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.

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.

scholarly journals Particle Swarm Optimization Combined with Inertia-Free Velocity and Direction Search

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 597
Author(s):  
Kun Miao ◽  
Qian Feng ◽  
Wei Kuang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Small Region ◽  
Benchmark Problems ◽  
Swarm Optimization ◽  
Local Searches ◽  
Engineering Design Problems ◽  
Direction Search

The particle swarm optimization algorithm (PSO) is a widely used swarm-based natural inspired optimization algorithm. However, it suffers search stagnation from being trapped into a sub-optimal solution in an optimization problem. This paper proposes a novel hybrid algorithm (SDPSO) to improve its performance on local searches. The algorithm merges two strategies, the static exploitation (SE, a velocity updating strategy considering inertia-free velocity), and the direction search (DS) of Rosenbrock method, into the original PSO. With this hybrid, on the one hand, extensive exploration is still maintained by PSO; on the other hand, the SE is responsible for locating a small region, and then the DS further intensifies the search. The SDPSO algorithm was implemented and tested on unconstrained benchmark problems (CEC2014) and some constrained engineering design problems. The performance of SDPSO is compared with that of other optimization algorithms, and the results show that SDPSO has a competitive performance.

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