This document provides a review of optimization algorithms that have been used to solve job shop scheduling problems (JSSP). It first discusses how JSSPs are NP-hard combinatorial optimization problems that are difficult to solve exactly. It then reviews both traditional and non-traditional algorithms that have been applied to JSSPs, including mathematical programming approaches, heuristic construction methods, evolutionary algorithms like genetic algorithms, and local search methods like simulated annealing and tabu search. The document also discusses metaheuristic algorithms and provides a classification of different metaheuristics. Overall, the document aims to assess the various techniques that have been used to approach solving JSSPs.
This document proposes a new quantile-based fuzzy time series forecasting model. It begins by discussing how time series models have been used to predict things like enrollments, weather, accidents, and stock prices. It then provides background on quantile regression models and fuzzy time series forecasting. The proposed method develops a time variant quantile-based fuzzy time series forecasting approach based on predicting future data trends. The method converts statistical quantiles to fuzzy quantiles using membership functions and provides a fuzzy metric to calculate future values based on trend forecasts. The model is applied to TAIFEX forecasting and shows better performance than other models in terms of complexity and accuracy.
This document describes a hybrid approach combining scatter search and simulated annealing to solve multi-objective optimization problems. The approach generates an initial population of solutions using a diversification method. It then uses simulated annealing as an improvement method to enhance solutions. Solutions are added to a reference set based on quality and diversity. A subset generation method operates on the reference set to produce combined solutions. The combination method then transforms subsets into new combined solutions. The approach was tested on benchmark problems and found to perform well.
Bio-Inspired Computation: Success and Challenges of IJBICXin-She Yang
This document summarizes the success of the International Journal of Bio-Inspired Computation (IJBIC) over its first 5 years of publication from 2009 to 2014. It discusses how IJBIC has become a leading journal in the field by publishing high-quality research on new algorithms, improvements to existing algorithms, and applications. Some of the key areas of research published in IJBIC include particle swarm optimization, ant colony optimization, firefly algorithm, bat algorithm, and cuckoo search. While applications are diverse, the document highlights examples in engineering, data mining, and network optimization. It concludes by discussing ongoing challenges like developing more theoretical analysis, solving larger-scale problems, and achieving true intelligence in algorithms.
This document presents a new multivariate fuzzy time series forecasting method to predict car road accidents. The method uses four secondary factors (number killed, mortally wounded, died 30 days after accident, severely wounded, and lightly casualties) along with the main factor of total annual car accidents in Belgium from 1974 to 2004. The new method establishes fuzzy logical relationships between the factors to generate forecasts. Experimental results show the proposed method performs better than existing fuzzy time series forecasting approaches at predicting car accidents. Actuaries can use this kind of multivariate fuzzy time series analysis to help define insurance premiums and underwriting.
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
The document presents a new method for forecasting enrollments at the University of Alabama using fuzzy time series. The method first partitions the historical enrollment data universe of discourse into intervals based on frequency density. It then defines fuzzy sets for each interval and fuzzifies the enrollment data. Fuzzy logical relationships are established based on the fuzzified enrollments. The method is then used to forecast enrollments from 1972 to 1992 and results are compared to existing fuzzy time series forecasting methods using average forecasting error rate and mean square error. The proposed method aims to improve forecasting accuracy over existing approaches.
Kolawole John Adebayo, Luigi Di Caro and Guido Boella | A Supervised Keyphras...semanticsconference
This document outlines a supervised keyphrase extraction system that uses machine learning classifiers like random forests. It describes related work on keyphrase extraction, the proposed methodology using candidate selection and feature engineering, and experimental results on different datasets. The methodology involves selecting keyphrase candidates, extracting statistical, positional, lexical, semantic and similarity-based features, and training a classifier to predict keyphrases. Evaluation shows the approach achieves a precision of 32.1% and recall of 20.6% on one dataset. An ablation test analyzes contributions of different feature groups.
Concatenated decision paths classification for time series shapeletsijics
Time-series classification is widely used approach for classification. Recent development known as timeseries
shapelets, based on local patterns from the time-series, shows potential as highly predictive and
accurate method for data mining. On the other hand, the slow training time remains an acute problem of
this method. In recent years there was a significant improvement of training time performance, reducing
the training time in several orders of magnitude. Reducing the training time degrade the accuracy in
general. This work applies combined classifiers to achieve high accuracies, maintaining low training
times- in the range from several second to several minutes- for datasets from the popular UCR database.
The goal is achieved by training small 2,3-nodes decision trees and combining their decisions in pattern
that uniquely identifies incoming time-series.
This document proposes a new quantile-based fuzzy time series forecasting model. It begins by discussing how time series models have been used to predict things like enrollments, weather, accidents, and stock prices. It then provides background on quantile regression models and fuzzy time series forecasting. The proposed method develops a time variant quantile-based fuzzy time series forecasting approach based on predicting future data trends. The method converts statistical quantiles to fuzzy quantiles using membership functions and provides a fuzzy metric to calculate future values based on trend forecasts. The model is applied to TAIFEX forecasting and shows better performance than other models in terms of complexity and accuracy.
This document describes a hybrid approach combining scatter search and simulated annealing to solve multi-objective optimization problems. The approach generates an initial population of solutions using a diversification method. It then uses simulated annealing as an improvement method to enhance solutions. Solutions are added to a reference set based on quality and diversity. A subset generation method operates on the reference set to produce combined solutions. The combination method then transforms subsets into new combined solutions. The approach was tested on benchmark problems and found to perform well.
Bio-Inspired Computation: Success and Challenges of IJBICXin-She Yang
This document summarizes the success of the International Journal of Bio-Inspired Computation (IJBIC) over its first 5 years of publication from 2009 to 2014. It discusses how IJBIC has become a leading journal in the field by publishing high-quality research on new algorithms, improvements to existing algorithms, and applications. Some of the key areas of research published in IJBIC include particle swarm optimization, ant colony optimization, firefly algorithm, bat algorithm, and cuckoo search. While applications are diverse, the document highlights examples in engineering, data mining, and network optimization. It concludes by discussing ongoing challenges like developing more theoretical analysis, solving larger-scale problems, and achieving true intelligence in algorithms.
This document presents a new multivariate fuzzy time series forecasting method to predict car road accidents. The method uses four secondary factors (number killed, mortally wounded, died 30 days after accident, severely wounded, and lightly casualties) along with the main factor of total annual car accidents in Belgium from 1974 to 2004. The new method establishes fuzzy logical relationships between the factors to generate forecasts. Experimental results show the proposed method performs better than existing fuzzy time series forecasting approaches at predicting car accidents. Actuaries can use this kind of multivariate fuzzy time series analysis to help define insurance premiums and underwriting.
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
The document presents a new method for forecasting enrollments at the University of Alabama using fuzzy time series. The method first partitions the historical enrollment data universe of discourse into intervals based on frequency density. It then defines fuzzy sets for each interval and fuzzifies the enrollment data. Fuzzy logical relationships are established based on the fuzzified enrollments. The method is then used to forecast enrollments from 1972 to 1992 and results are compared to existing fuzzy time series forecasting methods using average forecasting error rate and mean square error. The proposed method aims to improve forecasting accuracy over existing approaches.
Kolawole John Adebayo, Luigi Di Caro and Guido Boella | A Supervised Keyphras...semanticsconference
This document outlines a supervised keyphrase extraction system that uses machine learning classifiers like random forests. It describes related work on keyphrase extraction, the proposed methodology using candidate selection and feature engineering, and experimental results on different datasets. The methodology involves selecting keyphrase candidates, extracting statistical, positional, lexical, semantic and similarity-based features, and training a classifier to predict keyphrases. Evaluation shows the approach achieves a precision of 32.1% and recall of 20.6% on one dataset. An ablation test analyzes contributions of different feature groups.
Concatenated decision paths classification for time series shapeletsijics
Time-series classification is widely used approach for classification. Recent development known as timeseries
shapelets, based on local patterns from the time-series, shows potential as highly predictive and
accurate method for data mining. On the other hand, the slow training time remains an acute problem of
this method. In recent years there was a significant improvement of training time performance, reducing
the training time in several orders of magnitude. Reducing the training time degrade the accuracy in
general. This work applies combined classifiers to achieve high accuracies, maintaining low training
times- in the range from several second to several minutes- for datasets from the popular UCR database.
The goal is achieved by training small 2,3-nodes decision trees and combining their decisions in pattern
that uniquely identifies incoming time-series.
The document assessed the effect of pH on soils used for agriculture in Kaduna, Nigeria. Soil samples were collected from 21 irrigated farmlands and one control site. Most samples had acidic pH levels, with the lowest at Nasarawa and Kawo (pH 5.8). Kurmin Mashi had the highest pH of 8.5. Statistical analysis found significant pH differences between sites. Sites with lowest pH included Kawo, Nasarawa, and the control, while the highest were Unguwan Dosa and Kurmin Mashi. The variation in soil pH was attributed to excessive fertilizer and chemical use as well as human activities like waste water irrigation.
This document discusses greywater recycling and reuse in high-rise buildings in Kuwait. It estimates that greywater (water from sinks, showers, etc. but not toilets) could provide 35-39% of total domestic water demand. Light greywater can often be reused for gardening after minimal treatment, while dark greywater from laundry/dishwashers requires simple treatment before uses like toilet flushing or gardening. Proper disinfection is important if human contact is possible. The document reviews greywater reuse systems and practices in other countries and regions facing water scarcity.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document summarizes a research paper about improving power gating structures used to reduce leakage power in circuits. It describes how operating the sleep transistor between 0V and the threshold voltage (Vth) during sleep mode, called an "intermediate sleep mode", can reduce both wake up time and short circuit current compared to a conventional power gating structure.
The virtual ground node voltage (Vgnd) decreases when using intermediate sleep mode due to the sleep transistor operating in the weak inversion region. This helps reduce wake up time by decreasing the amount of charge that needs to be discharged. However, it also slightly increases leakage current. Simulation results showed up to 20% reduction in wake up time and 45% reduction in short circuit current
This document describes the design and analysis of a surface acoustic wave (SAW) based microelectromechanical systems (MEMS) gas sensor for detecting volatile organic gases. The sensor uses interdigitated transducers on a lithium niobate piezoelectric substrate coated with a polyisobutylene sensing film. Finite element modeling was used to simulate the sensor's response to various gases. The simulations showed shifts in resonant frequency when exposed to different gases, allowing for gas detection via mass loading effects. Sensitivity analysis found the sensor responded most strongly to tetrachloroethene exposure due to its high absorbed partial density in the sensing film. The SAW sensor design could enable applications in chemical industry, environmental monitoring, and other
1. The document analytically describes Feigen's experimental results on plastic straining under combined loading using the synthetic theory of irrecoverable deformation.
2. Feigen observed the unexpected phenomenon of plastic "untwisting" where the accumulated torsional plastic strain decreased during torsional unloading, which cannot be explained by classical plasticity theories.
3. The synthetic theory, which models plastic deformation through the displacement of tangent planes on the yield surface, can readily model Feigen's experiment without additional assumptions. It represents both loading and deformation as vectors, making the analysis simpler than using tensors.
This document discusses harmonic analysis in power systems to reduce transmission losses and save energy. It begins with an introduction to harmonics and their effects in power systems. It then reviews previous literature on harmonic mitigation techniques, focusing on multi-pulse methods. The paper proposes using a 48-pulse Statcom (Static Synchronous Compensator) in a 132kV/33kV substation to reduce harmonics on the 33kV side. Simulation results show the 48-pulse Statcom reduces voltage total harmonic distortion and is estimated to provide approximately Rs. 2 lakh in annual energy savings. In conclusion, the 48-pulse Statcom can help improve power quality by reducing harmonic effects and increase transformer capacity.
This document summarizes a research paper that proposes a content-based image retrieval system using cascaded color and texture features. Color features are first extracted from images using statistical measures like mean, standard deviation, energy, entropy, skewness and kurtosis. Similarity to a query image is then measured using distance metrics. The top 150 most similar images are then analyzed to extract Haralick texture features. Similarity is again measured to retrieve the most relevant images. The paper finds that Canberra distance provides better retrieval results than other distance metrics like City Block and Minkowski.
1) The document presents a study on implementing a fuzzy logic controller for an AC generator to improve transient stability.
2) A single machine infinite bus power system model is used to evaluate the fuzzy logic controller and compare it to a conventional power system stabilizer.
3) The fuzzy logic controller uses the speed deviation and acceleration of the generator rotor as inputs, and computes stabilizing signals based on these variables and fuzzy membership functions to dampen mechanical oscillations.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document presents a method for updating the memory table of a distributed arithmetic (DA) adaptive FIR filter without compromising convergence speed or requiring additional memory resources. DA reduces computational workload by precomputing and storing filter coefficient sums in a lookup table (LUT). However, updating the table is challenging for adaptive filters. The proposed method exploits temporal locality and subexpression sharing to fully update the table with each new sample. It reduces computations and maintains fast convergence. The method enables efficient implementation of computationally-intensive discrete wavelet transforms using DA's parallel architecture and maximal LUT utilization. Simulation results show the discrete wavelet transform can be computed with high LUT utilization using this adaptive DA filter design.
This document summarizes various image segmentation techniques including region-based, edge-based, thresholding, feature-based clustering, and model-based segmentation. It provides details on each technique, including advantages and disadvantages. Region-based segmentation groups similar pixels into regions while edge-based segmentation detects boundaries between regions. Thresholding uses threshold values from histograms to segment images. Feature-based clustering groups pixels based on characteristics like intensity. Model-based segmentation uses probabilistic models like Markov random fields. The document concludes that the best technique depends on the application and image type, though thresholding is simplest computationally.
This document summarizes a study on producing glazed tiles using incinerated sewage sludge ash (ISSA) and clay. Three proportions of clay were replaced with ISSA to produce biscuit tiles, which were then glazed. Four colorants were used in glazes applied at different concentrations to the biscuit tiles. The study found that replacing clay with ISSA increased water absorption and decreased strength of biscuit tiles. However, applying glaze improved water absorption and strength. Different glaze colors and concentrations also affected properties like abrasion resistance and acid-alkali resistance. Bending strength was highest for tiles with red glaze. In general, glazing enhanced the properties and potential uses of tiles containing ISSA
International Journal of Engineering Research and Applications (IJERA) aims to cover the latest outstanding developments in the field of all Engineering Technologies & science.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
El documento habla sobre las complicaciones para llegar a lugares como el Castillo de Santa Cecilia debido a colapsos en las carreteras, pero que a pesar de los problemas la UME ayudó y la gente encontró diversión.
Caminho formoso o weblog como ferramenta de participação e emancipação no mui...UNEB
Este documento apresenta um trabalho de conclusão de curso sobre o uso do weblog como ferramenta de participação e emancipação social no município de Campo Formoso, Bahia. O trabalho descreve como o weblog pode ser usado para divulgar as riquezas naturais da região e promover o desenvolvimento local, ao mesmo tempo em que permite a participação dos usuários. O objetivo é representar o contexto cultural do município, especialmente suas grutas e sítios pré-históricos.
El documento repite frases de ánimo que alientan al lector a seguir intentándolo hasta tener éxito y felicitarse, y luego invita al lector a pasar al siguiente paso.
An application of genetic algorithms to time cost-quality trade-off in constr...Alexander Decker
This document summarizes a research paper that develops an optimization model using genetic algorithms to solve the time-cost-quality trade-off problem in construction projects. The model aims to find the minimum cost for a construction project to meet certain quality levels within a given time limit. It does this by considering different activity execution modes and using genetic algorithms to efficiently explore the large solution space. The document provides background on optimization problems and techniques, an overview of the time-cost-quality trade-off problem and prior related research, and describes the objectives and approach of the developed genetic algorithms model.
Resource Allocation Using Metaheuristic Searchcsandit
This document discusses using metaheuristic search techniques to solve resource allocation and scheduling problems that are common in software development projects. It evaluates the performance of three algorithms - simulated annealing, tabu search, and genetic algorithms - on test problems representative of resource constrained project scheduling problems (RCPSP). The experimental results found that all three metaheuristics can solve such problems effectively, with genetic algorithms performing slightly better overall than the other two techniques.
The document assessed the effect of pH on soils used for agriculture in Kaduna, Nigeria. Soil samples were collected from 21 irrigated farmlands and one control site. Most samples had acidic pH levels, with the lowest at Nasarawa and Kawo (pH 5.8). Kurmin Mashi had the highest pH of 8.5. Statistical analysis found significant pH differences between sites. Sites with lowest pH included Kawo, Nasarawa, and the control, while the highest were Unguwan Dosa and Kurmin Mashi. The variation in soil pH was attributed to excessive fertilizer and chemical use as well as human activities like waste water irrigation.
This document discusses greywater recycling and reuse in high-rise buildings in Kuwait. It estimates that greywater (water from sinks, showers, etc. but not toilets) could provide 35-39% of total domestic water demand. Light greywater can often be reused for gardening after minimal treatment, while dark greywater from laundry/dishwashers requires simple treatment before uses like toilet flushing or gardening. Proper disinfection is important if human contact is possible. The document reviews greywater reuse systems and practices in other countries and regions facing water scarcity.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document summarizes a research paper about improving power gating structures used to reduce leakage power in circuits. It describes how operating the sleep transistor between 0V and the threshold voltage (Vth) during sleep mode, called an "intermediate sleep mode", can reduce both wake up time and short circuit current compared to a conventional power gating structure.
The virtual ground node voltage (Vgnd) decreases when using intermediate sleep mode due to the sleep transistor operating in the weak inversion region. This helps reduce wake up time by decreasing the amount of charge that needs to be discharged. However, it also slightly increases leakage current. Simulation results showed up to 20% reduction in wake up time and 45% reduction in short circuit current
This document describes the design and analysis of a surface acoustic wave (SAW) based microelectromechanical systems (MEMS) gas sensor for detecting volatile organic gases. The sensor uses interdigitated transducers on a lithium niobate piezoelectric substrate coated with a polyisobutylene sensing film. Finite element modeling was used to simulate the sensor's response to various gases. The simulations showed shifts in resonant frequency when exposed to different gases, allowing for gas detection via mass loading effects. Sensitivity analysis found the sensor responded most strongly to tetrachloroethene exposure due to its high absorbed partial density in the sensing film. The SAW sensor design could enable applications in chemical industry, environmental monitoring, and other
1. The document analytically describes Feigen's experimental results on plastic straining under combined loading using the synthetic theory of irrecoverable deformation.
2. Feigen observed the unexpected phenomenon of plastic "untwisting" where the accumulated torsional plastic strain decreased during torsional unloading, which cannot be explained by classical plasticity theories.
3. The synthetic theory, which models plastic deformation through the displacement of tangent planes on the yield surface, can readily model Feigen's experiment without additional assumptions. It represents both loading and deformation as vectors, making the analysis simpler than using tensors.
This document discusses harmonic analysis in power systems to reduce transmission losses and save energy. It begins with an introduction to harmonics and their effects in power systems. It then reviews previous literature on harmonic mitigation techniques, focusing on multi-pulse methods. The paper proposes using a 48-pulse Statcom (Static Synchronous Compensator) in a 132kV/33kV substation to reduce harmonics on the 33kV side. Simulation results show the 48-pulse Statcom reduces voltage total harmonic distortion and is estimated to provide approximately Rs. 2 lakh in annual energy savings. In conclusion, the 48-pulse Statcom can help improve power quality by reducing harmonic effects and increase transformer capacity.
This document summarizes a research paper that proposes a content-based image retrieval system using cascaded color and texture features. Color features are first extracted from images using statistical measures like mean, standard deviation, energy, entropy, skewness and kurtosis. Similarity to a query image is then measured using distance metrics. The top 150 most similar images are then analyzed to extract Haralick texture features. Similarity is again measured to retrieve the most relevant images. The paper finds that Canberra distance provides better retrieval results than other distance metrics like City Block and Minkowski.
1) The document presents a study on implementing a fuzzy logic controller for an AC generator to improve transient stability.
2) A single machine infinite bus power system model is used to evaluate the fuzzy logic controller and compare it to a conventional power system stabilizer.
3) The fuzzy logic controller uses the speed deviation and acceleration of the generator rotor as inputs, and computes stabilizing signals based on these variables and fuzzy membership functions to dampen mechanical oscillations.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document presents a method for updating the memory table of a distributed arithmetic (DA) adaptive FIR filter without compromising convergence speed or requiring additional memory resources. DA reduces computational workload by precomputing and storing filter coefficient sums in a lookup table (LUT). However, updating the table is challenging for adaptive filters. The proposed method exploits temporal locality and subexpression sharing to fully update the table with each new sample. It reduces computations and maintains fast convergence. The method enables efficient implementation of computationally-intensive discrete wavelet transforms using DA's parallel architecture and maximal LUT utilization. Simulation results show the discrete wavelet transform can be computed with high LUT utilization using this adaptive DA filter design.
This document summarizes various image segmentation techniques including region-based, edge-based, thresholding, feature-based clustering, and model-based segmentation. It provides details on each technique, including advantages and disadvantages. Region-based segmentation groups similar pixels into regions while edge-based segmentation detects boundaries between regions. Thresholding uses threshold values from histograms to segment images. Feature-based clustering groups pixels based on characteristics like intensity. Model-based segmentation uses probabilistic models like Markov random fields. The document concludes that the best technique depends on the application and image type, though thresholding is simplest computationally.
This document summarizes a study on producing glazed tiles using incinerated sewage sludge ash (ISSA) and clay. Three proportions of clay were replaced with ISSA to produce biscuit tiles, which were then glazed. Four colorants were used in glazes applied at different concentrations to the biscuit tiles. The study found that replacing clay with ISSA increased water absorption and decreased strength of biscuit tiles. However, applying glaze improved water absorption and strength. Different glaze colors and concentrations also affected properties like abrasion resistance and acid-alkali resistance. Bending strength was highest for tiles with red glaze. In general, glazing enhanced the properties and potential uses of tiles containing ISSA
International Journal of Engineering Research and Applications (IJERA) aims to cover the latest outstanding developments in the field of all Engineering Technologies & science.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
El documento habla sobre las complicaciones para llegar a lugares como el Castillo de Santa Cecilia debido a colapsos en las carreteras, pero que a pesar de los problemas la UME ayudó y la gente encontró diversión.
Caminho formoso o weblog como ferramenta de participação e emancipação no mui...UNEB
Este documento apresenta um trabalho de conclusão de curso sobre o uso do weblog como ferramenta de participação e emancipação social no município de Campo Formoso, Bahia. O trabalho descreve como o weblog pode ser usado para divulgar as riquezas naturais da região e promover o desenvolvimento local, ao mesmo tempo em que permite a participação dos usuários. O objetivo é representar o contexto cultural do município, especialmente suas grutas e sítios pré-históricos.
El documento repite frases de ánimo que alientan al lector a seguir intentándolo hasta tener éxito y felicitarse, y luego invita al lector a pasar al siguiente paso.
An application of genetic algorithms to time cost-quality trade-off in constr...Alexander Decker
This document summarizes a research paper that develops an optimization model using genetic algorithms to solve the time-cost-quality trade-off problem in construction projects. The model aims to find the minimum cost for a construction project to meet certain quality levels within a given time limit. It does this by considering different activity execution modes and using genetic algorithms to efficiently explore the large solution space. The document provides background on optimization problems and techniques, an overview of the time-cost-quality trade-off problem and prior related research, and describes the objectives and approach of the developed genetic algorithms model.
Resource Allocation Using Metaheuristic Searchcsandit
This document discusses using metaheuristic search techniques to solve resource allocation and scheduling problems that are common in software development projects. It evaluates the performance of three algorithms - simulated annealing, tabu search, and genetic algorithms - on test problems representative of resource constrained project scheduling problems (RCPSP). The experimental results found that all three metaheuristics can solve such problems effectively, with genetic algorithms performing slightly better overall than the other two techniques.
A review on non traditional algorithms for job shop schedulingiaemedu
The document provides a review of non-traditional algorithms that have been used for job shop scheduling problems. It discusses how job shop scheduling is an NP-hard problem and researchers have focused on hybrid methods and metaheuristics. The review covers various techniques including tabu search, genetic algorithms, simulated annealing, ant colony optimization, and iterative local search methods. It also includes tables summarizing different approximation algorithms and literature on job shop scheduling using techniques like priority dispatch rules, insertion algorithms, artificial intelligence methods, and local search methods.
This document reviews applications of evolutionary multiobjective optimization (EMO) techniques in production research. It summarizes EMO applications in several areas of production research, including scheduling, production planning and control, cellular manufacturing, flexible manufacturing systems, and assembly-line optimization. The review finds that EMO techniques have been successfully applied to optimization problems in these areas and provide a number of non-dominated solutions. However, future research opportunities remain, such as improved integration of EMO with other metaheuristics and consideration of additional objectives.
Research Inventy : International Journal of Engineering and Scienceinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Facility planning and associated problems, a surveyAlexander Decker
This document discusses facility planning and layout problems. It begins by classifying different types of facility planning problems related to locating facilities and optimizing the distribution of people, materials, and machines. It then reviews various mathematical models and solution techniques that have been used to solve facility layout and location problems, including expert systems, fuzzy logic, and neural networks. The document also surveys recent research on facility layout problems and discusses different layout types (e.g. product, process, group layouts) and factors that affect layout performance.
An Application of Genetic Algorithm for Non-restricted Space and Pre-determin...drboon
The use of a genetic algorithm is presented to solve a facility layout problem in the situation where there is non-restricted space but the ratio of plant length and width is pre-determined. A two-leveled chromosome is constructed. Six rules are established to translate the chromosome to facility design. An approach of solving a facility layout problem is proposed. A numerical example is employed to illustrate the approach.
A learning based transportation oriented simulation systemWolfsbane_John
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1. K.Mallikarjuna et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 3( Version 4), March 2014, pp.11-19
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A Review On Job Shop Scheduling Using Non-Conventional
Optimization Algorithm
K.Mallikarjuna*, Venkatesh.G**, Somanath.B***
*(Ass..Prof, Dept of M E,Ballari Institute of Tech and Management, Bellary, Karnataka, India,)
** (Dept of M E,Ballari Institute of Tech and Management, Bellary, Karnataka, India)
*** (Dept of M E,Ballari Institute of Tech and Management, Bellary, Karnataka, India)
ABSTRACT
A great deal of research has been focused on solving job shop scheduling problem (∫J), over the last four
decades, resulting in a wide variety of approaches. Recently much effort has been concentrated on hybrid
methods to solve ∫J, as a single technique cannot solve this stubborn problem. As a result much effort has
recently been concentrated on techniques that lead to combinatorial optimization methods and a meta-strategy
which guides the search out of local optima. In this paper, authors seek to assess the work done in the job-shop
domain by providing a review of many of the techniques used. It is established that Non- conventional
optimization methods should be considered complementary rather than competitive. In addition, this work
suggests guide-lines on features that should incorporated to create a good ∫J system. Finally, the possible
direction for future work is highlighted so that current barriers within ∫J may be surmounted as researchers
approach in the 21st
century.
Keywords - Exact algorithm, job shop, non conventional algorithms, scheduling, review
I. Introduction
Problems encountered in fields like
scheduling, assignment, vehicle routing are mostly
NP hard. These problems need efficient solution
procedures. If confronted with an NP-hard problem,
one may have three ways to go: one chooses to
apply an enumerative method that yields an
optimum solution, or apply an approximation
algorithm that runs in polynomial time, or one
resorts to some type of heuristic technique without
any a priori guarantee for quality of solution and
time of computing (Aarts & Lenstra, 2003).
Research in scheduling theory has evolved over the
past four decades and has been the subject of much
significant literature with techniques ranging from
unrefined dispatching rules to highly sophisticated
parallel branch and bound algorithms and bottleneck
based heuristics. Not surprisingly, approaches have
been formulated from a diverse spectrum of
researchers ranging from management scientists to
production workers. However with the advent of
new methodologies, such as neural networks and
evolutionary computation, researchers from fields
such as biology, genetics and neurophysiology have
also become regular contributors to scheduling
theory emphasising the multidisciplinary nature of
this field.
One of the most popular models in scheduling
theory is that of the job-shop, as it is considered to
be a good representation of the general domain and
has earned a reputation for being notoriously
difficult to solve. It is probably the most studied and
well developed model in deterministic scheduling
theory, serving as a comparative test-bed for
different solution techniques,old and new and as it is
also strongly motivated by practical requirements it
is clearly worth understanding.
The evolution of optimization techniques has
been mainly attributed to the increase in complexity
of problems encountered two branches of heuristics
exist: constructive and improvement (Onwubolu and
Mutingi 1999). Constructive methods are usually
problem dependent (Cambell et al. 1970, Nawaz et
al. 1983). Improvement methods are those involving
population-based heuristics which usually follow a
naturally occurring paradigm. Many approximate
methods have been developed to overcome the
limitations of exact enumeration techniques. These
approximate approaches include genetic algorithms
(GA), tabu search (TS), differential evolution
algorithm (DE) neural networks (NN), simulated
annealing (SA) and particle swamp optimization
(PSO).
Meta-heuristic techniques are the most recent
development in approximate search methods for
solving complex optimisation problems (Osman and
Kelly 1996a). ∫J meta-heuristics are based on the
neighbourhood strategies developed by Grabowski
et al. (1986, 1988), Matsuo et al. (1988), Van
Laarhooven et al. (1992) and Nowicki and
Smutnicki (1996). Vaessens et al. (1995) present a
template that captures most of the schemes proposed
and they suggest that multi-level local search
methods merit more investigation. Pirlot (1996)
RESEARCH ARTICLE OPEN ACCESS
2. K.Mallikarjuna et al Int. Journal of Engineering Research and Applications www.ijera.com
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www.ijera.com 12 | P a g e
indicates that few serious comparative studies have
been performed with regard to meta-solvers such as
Simulated Annealing (SA), Tabu Search (TS) and
Genetic Algorithms (GAs) and from his analysis
GAs appear to be the weakest of these three
methods both empirically and analytically. In a
recent work Mattfeld et al. (1998) analyse the
structure of the fitness landscape of ∫J with respect
to how it appears for an adaptive search heuristic.
They indicate that adaptive search heuristics are
suitable search techniques for ∫J, all that is required
is an effective navigation tool.
II. Objectives of scheduling
The scheduling is made to meet specific
objectives. The objectives are decided upon the
situation, market demands, company demands and
the customer’s satisfaction. There are two types for
the scheduling objectives:
Minimize the make Span for different
feasibility of job sequence.
Minimize the waiting time of job
The objectives considered under the minimizing the
makespan are,
(a) Minimize machine idle time
(b) Minimize the in process inventory costs
(c) Finish each job as soon as possible
The objectives considered under the minimizing the
waiting time are,
(a) Minimize the cost due to not meeting the due
dates
(b) Minimize the total tardiness
(c) Minimize the number of late jobs
Fig 1: Different algorithms for JSSP
III. Literature review on JSSP
scheduling
Many researchers have been focusing on
scheduling during the last few decades. A number of
approaches have been developed and employed for
solving various problems of Job Shop Scheduling
considering various objectives. The
following table discuss the Review on Job Shop
Scheduling using non traditional optimization
techniques.
Job shop scheduling problem
Traditional methods
methods
Non traditional methods
mmmethods
Exact methods
methods
Approximation methods
m methods methods
1.Constructive
Methods:
Priority dispatch
rules.
Composite
dispatching rules.
2. Evolutionary
Methods:
Genetic
Algorithm(GA).
Particle Swarm
Optimization
(PSO).
Differential
Evolution
Algorithm(DE).
3. Local Search
Techniques:
Ants Colony
Optimization
(ACO).
Simulated
Annealing(SA).
Tabu Search(TS).
1. Mathematical
programming ;
Linear
Programming
Integer
programming
Dynamic
Programming
Network
Branch and bound
2. Enumerate method;
Lagrangian
Relaxation
3. Efficient Methods
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Table.1: Review on Job Shop Scheduling using Non Traditional Optimization Techniques
SI.NO METHOD AUTHOR 1 AUTHOR 2
1. Tabu search
algorithm
Fred Glover (1977, 1986) Rafael Martí (2004,2006)
E.Nowicki (2005) C.Smutnicki (2005)
Dipak Laha (2008) Uday Kumar C (2008)
Sumanta Basu (2008) Diptesh Ghosh (2008)
Wassim Jaziri
2. Differential
evolution
algorithm
Warisa Wisittipanich (2011) Voratas Kachitvichyanukul(2011)
Donald Davendra Godfrey Onwubolu
Vanita G.Tonge (2012) Prof.P.S.Kulkarni (2012)
Zuzana Cickova (2010) Stanislav Stevo (2010)
3. Genetic
algorithm
Goldberg D.E (1989)
Hameshbabu Nanvala
Dirk C. Mattfeld (2004) Christian Bierwirth (2004)
Jason Chao-Hsien Pan (2009) Han-Chiang Huang (2009)
4. Simulated
Annealing
Reeves C.R (1993)
T.Yamada (1995) R.Nakano (1996)
Aarts, B. J. M (1996)
Kolonko M (1998)
Peter J.M Emile H.L
5. Particle swarm
optimization
Tsung-Lieh Lin
D.Y.Sha (2006)
Deming Lei (2008) Zhiming Wu(2005)
Hsing-Hung Lin (2009) Weijun Xia(2005)
Guohui zhang (2009) Xingsheng Gu(2008)
6. Ant colony
optimization
Colorni et al (1995,1996)
S.Goss, S. Aron J.-L. Deneubourg et J.-M. Pasteels
Colorni, M. Dorigo et V.Maniezzo (1991)
Betul Yagmahan
7. Artificial
immune system
U.Aickelin E Burke
Bagheri Zandieh
Mahdi Mobini Zahra Mobini
8. Sheep Flock
Heredity
Algorithm
S.Gobinath Prof.C.Arumugam
Koichi Nara Hyunchul Kim
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IV. Scheduling techniques
There are number of optimization and
approximation techniques are used for scheduling of
job shop scheduling problem. The techniques are
generally,
Conventional techniques Conventional
techniques are also called as optimization
techniques. These techniques are slow and
guarantee of global convergence as long as
problems are small. Mathematical programming
(Linear Programming, Integer programming,
Goal Programming, Dynamic Programming,
Transportation, Network, Branch-and-Bound,
Cutting Plane / Column Generation Method,
Mixed Integer Linear programming, Surrogate
Duality), Enumerate Procedure Decomposition
(Lagrangian Relaxation) and Efficient Methods.
Non conventional techniques Non conventional
techniques are also called as approximation
methods. These methods are very fast but they
do not guarantee for optimal solutions.
Constructive Methods(priority dispatch rules,
composite dispatching rules), Insertion
Algorithms (Bottleneck based heuristics,
Shifting Bottleneck Procedure(SBP)),
Evolutionary Programs(Genetic Algorithm,
Particle Swarm Optimization), Local Search
Techniques(Ants Colony Optimization,
Simulated Annealing, adaptive Search, Tabu
Search, problem Space Methods like Problem
& Heuristic Space and GRASP), Iterative
Methods((Artificial Intelligence Techniques,
Expert Systems, Artificial Neural Network),
Heuristics Procedure, Beam-Search, and Hybrid
Techniques.
V. Meta-heuristic procedures
It is possible to classify meta-heuristics in
many ways. Different view points differentiate the
classifications. Blum and Roli (2003) classified
meta-heuristics based on their diverse aspects:
nature-inspired (e.g. GA, ACO) vs. non-nature
inspired (e.g. TS); population-based (e.g. GA) vs.
single point search (also called trajectory methods,
e.g. TS); dynamic (i.e. guided local search) vs. static
objective function; one vs. various neighborhood
functions (i.e. variable neighborhood search);
memory usage vs. memory-less methods. A
classification of meta-heuristics is given in
the Table 5.1 in which “A” represents the adaptive
memory property, “M” represents the memory-less
property, “N” represents employing a special
neighborhood, “S” represents random sampling, “1”
represents iterating-based approach, and “P”
represents a population-based approach. Population
based approaches, also referred to as evolutionary
methods, manipulate a set of solutions rather than
one solution at a stage.
Meta-heuristic Classification
Tabu-Search A/N/1-P
Simulated Annealing M/S-N/1
GA M/S-N/P
ACO M/S-N/P
GRASP M/S-N/1
PSO M/S-N/P
Table 5.1 - Classification of Meta-heuristics
(modified from Glover, 1997)
Almost all meta-heuristic procedures require a
representation of solutions, a cost function, a
neighborhood function, an efficient method of
exploring a neighborhood, all of which can be
obtained easily for most problems (Aarts & Lenstra,
2003). It is important to mention that a successful
implementation of a meta-heuristic procedure
depends on how well it is modified for the problem
instance at hand.
5.1 Tabu Search (TS)
TS can be considered as a generalization of iterative
improvements like SA. It is regarded as an adaptive
procedure having the ability to use many methods,
such as linear programming algorithms and
specialized heuristics, which it guides to overcome
the limitations of local optimality (Glover, 1989).
TS applies restrictions to guide the search to
diverse regions. These restrictions are in relation to
memory structures that can be thought of as
intelligent qualifications. Intelligence needs adaptive
memory and responsive exploration (Glover &
Laguna, 1997). For example, while climbing a
mountain one remembers (adaptive memory)
attributes of paths s/he has traveled and makes
strategic choices (responsive exploration) on the
way to peak or descent. TS also uses responsive
exploration because a bad strategic decision may
give more information than a good random one to
come up with quality solutions. TS has memory
property that distinguishes it from other search
designs. It has adaptive memory that is also different
from rigid memory used by branch and bound
strategies. Memory in TS has four dimensions:
quality, recency, frequency, and influence. A basic
tabu search algorithm for a maximization problem is
illustrated in Figure 5.1
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Figure 5.1 – A basic tabu search algorithm
where T is a tabu list and N(s) is the set of
neighbourhood solutions. A generic flowchart of TS
algorithm can be given as follows in Figure 5.2:
Figure 5.2 - Generic flowchart of TS algorithm
(Zhang et al. 2007)
5.2 Simulated Annealing (SA)
SA is a randomized algorithm that tries to
avoid being trapped in local optimum solution by
assigning probabilities to deteriorating moves. In SA
a threshold value is chosen. The increase in cost of
two moves is compared with that threshold value. If
the difference is less than the threshold value, then
the new solution is chosen. A high threshold value
may be chosen to explore various parts of solution
space while a low threshold value may be chosen to
guide the search towards good solution values. The
threshold value is redefined in each iteration to
enable both diversification and intensification.
Starting with high threshold values and then
decreasing the value may result in finding good
algorithm Tabu search
begin
T:= [ ];
s:=initial solution;
s*:=s
repeat
find the best admissible s’ є N(s);
if f(s’) > f(s*) then s*:=s’
s:=s’;
update tabu list T;
until stopping criterion:
end;
Generate an initial solution, store it as the current seed
and the best solution, set parameters and clear the
tabu list
Is stop
criterion?
Output
optimization
result
Generate neighbours of the current seed solution by a
neighbourhood structure
Is the
aspiratio
n
criterio
n
satisfie
d?
Store the aspiration
solution as the new
seed and the best
solution.
The “best” neighbour which is not tabu is selected as
new seed
Update the tabu list
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solutions. SA uses threshold as a random variable.
In other words SA uses expected value of threshold.
In a maximization problem acceptance probability
of a solution is defined as follows:
1 f(s')
≥ f(s)
IP s' ═ exp f(s') - f(s) f(s') < f(s)
Ck
where ck is the temperature that gives the expected
value of the threshold. A generic SA algorithm for a
maximization problem is given in Figure 5.3 below:
Figure 5.3 – A simulated annealing algorithm
The cooling schedule is important in SA.
Temperature values (Ck) are specified according to
the cooling schedule. In general, the cooling
schedule’s temperature is kept constant for a number
of iterations before it is decreased.
5.3 Genetic Algorithms (GAs)
GAs are used to create new generation of
solutions among trial solutions in a population.
In a GA, a “fitness function” is utilized and hence a
quantitative study is performed. The fitness function
evaluates candidate solutions, determines their
weaknesses and deletes them if they are not
expected ones. After this step, the reproduction
among the candidates occurs and new solutions are
obtained and compared using the fitness function
again. The same process keeps repeating for number
of generations.
With the above description in mind,
Figure 5.4 shows a general scheme of using GA for
minimization problems. The initial step is to
determine P0, the first population of solutions. Using
the fitness function, improvements are made to the
initial population of solutions. Afterwards, the
algorithm enters into a loop in which crossover and
mutation operations are performed until a stopping
criterion is met. A typical stopping criterion is to
perform all the steps for a fixed number of
generations.
Begin
P0 := set of N solutions;
/*Mutation*/
replace each s є P0 by Iterative_Improvement(s);
t :=1;
repeat
Select Pt ⊆ Pt-1;
/* Recombination */
extend Pt by adding offspring;
/* Mutation */
replace each s є Pt by Iterative_Improvement(s) ;
t :=t+1;
until stop criterion;
end;
Figure 5.4 - A genetic local search algorithm for a
minimization problem (Michiels et.al.,2003)
GAs have many application areas in Aerospace
Engineering, Systems Engineering, Materials
Engineering, Routing, Scheduling, Robotics,
Biology, Chemistry, etc.
5.4 Ant Colony Optimization (ACO)
ACO is another branch of meta-heuristics
that is used to solve complex problems in a
reasonable amount of time. In Figure 5.5, a general
type of ant colony optimization is given.
algorithm Simulated annealing
begin
s:= initial solution
k:=1;
repeat
generate an s’ є N(s);
if f(s’) ≥ f(s) then s:=s’
else
if exp f(s')-f(s) > random[0,1)
Ck then s:=s’;
k:=k+1;
until stop criterion:
end;
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procedure ACO_Meta-heuristic
while (not_termination)
generate Solutions ()
pheromone Update ()
daemon Actions ()
end while
end procedure
Figure 5.5 - A general ant colony optimization
procedure
As seen from the general algorithm, a set of
initial solutions should be generated in each turn of
the while loop, then the pheromone levels should be
updated and actions should be taken. When the
termination criterion is reached, the procedure ends.
This algorithm can be modified to fit the needs of
the specific problem.
5.5 Greedy Randomized Adaptive Search
Procedure (GRASP)
GRASP is another meta-heuristic method
used for solving combinatorial optimization
problems. Figure 5.6 demonrates how GRASP
works for a minimization problem.
Figure 5.6 - High level pseudo-code for GRASP
This algorithm is composed of two main
phases: a construction phase and a local search
phase. In the construction phase, there is a greedy
function which maintains the rankings of partial
solutions. This step is very important because it
affects the time efficiency of the algorithm. After
ranking the partial solutions, some of the best ones
are stored in a restricted candidate list (RCL). In the
local search phase, as shown in Figure 5.6, a
comparison is done to differentiate the quality of
solutions. The algorithm terminates after a fixed
number of iterations.
Fogel & Michalewicz (2000) provide a GRASP
application to solve a TSP with 70 cities. They
randomly select a city to begin the tour and then add
the other 69 cities one at a time to the tour. After
constructing an initial solution, they run the
algorithm and evaluate 2415 different solutions. In
such big TSP problems, GRASP seems to find good
solutions in reasonable amounts of time.
5.6 Particle Swarm Optimization (PSO)
PSO is inspired from the collective
behaviors of animals. In this section, we will present
a sample PSO algorithm to demonstrate how it
works and talk about the kinds of problems it is
applied to.
There are two key definitions in using PSO
algorithms that have been defined in Section 4
earlier: position and velocity. The position and
velocity of particle i at time t are represented by xi
(t) and vi (t) respectively. The position and velocity
of a particle changes based on the following
equations:
xi (t) = xi (t − 1) + vi (t − 1) (1)
equivalently, xi (t) can be represented as a function
of the previous position, previous velocity, pi, and
pg where, pi is the local best position of particle i,
and pg is the neighborhood best position.
xi (t) = f (xi (t − 1), vi (t − 1), pi, pg) (2)
vi (t) = vi (t − 1) + Φ1 (p i − xi (t − 1)) + Φ2 (pg − x i
(t − 1))
(3)
Equation (8) shows the velocity of particle i.
Where, Φ1 and Φ2 are randomly chosen parameters.
Φ1 represents the individual experience and
Φ2 represents the social
communication. In figure 5.7 the PSO algorithm is
given for n particles:
procedure GRASP
while (termination condition not met) do
S Construct Greedy Randomized
Solution
ˆS Local Search(S)
If f (ˆS) < f (Sbest) then
Sbest ˆS
end-if
end-while
return Sbest
end-procedure
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Figure 5.7 - The PSO algorithm for n particles
(Dréo
et al., 2006)
As seen in Figure 5.7, this algorithm can be used
in multiple dimensions. This PSO algorithm can
applied to many problems in the real life such as the
TSP, the vehicle routing problem, the flow shop
scheduling problem, etc. However, it is more
commonly used in training of artificial neural
networks.
VI. CONCLUSIONS
Since job shop scheduling problems fall
into the class of NP-complete problems, they are
among the most difficult to formulate and solve.
Some optimization problems (including various
combinatorial optimization problems) are
sufficiently complex that it may not be possible to
solve for an optimal solution with the kinds of exact
algorithms. In such cases, heuristic methods are
commonly used to search for a good (but not
necessarily optimal) feasible solution. Several
metaheuristics are available that provide a general
structure and strategy guidelines for designing a
specific heuristic method to fit a particular problem.
A key feature of these metaheuristics procedures is
their ability to escape from local optima and
perform a robust search of a feasible region
This paper introduces the most prominent types
of non-conventional type algorithms or
meteheuristics.Tabu search moves from current trial
solution to the best neighboring trial solution at each
iteration, much like a local improvement procedure,
except that it allows a non improving move when an
improving move is not available. It then
incorporates short-term memory of the past search
to encourage moiving toward new parts of the
feasible region rather than cycling back to
previously considered solutions. In addition, it may
employ intensification and diversification strategies
based on long-term memory to focus the search on
promising continuious.
The following are the advantages of non-traditional
techniques over the traditional techniques:
The non-traditional techniques yield a global
optimal solution.
The techniques use a population of points
during search.
Initial populations are generated randomly
which enable to explore the search space.
The techniques efficiently explore the new
combinations with available knowledge to find
a new generation.
The objective functions are used rather than
their derivatives.
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For i = 1 to n :
If F(xi) > F(pi) then :
For d = 1, . . . , D :
pid = kid // pid is thus the best
found individual
end d
end if
g = i
For j =index of the neighbours :
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g = j // g is the best individual
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vid(t) = vid(t − 1) + Φ1 (pid − xid
(t − 1)) + Φ2 (pgd −xid (t − 1))
vid є (−Vmax_ + Vmax)
vid(t) = xid(t − 1) + vid(t)
end d
end i
end
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