List of Articles Genetic Algorithm

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  1 - Multimodal Biometric Recognition Using Particle Swarm Optimization-Based Selected Features
  Sara Motamed Ali Broumandnia Azam sadat  Nourbakhsh
  10.7508/jist.2013.02.002
  Feature selection is one of the best optimization problems in human recognition, which reduces the number of features, removes noise and redundant data in images, and results in high rate of recognition. This step affects on the performance of a human recognition system More

  Feature selection is one of the best optimization problems in human recognition, which reduces the number of features, removes noise and redundant data in images, and results in high rate of recognition. This step affects on the performance of a human recognition system. This paper presents a multimodal biometric verification system based on two features of palm and ear which has emerged as one of the most extensively studied research topics that spans multiple disciplines such as pattern recognition, signal processing and computer vision. Also, we present a novel Feature selection algorithm based on Particle Swarm Optimization (PSO). PSO is a computational paradigm based on the idea of collaborative behavior inspired by the social behavior of bird flocking or fish schooling. In this method, we used from two Feature selection techniques: the Discrete Cosine Transforms (DCT) and the Discrete Wavelet Transform (DWT). The identification process can be divided into the following phases: capturing the image; pre-processing; extracting and normalizing the palm and ear images; feature extraction; matching and fusion; and finally, a decision based on PSO and GA classifiers. The system was tested on a database of 60 people (240 palm and 180 ear images). Experimental results show that the PSO-based feature selection algorithm was found to generate excellent recognition results with the minimal set of selected features. Manuscript profile
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  2 - A Hybrid Cuckoo Search for Direct Blockmodeling
  Saeed NasehiMoghaddam mehdi ghazanfari babak teimourpour
  10.7508/jist.2017.18.001
  As a way of simplifying, size reducing and making sense of the structure of each social network, blockmodeling consists of two major, essential components: partitioning of actors to equivalence classes, called positions, and clarifying relations between and within posit More

  As a way of simplifying, size reducing and making sense of the structure of each social network, blockmodeling consists of two major, essential components: partitioning of actors to equivalence classes, called positions, and clarifying relations between and within positions. Partitioning of actors to positions is done variously and the ties between and within positions can be represented by density matrices, image matrices and reduced graphs. While actor partitioning in classic blockmodeling is performed by several equivalence definitions, such as structural and regular equivalence, generalized blockmodeling, using a local optimization procedure, searches the best partition vector that best satisfies a predetermined image matrix. The need for known predefined social structure and using a local search procedure to find the best partition vector fitting into that predefined image matrix, makes generalized blockmodeling be restricted. In this paper, we formulate blockmodel problem and employ a genetic algorithm to search for the best partition vector fitting into original relational data in terms of the known indices. In addition, during multiple samples and various situations such as dichotomous, signed, ordinal or interval valued relations, and multiple relations the quality of results shows better fitness to original relational data than solutions reported by researchers in classic, generalized, and stochastic blockmodeling field. Manuscript profile
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  3 - Hybrid Task Scheduling Method for Cloud Computing by Genetic and PSO Algorithms
  Amin Kamalinia Ali Ghaffari
  10.7508/jist.2016.04.008
  Cloud computing makes it possible for users to use different applications through the internet without having to install them. Cloud computing is considered to be a novel technology which is aimed at handling and providing online services. For enhancing efficiency in cl More

  Cloud computing makes it possible for users to use different applications through the internet without having to install them. Cloud computing is considered to be a novel technology which is aimed at handling and providing online services. For enhancing efficiency in cloud computing, appropriate task scheduling techniques are needed. Due to the limitations and heterogeneity of resources, the issue of scheduling is highly complicated. Hence, it is believed that an appropriate scheduling method can have a significant impact on reducing makespans and enhancing resource efficiency. Inasmuch as task scheduling in cloud computing is regarded as an NP complete problem; traditional heuristic algorithms used in task scheduling do not have the required efficiency in this context. With regard to the shortcomings of the traditional heuristic algorithms used in job scheduling, recently, the majority of researchers have focused on hybrid meta-heuristic methods for task scheduling. With regard to this cutting edge research domain, we used HEFT (Heterogeneous Earliest Finish Time) algorithm to propose a hybrid meta-heuristic method in this paper where genetic algorithm (GA) and particle swarm optimization (PSO) algorithms were combined with each other. The results of simulation and statistical analysis of proposed scheme indicate that the proposed algorithm, when compared with three other heuristic and a memetic algorithms, has optimized the makespan required for executing tasks. Manuscript profile
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  4 - Toward Energy-Aware Traffic Engineering in Intra-Domain IP Networks Using Heuristic and Meta-Heuristics Approaches
  Muharram Mansoorizadeh
  10.7508/jist.2018.02.005
  Because of various ecological, environmental, and economic issues, energy efficient networking has been a subject of interest in recent years. In a typical backbone network, all the routers and their ports are always active and consume energy. Average link utilization i More

  Because of various ecological, environmental, and economic issues, energy efficient networking has been a subject of interest in recent years. In a typical backbone network, all the routers and their ports are always active and consume energy. Average link utilization in internet service providers is about 30-40%. Energy-aware traffic engineering aims to change routing algorithms so that low utilized links would be deactivated and their load would be distributed over other routes. As a consequence, by turning off these links and their respective devices and ports, network energy consumption is significantly decreased. In this paper, we propose four algorithms for energy-aware traffic engineering in intra-domain networks. Sequential Link Elimination (SLE) removes links based on their role in maximum network utilization. As a heuristic method, Extended Minimum Spanning Tree (EMST) uses minimum spanning trees to eliminate redundant links and nodes. Energy-aware DAMOTE (EAD) is another heuristic method that turns off links with low utilization. The fourth approach is based on genetic algorithms that randomly search for feasible network architectures in a potentially huge solution space. Evaluation results on Abilene network with real traffic matrix indicate that about 35% saving can be obtained by turning off underutilized links and routers on off-peak hours with respect to QoS. Furthermore, experiments with GA confirm that a subset of links and core nodes with respect to QoS can be switched off when traffic is in its off-peak periods, and hence energy can be saved up to 37%. Manuscript profile
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  5 - Handwritten Digits Recognition Using an Ensemble Technique Based on the Firefly Algorithm
  Azar Mahmoodzadeh Hamed Agahi Marzieh  Salehi
  10.7508/jist.2018.03.003
  This paper develops a multi-step procedure for classifying Farsi handwritten digits using a combination of classifiers. Generally, the technique relies on extracting a set of characteristics from handwritten samples, training multiple classifiers to learn to discriminat More

  This paper develops a multi-step procedure for classifying Farsi handwritten digits using a combination of classifiers. Generally, the technique relies on extracting a set of characteristics from handwritten samples, training multiple classifiers to learn to discriminate between digits, and finally combining the classifiers to enhance the overall system performance. First, a pre-processing course is performed to prepare the images for the main steps. Then three structural and statistical characteristics are extracted which include several features, among which a multi-objective genetic algorithm selects those more effective ones in order to reduce the computational complexity of the classification step. For the base classification, a decision tree (DT), an artificial neural networks (ANN) and a k-nearest neighbor (KNN) models are employed. Finally, the outcomes of the classifiers are fed into a classifier ensemble system to make the final decision. This hybrid system assigns different weights for each class selected by each classifier. These voting weights are adjusted by a metaheuristic firefly algorithm which optimizes the accuracy of the overall system. The performance of the implemented approach on the standard HODA dataset is compared with the base classifiers and some state-of-the-art methods. Evaluation of the proposed technique demonstrates that the proposed hybrid system attains high performance indices including accuracy of 98.88% with only eleven features. Manuscript profile
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  6 - Sailor Localization in Oceans Beds using Genetic and Firefly Algorithm
  Shruti  Gupta Dr Ajay  Rana Vineet  Kansal
  10.29252/jist.8.31.175
  20.1001.1.23221437.2020.8.31.1.5
  The Localization is the core element in Wireless Sensor Network WSN, especially for those nodes without GPS or BDS; leaning towards improvement, based on its effective and increased use in the past decade. Localization methods are thus very important for estimating the More

  The Localization is the core element in Wireless Sensor Network WSN, especially for those nodes without GPS or BDS; leaning towards improvement, based on its effective and increased use in the past decade. Localization methods are thus very important for estimating the position of relative nodes in the network allowing a better and effective network for increasing the efficiency and thus increasing the lifeline of the network. Determining the current limitations in FA that are applied for solving different optimization problems is poor exploitation capability when the randomization factor is taken large during firefly changing position. This poor exploitation may lead to skip the most optimal solution even present in the vicinity of the current solution which results in poor local convergence rate that ultimately degrades the solution quality. This paper presents GEFIR (GenFire) algorithm to calculate position of unknown nodes for the fishermen in the ocean. The proposed approach calculates the position of unknown nodes, the proposed method effectively selects the anchor node in the cluster head to reduce the energy dissipation. Major benefits over other similar localization algorithms are a better positioning of nodes is provided and average localization error is reduced which eventually leads to better efficiency thus optimize the lifetime of the network for sailors. The obtained results depict that the proposed model surpasses the previous generation of localization algorithm in terms of energy dispersion and location estimation which is suitable for fishermen on the ocean bed. Manuscript profile
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  7 - Reducing Energy Consumption in Sensor-Based Internet of Things Networks Based on Multi-Objective Optimization Algorithms
  Mohammad sedighimanesh Hessam  Zandhessami Mahmood  Alborzi Mohammadsadegh  Khayyatian
  10.52547/jist.15639.10.39.180
  20.1001.1.23221437.2022.10.39.1.5
  Energy is an important parameter in establishing various communications types in the sensor-based IoT. Sensors usually possess low-energy and non-rechargeable batteries since these sensors are often applied in places and applications that cannot be recharged. The mos More

  Energy is an important parameter in establishing various communications types in the sensor-based IoT. Sensors usually possess low-energy and non-rechargeable batteries since these sensors are often applied in places and applications that cannot be recharged. The most important objective of the present study is to minimize the energy consumption of sensors and increase the IoT network's lifetime by applying multi-objective optimization algorithms when selecting cluster heads and routing between cluster heads for transferring data to the base station. In the present article, after distributing the sensor nodes in the network, the type-2 fuzzy algorithm has been employed to select the cluster heads and also the genetic algorithm has been used to create a tree between the cluster heads and base station. After selecting the cluster heads, the normal nodes become cluster members and send their data to the cluster head. After collecting and aggregating the data by the cluster heads, the data is transferred to the base station from the path specified by the genetic algorithm. The proposed algorithm was implemented with MATLAB simulator and compared with LEACH, MB-CBCCP, and DCABGA protocols, the simulation results indicate the better performance of the proposed algorithm in different environments compared to the mentioned protocols. Due to the limited energy in the sensor-based IoT and the fact that they cannot be recharged in most applications, the use of multi-objective optimization algorithms in the design and implementation of routing and clustering algorithms has a significant impact on the increase in the lifetime of these networks. Manuscript profile