scholarly journals Hepatitis Disease Diagnosis Using Hybrid Case Based Reasoning and Particle Swarm Optimization

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Mehdi Neshat ◽  
Mehdi Sargolzaei ◽  
Adel Nadjaran Toosi ◽  
Azra Masoumi
Keyword(s):  
Particle Swarm Optimization ◽  
Correct Diagnosis ◽  
Particle Swarm ◽  
Weight Vector ◽  
Disease Diagnosis ◽  
Case Based Reasoning ◽  
Data Set ◽  
Swarm Optimization ◽  
Decision Making System ◽  
Case Based

Correct diagnosis of a disease is one of the most important problems in medicine. Hepatitis disease is one of the most dangerous diseases that affect millions of people every year and take man’s life. In this paper, the combination of two methods of PSO and CBR (case-based reasoning) has been used to diagnose hepatitis disease. First, a case-based reasoning method is workable to preprocess the data set therefore a weight vector for every one feature is extracted. A particle swarm optimization model is then practical to assemble a decision-making system based on the selected features and diseases recognized. Many researchers have tried to have a more accurate diagnosis of the disease through the use of various methods. The data used has been taken from the site UCI called hepatitis disease. This database has 155 records and 19 fields. This method was compared with five other classification methods and given the results of the proposed method (CBR-PSO), better results were achieved. The proposed method could diagnose hepatitis disease with the accuracy of 93.25%.

An Improved Particle Swarm Optimization Algorithm for Dynamic Power Allocation Problem

2014 ◽  
Vol 687-691 ◽  
pp. 1380-1384
Author(s):  
Jian Jun Zhao ◽  
Wen Jie Zhao
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Dynamic Environment ◽  
Particle Swarm ◽  
Case Based Reasoning ◽  
Optimal Solutions ◽  
Swarm Optimization ◽  
Multiobjective Particle Swarm Optimization ◽  
Case Based

In this paper, we propose a fast multiobjective particle swarm optimization algorithm (called CBR-fMOPSO for short). In the algorithm, a case-based reasoning (CBR) technique is used to retrieve history optimization results and experts’ experience and add them into the population of multiobjective particle swarm optimization algorithm (MOPSO) in dynamic environment. The optimal solutions found by CBR-fMOPSO are used to mend the case library to improve the accuracy of solving based on CBR in next solving. The results from a suit of experiments in electric furnaces show that the proposed algorithm maintains good performances however the environment changes.

A Particle Swarm Optimization Approach for Reuse Guided Case Retrieval

2014 ◽  
Vol 6 (3) ◽  
pp. 16-30 ◽  
Author(s):  
Nabila Nouaouria ◽  
Mounir Boukadoum ◽  
Robert Proulx
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Direct Access ◽  
Memory Model ◽  
Case Based Reasoning ◽  
Optimization Approach ◽  
Case Retrieval ◽  
Swarm Optimization ◽  
Case Based

The success of Case Based Reasoning (CBR) problem solving is mainly based on the recall process. The ideal CBR memory is one that simultaneously speeds up the retrieval step while improving the reuse of retrieved cases. In this paper, the authors present a novel associative memory model to perform the retrieval stage in a case based reasoning system. The described approach makes no prior assumption of a specific organization of the case memory, thus leading to a generic recall process. This is made possible by using Particle Swarm Optimization (PSO) to compute the neighborhood of a new problem, followed by direct access to the cases it contains. The fitness function of the PSO stage has a reuse semantic that combines similarity and adaptability as criteria for optimal case retrieval. The model was experimented on two proprietary databases and compared to the flat memory model for performance. The obtained results are very promising.

scholarly journals Provide a Diagnostic Model Using a Combination of Two Neural Network Algorithms and a Genetic Algorithm

2021 ◽  
Vol 10 (1) ◽  
pp. 91
Author(s):  
Farshad Minaei ◽  
Hassan Dosti ◽  
Ebrahim Salimi Turk ◽  
Amin Golabpour
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Cuckoo Search ◽  
Disease Diagnosis ◽  
Diagnostic Model ◽  
Data Set ◽  
Swarm Optimization ◽  
Artificial Neural

Introduction: Improvement of technology can increase the use of machine learning algorithms in predicting diseases. Early diagnosis of the disease can reduce mortality and morbidity at the community level.Material and Methods: In this paper, a clinical decision support system for the diagnosis of gestational diabetes is presented by combining artificial neural network and meta-heuristic algorithm. In this study, four meta-innovative algorithms of genetics, ant colony, particle Swarm optimization and cuckoo search were selected to be combined with artificial neural network. Then these four algorithms were compared with each other. The data set contains 768 records and 8 dependent variables. This data set has 200 missing records, so the number of study records was reduced to 568 records.Results: The data were divided into two sets of training and testing by 10-Fold method. Then, all four algorithms of neural-genetic network, ant-neural colony network, neural network-particle Swarm optimization and neural network-cuckoo search on the data The trainings were performed and then evaluated by the test set. And the accuracy of 95.02 was obtained. Also, the final output of the algorithm was examined with two similar tasks and it was shown that the proposed model worked better.Conclusion: In this study showed that the combination of two neural network and genetic algorithms can provide a suitable predictive model for disease diagnosis.

A Particle Swarm Optimization Approach to Fuzzy Case-based Reasoning in the Framework of Collaborative Filtering

2014 ◽  
Vol 1 (1) ◽  
pp. 48-64 ◽  
Author(s):  
Shweta Tyagi ◽  
Kamal K. Bharadwaj
Keyword(s):  
Particle Swarm Optimization ◽  
Collaborative Filtering ◽  
Prediction Accuracy ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Personalized Recommendation ◽  
Case Based Reasoning ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Case Based

The particle Swarm Optimization (PSO) algorithm, as one of the most effective search algorithm inspired from nature, is successfully applied in a variety of fields and is demonstrating fairly immense potential for development. Recently, researchers are investigating the use of PSO algorithm in the realm of personalized recommendation systems for providing tailored suggestions to users. Collaborative filtering (CF) is the most promising technique in recommender systems, providing personalized recommendations to users based on their previously expressed preferences and those of other similar users. However, data sparsity and prediction accuracy are the major concerns related to CF techniques. In order to handle these problems, this paper proposes a novel approach to CF technique by employing fuzzy case-based reasoning (FCBR) augmented with PSO algorithm, called PSO/FCBR/CF technique. In this method, the PSO algorithm is utilized to estimate the features importance and assign their weights accordingly in the process of fuzzy case-based reasoning (FCBR) for the computation of similarity between users and items. In this way, PSO embedded FCBR algorithm is applied for the prediction of missing values in user-item rating matrix and then CF technique is employed to generate recommendations for an active user. The experimental results clearly reveal that the proposed scheme, PSO/FCBR/CF, deals with the problem of sparsity as well as improves the prediction accuracy when compared with other state of the art CF schemes.

scholarly journals Multiobjective Synthesis of Linear Arrays by Using an Improved Genetic Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Bo Yang
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Weight Vector ◽  
Side Lobe Level ◽  
Improved Genetic Algorithm ◽  
Pattern Synthesis ◽  
Swarm Optimization ◽  
Dynamic Weight

In this paper, an improved genetic algorithm with dynamic weight vector (IGA-DWV) is proposed for the pattern synthesis of a linear array. To maintain the diversity of the selected solution in each generation, the objective function space is divided by the dynamic weight vector, which is uniformly distributed on the Pareto front (PF). The individuals closer to the dynamic weight vector can be chosen to the new population. Binary- and real-coded genetic algorithms (GAs) with a mapping method are implemented for different optimization problems. To reduce the computation complexity, the repeat calculation of the fitness function in each generation is replaced by a precomputed discrete cosine transform matrix. By transforming the array pattern synthesis into a multiobjective optimization problem, the conflict among the side lobe level (SLL), directivity, and nulls can be efficiently addressed. The proposed method is compared with real number particle swarm optimization (RNPSO) and quantized particle swarm optimization (QPSO) as applied in the pattern synthesis of a linear thinned array and a digital phased array. The numerical examples show that IGA-DWV can achieve a high performance with a lower SLL and more accurate nulls.

scholarly journals PSOVina: The hybrid particle swarm optimization algorithm for protein–ligand docking

2015 ◽  
Vol 13 (03) ◽  
pp. 1541007 ◽  
Author(s):  
Marcus C. K. Ng ◽  
Simon Fong ◽  
Shirley W. I. Siu
Keyword(s):  
Particle Swarm Optimization ◽  
Virtual Screening ◽  
Particle Swarm ◽  
Ligand Docking ◽  
Conformational Search ◽  
Search Problem ◽  
Data Set ◽  
Swarm Optimization ◽  
Screening Experiments ◽  
Hybrid Particle Swarm Optimization

Protein–ligand docking is an essential step in modern drug discovery process. The challenge here is to accurately predict and efficiently optimize the position and orientation of ligands in the binding pocket of a target protein. In this paper, we present a new method called PSOVina which combined the particle swarm optimization (PSO) algorithm with the efficient Broyden–Fletcher–Goldfarb–Shannon (BFGS) local search method adopted in AutoDock Vina to tackle the conformational search problem in docking. Using a diverse data set of 201 protein–ligand complexes from the PDBbind database and a full set of ligands and decoys for four representative targets from the directory of useful decoys (DUD) virtual screening data set, we assessed the docking performance of PSOVina in comparison to the original Vina program. Our results showed that PSOVina achieves a remarkable execution time reduction of 51–60% without compromising the prediction accuracies in the docking and virtual screening experiments. This improvement in time efficiency makes PSOVina a better choice of a docking tool in large-scale protein–ligand docking applications. Our work lays the foundation for the future development of swarm-based algorithms in molecular docking programs. PSOVina is freely available to non-commercial users at http://cbbio.cis.umac.mo .

Designing a Multi Echelon Flexible Logistics Network Using Co-Evolutionary Immune-Particle Swarm Optimization with Penetrated Hyper-Mutation (COIPSO-PHM)

2011 ◽  
Vol 110-116 ◽  
pp. 3713-3719
Author(s):  
N. C. Hiremath ◽  
Sadanand Sahu ◽  
Manoj Kumar Tiwari
Keyword(s):  
Particle Swarm Optimization ◽  
Real Life ◽  
Particle Swarm ◽  
Mixed Integer ◽  
Data Set ◽  
Swarm Optimization ◽  
Automotive Manufacturing ◽  
Logistics Network ◽  
Delivery Modes ◽  
Flexible Delivery

The strategic design and operation of outbound logistics network in an automotive manufacturing supply chain is directly related with the competitive strategy adopted by the firm. We discuss here an outbound logistics network model with four echelons and flexible delivery modes by incorporating cross-dock facility in the network. The paper aims to achieve a minimum total logistics cost for flexible delivery modes adopted in the network. The mathematical model is formulated as a mixed integer programming model and solved by using a hybrid algorithm named co-evolutionary immune-particle swarm optimization with penetrated hyper-mutation (COIPSO-PHM). The proposed model is combinatorial in nature owing to varying problem instances. The proposed solution methodology is tested on a sample data set mimicking the real life situation and the results are found to be satisfactory.

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