning particle swarm optimization (SLPSO) algorithm is developed to solve the problem. The results show that the proposed algorithm outperforms the exact method by providing high quality solutions in the reasonable amount of computational runtime.

Sign in / Sign up

Export Citation Format

Share Document