scholarly journals Genetic Algorithms and Particle Swarm Optimization for Interference Minimization in Mobile Network Channel Assignment Problem

2021 ◽  
Vol 14 (4) ◽  
pp. 276-288
Author(s):  
Joe Keek ◽  
◽  
Ser Loh ◽  
Yan Wong ◽  
Xiu Woo ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Particle Swarm Optimization ◽  
Channel Assignment ◽  
Assignment Problem ◽  
Particle Swarm ◽  
Mobile Network ◽  
Swarm Optimization ◽  
Channel Assignment Problem ◽  
Interference Minimization ◽  
Network Channel

scholarly journals SIMULATION OF PSO BASED APPROACH FOR CMOL CELL ASSIGNMENT PROBLEM

2015 ◽  
Vol 3 (5) ◽  
pp. 1-12
Author(s):  
Prateek Shrivastava ◽  
Khemraj Deshmukh
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Particle Swarm Optimization ◽  
Assignment Problem ◽  
Optimization Technique ◽  
Particle Swarm ◽  
The Other ◽  
Swarm Optimization ◽  
Cell Assignment

Particle swarm optimization (PSO) approach is used over genetic algorithms (GAS) to solve many of the same kinds of problems. This optimization technique does not suffer, however, from some of GA’s difficulties; interaction in the group enhances rather than detracts from progress toward the solution. Further, a particle swarm system has memory, which the genetic algorithm does not have. In particle swarm optimization, individuals who fly past optima are tugged to return toward them; knowledge of good solutions is retained by all particles. The genetic algorithm works with the concept of chromosomes having gene where each gene act as a block of one solution. This is totally based on the solution which is followed by crossover and then mutation and finally reaches to fitness. The best fitness will be considered as a result and implemented in the practical area. Due to some drawbacks and problems exist in the genetic algorithm implemented, scientists moved to the other algorithm technique which is apparently based on the flock of birds moving to the target. This effectively overcome the shortcomings of GA and provides better fitness solutions to implement in the circuit.

On the Use of Particle Swarm Optimization Techniques for Channel Assignments in Cognitive Radio Networks

2012 ◽  
pp. 202-214 ◽  
Author(s):  
Hisham M. Abdelsalam ◽  
Haitham S. Hamza ◽  
Abdoulraham M. Al-Shaar ◽  
Abdelbaset S. Hamza
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Channel Assignment ◽  
Assignment Problem ◽  
Pso Algorithm ◽  
Radio Networks ◽  
Optimization Techniques ◽  
Secondary Users ◽  
Swarm Optimization ◽  
Channel Assignment Problem

Efficient utilization of open spectrum in cognitive radio networks requires appropriate allocation of idle spectrum frequency bands (not used by licensed users) among coexisting cognitive radios (secondary users) while minimizing interference among all users. This problem is referred to as the spectrum allocation or the channel assignment problem in cognitive radio networks, and is shown to be NP-hard. Accordingly, different optimization techniques based on evolutionary algorithms were needed in order to solve the channel assignment problem. This chapter investigates the use of particular swarm optimization (PSO) techniques to solve the channel assignment problem in cognitive radio networks. In particular, the authors study the definitiveness of using the native PSO algorithm and the Improved Binary PSO (IBPSO) algorithm to solve the assignment problem. In addition, the performance of these algorithms is compared to that of a fine-tuned genetic algorithm (GA) for this particular problem. Three utilization functions, namely, Mean-Reward, Max-Min-Reward, and Max-Proportional-Fair, are used to evaluate the effectiveness of three optimization algorithms. Extensive simulation results show that PSO and IBPSO algorithms outperform that fine-tuned GA. More interestingly, the native PSO algorithm outperforms both the GA and the IBPSO algorithms in terms of solution speed and quality.

scholarly journals Particle Swarm Optimization Using Neighborhood-Based Mutation Operator and Intermediate Disturbance Strategy for Outbound Container Storage Location Assignment Problem

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Yuyan He ◽  
Aihu Wang ◽  
Hailiang Su ◽  
Mengyao Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Assignment Problem ◽  
Particle Swarm ◽  
Local Solution ◽  
Mutation Operator ◽  
Swarm Optimization ◽  
Intermediate Disturbance ◽  
Storage Location ◽  
Location Assignment ◽  
Container Storage Location Assignment

Outbound container storage location assignment problem (OCSLAP) could be defined as how a series of outbound containers should be stacked in the yard according to certain assignment rules so that the outbound process could be facilitated. Considering the NP-hard nature of OCSLAP, a novel particle swarm optimization (PSO) method is proposed. The contributions of this paper could be outlined as follows: First, a neighborhood-based mutation operator is introduced to enrich the diversity of the population to strengthen the exploitation ability of the proposed algorithm. Second, a mechanism to transform the infeasible solutions into feasible ones through the lowest stack principle is proposed. Then, in the case of trapping into the local solution in the search process, an intermediate disturbance strategy is implemented to quickly jump out of the local solution, thereby enhancing the global search capability. Finally, numerical experiments have been done and the results indicate that the proposed algorithm achieves a better performance in solving OCSLAP.

scholarly journals Training Multilayer Perceptron with Genetic Algorithms and Particle Swarm Optimization for Modeling Stock Price Index Prediction

Entropy ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. 1239
Author(s):  
Fatih Ecer ◽  
Sina Ardabili ◽  
Shahab S. Band ◽  
Amir Mosavi
Keyword(s):  
Genetic Algorithms ◽  
Particle Swarm Optimization ◽  
Population Size ◽  
Correlation Coefficient ◽  
Multilayer Perceptron ◽  
Gaussian Function ◽  
Particle Swarm ◽  
Series Data ◽  
Output Function ◽  
Swarm Optimization

Predicting stock market (SM) trends is an issue of great interest among researchers, investors and traders since the successful prediction of SMs’ direction may promise various benefits. Because of the fairly nonlinear nature of the historical data, accurate estimation of the SM direction is a rather challenging issue. The aim of this study is to present a novel machine learning (ML) model to forecast the movement of the Borsa Istanbul (BIST) 100 index. Modeling was performed by multilayer perceptron–genetic algorithms (MLP–GA) and multilayer perceptron–particle swarm optimization (MLP–PSO) in two scenarios considering Tanh (x) and the default Gaussian function as the output function. The historical financial time series data utilized in this research is from 1996 to 2020, consisting of nine technical indicators. Results are assessed using Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and correlation coefficient values to compare the accuracy and performance of the developed models. Based on the results, the involvement of the Tanh (x) as the output function, improved the accuracy of models compared with the default Gaussian function, significantly. MLP–PSO with population size 125, followed by MLP–GA with population size 50, provided higher accuracy for testing, reporting RMSE of 0.732583 and 0.733063, MAPE of 28.16%, 29.09% and correlation coefficient of 0.694 and 0.695, respectively. According to the results, using the hybrid ML method could successfully improve the prediction accuracy.

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