Generating Bin Packing Heuristic Through Grammatical Evolution Based on Bee Swarm Optimization

2016 ◽  
pp. 655-671 ◽  
Author(s):  
Marco Aurelio Sotelo-Figueroa ◽  
Héctor José Puga Soberanes ◽  
Juan Martín Carpio ◽  
Héctor J. Fraire Huacuja ◽  
Laura Cruz Reyes ◽  
...  
Keyword(s):  
Bin Packing ◽  
Grammatical Evolution ◽  
Swarm Optimization

scholarly journals Improving the Bin Packing Heuristic through Grammatical Evolution Based on Swarm Intelligence

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Marco Aurelio Sotelo-Figueroa ◽  
Héctor José Puga Soberanes ◽  
Juan Martín Carpio ◽  
Héctor J. Fraire Huacuja ◽  
Laura Cruz Reyes ◽  
...  
Keyword(s):  
Swarm Intelligence ◽  
Boolean Functions ◽  
Bin Packing ◽  
Optimization Problems ◽  
Packing Problem ◽  
Grammatical Evolution ◽  
Friedman Test ◽  
Swarm Optimization ◽  
Algorithmic Problems ◽  
Test Instance

In recent years Grammatical Evolution (GE) has been used as a representation of Genetic Programming (GP) which has been applied to many optimization problems such as symbolic regression, classification, Boolean functions, constructed problems, and algorithmic problems. GE can use a diversity of searching strategies including Swarm Intelligence (SI). Particle Swarm Optimisation (PSO) is an algorithm of SI that has two main problems: premature convergence and poor diversity. Particle Evolutionary Swarm Optimization (PESO) is a recent and novel algorithm which is also part of SI. PESO uses two perturbations to avoid PSO’s problems. In this paper we propose using PESO and PSO in the frame of GE as strategies to generate heuristics that solve the Bin Packing Problem (BPP); it is possible however to apply this methodology to other kinds of problems using another Grammar designed for that problem. A comparison between PESO, PSO, and BPP’s heuristics is performed through the nonparametric Friedman test. The main contribution of this paper is proposing a Grammar to generate online and offline heuristics depending on the test instance trying to improve the heuristics generated by other grammars and humans; it also proposes a way to implement different algorithms as search strategies in GE like PESO to obtain better results than those obtained by PSO.

scholarly journals Virtual Machine Placement via Bin Packing in Cloud Data Centers

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 389 ◽  
Author(s):  
Aisha Fatima ◽  
Nadeem Javaid ◽  
Tanzeela Sultana ◽  
Waqar Hussain ◽  
Muhammad Bilal ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Data Centers ◽  
Bin Packing ◽  
Virtual Machines ◽  
Particle Swarm ◽  
Vm Placement ◽  
Swarm Optimization ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Vm Consolidation

With the increasing size of cloud data centers, the number of users and virtual machines (VMs) increases rapidly. The requests of users are entertained by VMs residing on physical servers. The dramatic growth of internet services results in unbalanced network resources. Resource management is an important factor for the performance of a cloud. Various techniques are used to manage the resources of a cloud efficiently. VM-consolidation is an intelligent and efficient strategy to balance the load of cloud data centers. VM-placement is an important subproblem of the VM-consolidation problem that needs to be resolved. The basic objective of VM-placement is to minimize the utilization rate of physical machines (PMs). VM-placement is used to save energy and cost. An enhanced levy-based particle swarm optimization algorithm with variable sized bin packing (PSOLBP) is proposed for solving the VM-placement problem. Moreover, the best-fit strategy is also used with the variable sized bin packing problem (VSBPP). Simulations are done to authenticate the adaptivity of the proposed algorithm. Three algorithms are implemented in Matlab. The given algorithm is compared with simple particle swarm optimization (PSO) and a hybrid of levy flight and particle swarm optimization (LFPSO). The proposed algorithm efficiently minimized the number of running PMs. VM-consolidation is an NP-hard problem, however, the proposed algorithm outperformed the other two algorithms.

Clustering Bin Packing Instances for Generating a Minimal Set of Heuristics by Using Grammatical Evolution

2014 ◽  
pp. 151-162
Author(s):  
Marco Aurelio Sotelo-Figueroa ◽  
Héctor José Puga Soberanes ◽  
Juan Martín Carpio ◽  
Héctor J. Fraire Huacuja ◽  
Laura Cruz Reyes ◽  
...  
Keyword(s):  
Bin Packing ◽  
Grammatical Evolution ◽  
Minimal Set

scholarly journals Analisis Perbandingan Metode Genetic Algorithm dan Particle Swarm Optimization dalam Menilai Tingkat Optimasi Hasil Pada Bin Packing Problem Satu Dimensi

2018 ◽  
Author(s):  
Adidtya Perdana
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Bin Packing ◽  
Particle Swarm ◽  
Packing Problem ◽  
Swarm Optimization ◽  
Bin Packing Problem

Paper ini adalah hasil karya publikasi dalam bentuk prosiding di Seminar Nasional Teknologi Informasi dan Komunikasi (SNASTIKOM) tahun 2017 yang diadakan pada tanggal 11 sampai 12 Februari 2017 yang diadakan di hotel grand kanaya medan. Dengan ISBN Prosiding adalah 978-602-61039-0-1

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

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