An improved CPSO algorithm

Borowska, Bożena

doi:10.1109/stc-csit.2016.7589854

Cited by 4 publications

(4 citation statements)

References 26 publications

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“…Traditional particle swarm optimization easily converges to local optima during the optimization process [ 33 ]. We use a parallel particle swarm optimization algorithm, in order to address this problem.…”

Section: Parallel Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Multi-Objective and Parallel Particle Swarm Optimization Algorithm for Container-Based Microservice Scheduling

Chen

Xiao

2021

Sensors

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An application based on a microservice architecture with a set of independent, fine-grained modular services is desirable, due to its low management cost, simple deployment, and high portability. This type of container technology has been widely used in cloud computing. Several methods have been applied to container-based microservice scheduling, but they come with significant disadvantages, such as high network transmission overhead, ineffective load balancing, and low service reliability. In order to overcome these disadvantages, in this study, we present a multi-objective optimization problem for container-based microservice scheduling. Our approach is based on the particle swarm optimization algorithm, combined parallel computing, and Pareto-optimal theory. The particle swarm optimization algorithm has fast convergence speed, fewer parameters, and many other advantages. First, we detail the various resources of the physical nodes, cluster, local load balancing, failure rate, and other aspects. Then, we discuss our improvement with respect to the relevant parameters. Second, we create a multi-objective optimization model and use a multi-objective optimization parallel particle swarm optimization algorithm for container-based microservice scheduling (MOPPSO-CMS). This algorithm is based on user needs and can effectively balance the performance of the cluster. After comparative experiments, we found that the algorithm can achieve good results, in terms of load balancing, network transmission overhead, and optimization speed.

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Section: Parallel Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Multi-Objective and Parallel Particle Swarm Optimization Algorithm for Container-Based Microservice Scheduling

Chen

Xiao

2021

Sensors

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“…Swarm intelligence (SI) is a branch of artificial intelligence (AI) based on the social behavior of simple organisms occurring in natural environments [ 1 ]. The source of its inspiration was observations of the collective behavior of animals such as birds, fishes, bees, bacteria, ants, squirrels and others [ 2 , 3 , 4 , 5 , 6 , 7 , 8 ]. There is a number of methods based on swarm intelligence.…”

Section: Introductionmentioning

confidence: 99%

“…The key to the success of the method is the ability to share information found by population individuals. Due to many advantages (simplicity, easy implementation, lack of coding and special operators) [ 4 ], the PSO method has been widely applied in solving various optimization problems, including control systems [ 10 ], prediction problems [ 11 ], image classification [ 12 ], energy management [ 13 ], bilevel programming problems [ 14 , 15 ], antenna design [ 16 ], scheduling problems [ 17 , 18 ], electromagnetism [ 19 , 20 ] and many others.…”

Section: Introductionmentioning

confidence: 99%

Learning Competitive Swarm Optimization

Borowska¹

2022

Entropy

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Particle swarm optimization (PSO) is a popular method widely used in solving different optimization problems. Unfortunately, in the case of complex multidimensional problems, PSO encounters some troubles associated with the excessive loss of population diversity and exploration ability. This leads to a deterioration in the effectiveness of the method and premature convergence. In order to prevent these inconveniences, in this paper, a learning competitive swarm optimization algorithm (LCSO) based on the particle swarm optimization method and the competition mechanism is proposed. In the first phase of LCSO, the swarm is divided into sub-swarms, each of which can work in parallel. In each sub-swarm, particles participate in the tournament. The participants of the tournament update their knowledge by learning from their competitors. In the second phase, information is exchanged between sub-swarms. The new algorithm was examined on a set of test functions. To evaluate the effectiveness of the proposed LCSO, the test results were compared with those achieved through the competitive swarm optimizer (CSO), comprehensive particle swarm optimizer (CLPSO), PSO, fully informed particle swarm (FIPS), covariance matrix adaptation evolution strategy (CMA-ES) and heterogeneous comprehensive learning particle swarm optimization (HCLPSO). The experimental results indicate that the proposed approach enhances the entropy of the particle swarm and improves the search process. Moreover, the LCSO algorithm is statistically and significantly more efficient than the other tested methods.

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“…The particle swarm optimization (PSO) is a computational intelligence-oriented, stochastic, population-based global optimization technique [27]. It is concerned with the elementary algorithm, which has the characteristics of simplicity, easy implementation, and few parameters to be adjusted [28]. However, the PSO seems to be sensitive to the tuning of its parameters [29].…”

Section: Introductionmentioning

confidence: 99%

Parameter Optimization on FNN/PID Compound Controller for a Three-Axis Inertially Stabilized Platform for Aerial Remote Sensing Applications

et al. 2019

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This paper presents a composite parameter optimization method based on the chaos particle swarm optimization and the back propagation algorithms for a fuzzy neural network/proportion integration differentiation compound controller, which is applied for an aerial inertially stabilized platform for aerial remote sensing applications. Firstly, a compound controller combining both the adaptive fuzzy neural network and traditional PID control methods is developed to deal with the contradiction between the control precision and robustness due to disturbances. Then, on the basis of both the chaos particle swarm optimization and the back propagation compound algorithms, the parameters of the fuzzy neural network/PID compound controller are optimized offline and fine-tuned online, respectively. In this way, the compound controller can achieve good adaptive convergence so as to get high stabilization precision under the multisource dynamic disturbance environment. To verify the method, the simulations are carried out. The results show that the composite parameter optimization method can effectively enhance the convergence of the controller, by which the stabilization precision and disturbance rejection capability of the proposed fuzzy neural network/PID compound controller are improved obviously.

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An improved CPSO algorithm

Cited by 4 publications

References 26 publications

Multi-Objective and Parallel Particle Swarm Optimization Algorithm for Container-Based Microservice Scheduling

Multi-Objective and Parallel Particle Swarm Optimization Algorithm for Container-Based Microservice Scheduling

Learning Competitive Swarm Optimization

Parameter Optimization on FNN/PID Compound Controller for a Three-Axis Inertially Stabilized Platform for Aerial Remote Sensing Applications

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