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.

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.

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 Discrete Particle Swarm Optimization Approach to Optimize the Assembly Sequence of Mechanical Product

2012 ◽  
Vol 490-495 ◽  
pp. 203-207
Author(s):  
Zhong Bo Zhang ◽  
Chuan Yong Huang
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Assembly Sequence Planning ◽  
Optimization Approach ◽  
Assembly Sequence ◽  
Swarm Optimization ◽  
Discrete Pso ◽  
The Difference ◽  
Assembly Tool

The aim of assembly sequence planning (ASP) is to achieve the best assembly sequence which assembly cost and time used is less. The geometrical feasibility of an assembly sequence is validated by the interference matrix of the product. The number of assembly tool changes and the number of assembly operation type changes are considered in the fitness function. To establish the mapping relation between ASP and particle swarm optimization (PSO) approach, some definitions of position, velocity and operator of particles are proposed. The difference of the proposed discrete PSO (DPSO) algorithm with the other algorithm is the emphasis on the geometrical feasibility of the assembly sequence. The geometrical feasibility is verified at the first and the every iteration. The performance and feasibility of the proposed algorithm is verified via a simplified engine assembly case.

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%.

scholarly journals Estimation of Fuzzy Parameters in the Linear Muskingum Model with the Aid of Particle Swarm Optimization

2021 ◽  
Vol 13 (13) ◽  
pp. 7152
Author(s):  
Mike Spiliotis ◽  
Alvaro Sordo-Ward ◽  
Luis Garrote
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Flood Routing ◽  
Performance Criteria ◽  
Extension Principle ◽  
Swarm Optimization ◽  
Muskingum Model ◽  
Muskingum Method ◽  
Fuzzy Parameters

The Muskingum method is one of the widely used methods for lumped flood routing in natural rivers. Calibration of its parameters remains an active challenge for the researchers. The task has been mostly addressed by using crisp numbers, but fuzzy seems a reasonable alternative to account for parameter uncertainty. In this work, a fuzzy Muskingum model is proposed where the assessment of the outflow as a fuzzy quantity is based on the crisp linear Muskingum method but with fuzzy parameters as inputs. This calculation can be achieved based on the extension principle of the fuzzy sets and logic. The critical point is the calibration of the proposed fuzzy extension of the Muskingum method. Due to complexity of the model, the particle swarm optimization (PSO) method is used to enable the use of a simulation process for each possible solution that composes the swarm. A weighted sum of several performance criteria is used as the fitness function of the PSO. The function accounts for the inclusive constraints (the property that the data must be included within the produced fuzzy band) and for the magnitude of the fuzzy band, since large uncertainty may render the model non-functional. Four case studies from the references are used to benchmark the proposed method, including smooth, double, and non-smooth data and a complex, real case study that shows the advantages of the approach. The use of fuzzy parameters is closer to the uncertain nature of the problem. The new methodology increases the reliability of the prediction. Furthermore, the produced fuzzy band can include, to a significant degree, the observed data and the output of the existent crisp methodologies even if they include more complex assumptions.

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