21 research outputs found
A Social- And Knowledge-Based Coalition Formation Using Modified Combinatorial Particle Swarm Optimization
The thesis main objective is to develop a new framework for social- and knowledge-based coalition formation (SKCF). The related sub-objectives are: 1) to define coalition and social factors to form a coalition formation model, 2) to develop a knowledge representation scheme to store knowledge of formed coalitions, and 3) to develop an effective algorithm to optimize the coalition which can also be treated as a clustering problem. In order to realize these objectives, the coalition factors are compiled from existing coalition formation work, whereas social factors are chosen to satisfy the coalition’s payoff to address the selfish agent approach
DISCRETE PARTICLE SWARM OPTIMIZATION FOR THE ORIENTEERING PROBLEM
Discrete particle swarm optimization (DPSO) is gaining popularity in the area of combinatorial optimization in the recent past due to its simplicity in coding and consistency in performance. A DPSO algorithm has been developed for orienteering problem (OP) which has been shown to have many practical applications. It uses reduced variable neighborhood search as a local search tool. The DPSO algorithm was compared with ten heuristic models from the literature using benchmark problems. The results show that the DPSO algorithm is a robust algorithm that can optimally solve the well known OP test problems
Εφαρμογές του αλγόριθμου «Βελτιστοποίησης Σμήνους Σωματιδίων» σε προβλήματα ταξινόμησης και ομαδοποίησης
Στην εργασία μελετάται και αναλύεται η χρήση του αλγόριθμου
βελτιστοποίησης σμήνους σωματιδίων (Particle Swarm Optimization - PSO)
σε προβλήματα ταξινομησης και ομαδοποίησης (επιβλεπόμενης και μη-
επιβλεπόμενης μάθησης).
Ο αλγόριθμος PSO μπορεί να χρησιμοποιηθεί σε ένα πρόβλημα ταξινόμησης
ή ομαδοποίησης μετατρέποντάς το σε πρόβλημα βελτιστοποίησης των
θέσεων των κέντρων των ομάδων. Το κύριο πλεονέκτημά του είναι ότι δεν
εξαρτάται από τις τιμές αρχικοποίησης και πραγματοποιεί αναζήτηση σε
ευρύτερο χώρο σε σχέση με άλλους αλγορίθμους, με αποτέλεσμα να μην
καταλήγει εύκολα σε τοπικά βέλτιστες λύσεις.
Μελετώνται επίσης παραλλαγές του PSO οι οποίες εφαρμόζονται για να
ξεπεραστούν κάποια προβλήματα ή για να αυξηθεί η αποτελεσματικότητά του.
Προτείνεται μια μέθοδος βελτίωσης της αποτελεσματικότητας του PSO με τη χρήση
γραμμικής αύξησης της επιρροής της προσωπικής παραμέτρου και ταυτόχρονα
γραμμικής μείωσης της επιρροής της κοινωνικής παραμέτρου. Τέλος, γίνεται
πειραματική αξιολόγησή του αλγορίθμου, με και χωρίς την προτεινόμενη βελτίωση,
συγκρίνοντάς τον με άλλους αλγορίθμους ταξινόμησης και ομαδοποίησης δεδομένων.In this thesis we discuss the application of the “Particle Swarm Optimization
Algorithm” on clustering and classification problems (supervised and
unsupervised learning)
The PSO algorithm can be used to find the solution to a classification problem
by transforming it to a typical optimization problem of the positions of the
centers of the clusters.
Its main advantage is that it does not depend on the initialization values and
performs a broader search in the search space in comparison to other
algorithms. Thus, it is more difficult to be trapped in local optima.
We also present some PSO variants which are used in order to maximize its
performance or to overcome particular problems.
Finally we show experimental results and we compare the PSO algorithm to
other classification algorithms
A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications
Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms
Hybrid optimization for k-means clustering learning enhancement
In recent years, combinational optimization issues are introduced as critical problems in clustering algorithms to partition data in a way that optimizes the performance of clustering. K-means algorithm is one of the famous and more popular clustering algorithms which can be simply implemented and it can easily solve the optimization issue with less extra information. But the problems associated with Kmeans algorithm are high error rate, high intra cluster distance and low accuracy. In this regard, researchers have worked to improve the problems computationally, creating efficient solutions that lead to better data analysis through the K-means clustering algorithm. The aim of this study is to improve the accuracy of the Kmeans algorithm using hybrid and meta-heuristic methods. To this end, a metaheuristic approach was proposed for the hybridization of K-means algorithm scheme. It obtained better results by developing a hybrid Genetic Algorithm-K-means (GAK- means) and a hybrid Partial Swarm Optimization-K-means (PSO-K-means) method. Finally, the meta-heuristic of Genetic Algorithm-Partial Swarm Optimization (GAPSO) and Partial Swarm Optimization-Genetic Algorithm (PSOGA) through the K-means algorithm were proposed. The study adopted a methodological approach to achieve the goal in three phases. First, it developed a hybrid GA-based K-means algorithm through a new crossover algorithm based on the range of attributes in order to decrease the number of errors and increase the accuracy rate. Then, a hybrid PSO-based K-means algorithm was mooted by a new calculation function based on the range of domain for decreasing intra-cluster distance and increasing the accuracy rate. Eventually, two meta-heuristic algorithms namely GAPSO-K-means and PSOGA-K-means algorithms were introduced by combining the proposed algorithms to increase the number of correct answers and improve the accuracy rate. The approach was evaluated using six integer standard data sets provided by the University of California Irvine (UCI). Findings confirmed that the hybrid optimization approach enhanced the performance of K-means clustering algorithm. Although both GA-K-means and PSO-K-means improved the result of K-means algorithm, GAPSO-K-means and PSOGA-K-means meta-heuristic algorithms outperformed the hybrid approaches. PSOGA-K-means resulted in 5%- 10% more accuracy for all data sets in comparison with other methods. The approach adopted in this study successfully increased the accuracy rate of the clustering analysis and decreased its error rate and intra-cluster distance
Precipitation Sensor Network Optimal Design Using Time-Space Varying Correlation Structure
Design of optimal precipitation sensor networks is a common topic in hydrological literature, however this is still an open problem due to lack of understanding of some spatially variable processes, and assumptions that often cannot be verified. Among these assumptions lies the homoscedasticity of precipitation fields, common in hydrological practice. To overcome this, it is proposed a local intensity-variant covariance structure, which in the broad extent, provides a fully updated correlation structure as long as new data are coming into the system. These considerations of intensity-variant correlation structure will be tested in the design of a precipitation sensor network for a case study, improving the estimation of precipitation fields, and thus, reducing the input uncertainty in hydrological models, especially in the scope of rainfall-runoff models
Clustering analysis using Swarm Intelligence
This thesis is concerned with the application of the swarm intelligence methods in
clustering analysis of datasets. The main objectives of the thesis are
∙ Take the advantage of a novel evolutionary algorithm, called artificial bee colony,
to improve the capability of K-means in finding global optimum clusters in
nonlinear partitional clustering problems.
∙ Consider partitional clustering as an optimization problem and an improved antbased
algorithm, named Opposition-Based API (after the name of Pachycondyla
APIcalis ants), to automatic grouping of large unlabeled datasets.
∙ Define partitional clustering as a multiobjective optimization problem. The
aim is to obtain well-separated, connected, and compact clusters and for this
purpose, two objective functions have been defined based on the concepts of
data connectivity and cohesion. These functions are the core of an efficient
multiobjective particle swarm optimization algorithm, which has been devised
for and applied to automatic grouping of large unlabeled datasets.
For that purpose, this thesis is divided is five main parts:
∙ The first part, including Chapter 1, aims at introducing state of the art of swarm
intelligence based clustering methods.
∙ The second part, including Chapter 2, consists in clustering analysis with combination
of artificial bee colony algorithm and K-means technique.
∙ The third part, including Chapter 3, consists in a presentation of clustering
analysis using opposition-based API algorithm.
∙ The fourth part, including Chapter 4, consists in multiobjective clustering analysis
using particle swarm optimization.
∙ Finally, the fifth part, including Chapter 5, concludes the thesis and addresses
the future directions and the open issues of this research
Water filtration by using apple and banana peels as activated carbon
Water filter is an important devices for reducing the contaminants in raw water. Activated from charcoal is used to absorb the contaminants. Fruit peels are some of the suitable alternative carbon to substitute the charcoal. Determining the role of fruit peels which were apple and banana peels powder as activated carbon in water filter is the main goal. Drying and blending the peels till they become powder is the way to allow them to absorb the contaminants. Comparing the results for raw water before and after filtering is the observation. After filtering the raw water, the reading for pH was 6.8 which is in normal pH and turbidity reading recorded was 658 NTU. As for the colour, the water becomes more clear compared to the raw water. This study has found that fruit peels such as banana and apple are an effective substitute to charcoal as natural absorbent