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21 – 30 of 748Bundit Manaskasemsak and Arnon Rungsawang
This paper aims to present a machine learning approach for solving the problem of Web spam detection. Based on an adoption of the ant colony optimization (ACO), three algorithms…
Abstract
Purpose
This paper aims to present a machine learning approach for solving the problem of Web spam detection. Based on an adoption of the ant colony optimization (ACO), three algorithms are proposed to construct rule-based classifiers to distinguish between non-spam and spam hosts. Moreover, the paper also proposes an adaptive learning technique to enhance the spam detection performance.
Design/methodology/approach
The Trust-ACO algorithm is designed to let an ant start from a non-spam seed, and afterwards, decide to walk through paths in the host graph. Trails (i.e. trust paths) discovered by ants are then interpreted and compiled to non-spam classification rules. Similarly, the Distrust-ACO algorithm is designed to generate spam classification ones. The last Combine-ACO algorithm aims to accumulate rules given from the former algorithms. Moreover, an adaptive learning technique is introduced to let ants walk with longer (or shorter) steps by rewarding them when they find desirable paths or penalizing them otherwise.
Findings
Experiments are conducted on two publicly available WEBSPAM-UK2006 and WEBSPAM-UK2007 datasets. The results show that the proposed algorithms outperform well-known rule-based classification baselines. Especially, the proposed adaptive learning technique helps improving the AUC scores up to 0.899 and 0.784 on the former and the latter datasets, respectively.
Originality/value
To the best of our knowledge, this is the first comprehensive study that adopts the ACO learning approach to solve the problem of Web spam detection. In addition, we have improved the traditional ACO by using the adaptive learning technique.
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Kalpna Guleria and Anil Kumar Verma
Wireless sensor networks (WSNs) have emerged as one of the most promising technology in our day-to-day life. Limited network lifetime and higher energy consumption are two most…
Abstract
Purpose
Wireless sensor networks (WSNs) have emerged as one of the most promising technology in our day-to-day life. Limited network lifetime and higher energy consumption are two most critical issues in WSNs. The purpose of this paper is to propose an energy-efficient load balanced cluster-based routing protocol using ant colony optimization (LB-CR-ACO) which ultimately results in enhancement of the network lifetime of WSNs.
Design/methodology/approach
The proposed protocol performs optimal clustering based on cluster head selection weighing function which leads to novel cluster head selection. The cluster formation uses various parameters which are remaining energy of the nodes, received signal strength indicator (RSSI), node density and number of load-balanced node connections. Priority weights are also assigned among these metrics. The cluster head with the highest probability will be selected as an optimal cluster head for a particular round. LB-CR-ACO also performs a dynamic selection of optimal cluster head periodically which conserves energy, thereby using network resources in an efficient and balanced manner. ACO is used in steady state phase for multi-hop data transfer.
Findings
It has been observed through simulation that LB-CR-ACO protocol exhibits better performance for network lifetime in sparse, medium and dense WSN deployments than its peer protocols.
Originality/value
The proposed paper provides a unique energy-efficient LB-CR-ACO for WSNs. LB-CR-ACO performs novel cluster head selection using optimal clustering and multi-hop routing which utilizes ACO. The proposed work results in achieving higher network lifetime than its peer protocols.
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Maryam Daei and S. Hamid Mirmohammadi
The interest in the ability to detect damage at the earliest possible stage is pervasive throughout the civil engineering over the last two decades. In general, the experimental…
Abstract
Purpose
The interest in the ability to detect damage at the earliest possible stage is pervasive throughout the civil engineering over the last two decades. In general, the experimental techniques for damage detection are expensive and require that the vicinity of the damage is known and readily accessible; therefore several methods intend to detect damage based on numerical model and by means of minimum experimental data about dynamic properties or response of damaged structures. The paper aims to discuss these issues.
Design/methodology/approach
In this paper, the damage detection problem is formulated as an optimization problem such as to obtain the minimum difference between the numerical and experimental variables, and then a modified ant colony optimization (ACO) algorithm is proposed for solving this optimization problem. In the proposed algorithm, the structural damage is detected by using dynamically measured flexibility matrix, since the flexibility matrix of the structure can be estimated from only the first few modes. The continuous version of ACO is employed as a probabilistic technique for solving this computational problem.
Findings
Compared to classical methods, one of the main strengths of this meta-heuristic method is the generally better robustness in achieving global optimum. The efficiency of the proposed algorithm is illustrated by numerical examples. The proposed method enables the deduction of the extent and location of structural damage, while using short computational time and resulting good accuracy.
Originality/value
Finding accurate results by means of minimum experimental data, while using short computational time is the final goal of all researches in the structural damage detection methods. In this paper, it gains by applying flexibility matrix in the definition of objective function, and also via using continuous ant colony algorithm as a powerful meta-heuristic techniques in the constrained nonlinear optimization problem.
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Xiaofan Liu, Yupeng Zhou, Minghao Yin and Shuai Lv
The paper aims to provide an efficient meta-heuristic algorithm to solve the partial set covering problem (PSCP). With rich application scenarios, the PSCP is a fascinating and…
Abstract
Purpose
The paper aims to provide an efficient meta-heuristic algorithm to solve the partial set covering problem (PSCP). With rich application scenarios, the PSCP is a fascinating and well-known non-deterministic polynomial (NP)-hard problem whose goal is to cover at least k elements with as few subsets as possible.
Design/methodology/approach
In this work, the authors present a novel variant of the ant colony optimization (ACO) algorithm, called Argentine ant system (AAS), to deal with the PSCP. The developed AAS is an integrated system of different populations that use the same pheromone to communicate. Moreover, an effective local search framework with the relaxed configuration checking (RCC) and the volatilization-fixed weight mechanism is proposed to improve the exploitation of the algorithm.
Findings
A detailed experimental evaluation of 75 instances reveals that the proposed algorithm outperforms the competitors in terms of the quality of the optimal solutions. Also, the performance of AAS gradually improves with the growing instance size, which shows the potential in handling complex practical scenarios. Finally, the designed components of AAS are experimentally proved to be beneficial to the whole framework. Finally, the key components in AAS have been demonstrated.
Originality/value
At present, there is no heuristic method to solve this problem. The authors present the first implementation of heuristic algorithm for solving PSCP and provide competitive solutions.
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Tintu Mary John and Shanty Chacko
This paper aims to concentrate on an efficient finite impulse response (FIR) filter architecture in combination with the differential evolution ant colony algorithm (DE-ACO). For…
Abstract
Purpose
This paper aims to concentrate on an efficient finite impulse response (FIR) filter architecture in combination with the differential evolution ant colony algorithm (DE-ACO). For the design of FIR filter, the evolutionary algorithm (EA) is found to be very efficient because of its non-conventional, nonlinear, multi-modal and non-differentiable nature. While focusing with frequency domain specifications, most of the EA techniques described with the existing systems diverge from the power related matters.
Design/methodology/approach
The FIR filters are extensively used for many low power, low complexities, less area and high speed digital signal processing applications. In the existing systems, various FIR filters have been proposed to focus on the above criterion.
Findings
In the proposed method, a novel DE-ACO is used to design the FIR filter. It focuses on satisfying the economic power utilization and also the specifications in the frequency domain.
Originality/value
The proposed DE-ACO gives outstanding performance with a strong ability to find optimal solution, and it has got quick convergence speed. The proposed method also uses the Software integrated synthesis environment (ISE) project navigator (p.28xd) for the simulation of FIR filter based on DE-ACO techniques.
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Habib Karimi, Hossein Ahmadi Danesh Ashtiani and Cyrus Aghanajafi
This paper aims to examine total annual cost from economic view mixed materials heat exchangers based on three optimization algorithms. This study compares the use of three…
Abstract
Purpose
This paper aims to examine total annual cost from economic view mixed materials heat exchangers based on three optimization algorithms. This study compares the use of three optimization algorithms in the design of economic optimization shell and tube mixed material heat exchangers.
Design/methodology/approach
A shell and tube mixed materials heat exchanger optimization design approach is expanded based on the total annual cost measured by dividing the costs of the heat exchanger to area of surface and power consumption. In this study, optimization and minimization of the total annual cost is considered as the objective function. There are three types of exchangers: cheap, expensive and mixed. Mixed materials are used in corrosive flows in the heat exchanger network. The present study explores the use of three optimization techniques, namely, hybrid genetic-particle swarm optimization, shuffled frog leaping algorithm techniques and ant colony optimization.
Findings
There are three parameters as decision variables such as tube outer diameter, shell diameter and central baffle spacing considered for optimization. Results have been compared with the findings of previous studies to demonstrate the accuracy of algorithms.
Originality/value
The present study explores the use of three optimization techniques, namely, hybrid genetic-particle swarm optimization, shuffled frog leaping algorithm techniques and ant colony optimization. This study has demonstrated successful application of each technique for the optimal design of a mixed material shell and tube heat exchanger from the economic view point.
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Shailja Agnihotri and K.R. Ramkumar
The purpose of this paper is to provide insight into various swarm intelligence-based routing protocols for Internet of Things (IoT), which are currently available for the Mobile…
Abstract
Purpose
The purpose of this paper is to provide insight into various swarm intelligence-based routing protocols for Internet of Things (IoT), which are currently available for the Mobile Ad-hoc networks (MANETs) and wireless sensor networks (WSNs). There are several issues which are limiting the growth of IoT. These include privacy, security, reliability, link failures, routing, heterogeneity, etc. The routing issues of MANETs and WSNs impose almost the same requirements for IoT routing mechanism. The recent work of worldwide researchers is focused on this area.
Design/methodology/approach
The paper provides the literature review for various standard routing protocols. The different comparative analysis of the routing protocols is done. The paper surveys various routing protocols available for the seamless connectivity of things in IoT. Various features, advantages and challenges of the said protocols are discussed. The protocols are based on the principles of swarm intelligence. Swarm intelligence is applied to achieve optimality and efficiency in solving the complex, multi-hop and dynamic requirements of the wireless networks. The application of the ant colony optimization technique tries to provide answers to many routing issues.
Findings
Using the swarm intelligence and ant colony optimization principles, it has been seen that the protocols’ efficiency definitely increases and also provides more scope for the development of more robust, reliable and efficient routing protocols for the IoT.
Research limitations/implications
The existing protocols do not solve all reliability issues and efficient routing is still not achieved completely. As of now no techniques or protocols are efficient enough to cover all the issues and provide the solution. There is a need to develop new protocols for the communication which will cater to all these needs. Efficient and scalable routing protocols adaptable to different scenarios and network size variation capable to find optimal routes are required.
Practical implications
The various routing protocols are discussed and there is also an introduction to new parameters which can strengthen the protocols. This can lead to encouragement of readers, as well as researchers, to analyze and develop new routing algorithms.
Social implications
The paper provides better understanding of the various routing protocols and provides better comparative analysis for the use of swarm-based research methodology in the development of routing algorithms exclusively for the IoT.
Originality/value
This is a review paper which discusses the various routing protocols available for MANETs and WSNs and provides the groundwork for the development of new intelligent routing protocols for IoT.
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In this paper, a data mining approach is proposed for monitoring the conditions leading to a rail wheel high impact load. The proposed approach incorporates logical analysis of…
Abstract
Purpose
In this paper, a data mining approach is proposed for monitoring the conditions leading to a rail wheel high impact load. The proposed approach incorporates logical analysis of data (LAD) and ant colony optimization (ACO) algorithms in extracting patterns of high impact loads and normal loads from historical railway records. In addition, the patterns are employed in establishing a classification model used for classifying unseen observations. A case study representing real-world impact load data is presented to illustrate the impact of the proposed approach in improving railway services.
Design/methodology/approach
Application of artificial intelligence and machine learning approaches becomes an essential tool in improving the performance of railway transportation systems. By using these approaches, the knowledge extracted from historical data can be employed in railway assets monitoring to maintain the assets in a reliable state and to improve the service provided by the railway network.
Findings
Results achieved by the proposed approach provide a prognostic system used for monitoring the conditions surrounding rail wheels. Incorporating this prognostic system in surveilling the rail wheels indeed results in better railway services as trips with no-delay or no-failure can be realized. A comparative study is conducted to evaluate the performance of the proposed approach versus other classification algorithms. In addition to the highly interpretable results obtained by the generated patterns, the comparative study demonstrates that the proposed approach provides classification accuracy higher than other common machine learning classification algorithms.
Originality/value
The methodology followed in this research employs ACO algorithm as an artificial intelligent technique and LDA as a machine learning algorithm in analyzing wheel impact load alarm-collected datasets. This new methodology provided a promising classification model to predict future alarm and a prognostic system to guide the system while avoiding this alarm.
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Hongbo Shan, Shenhua Zhou and Zhihong Sun
The purpose of this paper is to propose a novel method under the name of genetic simulated annealing algorithm (GSAA) and ant colony optimization (ACO) algorithm for assembly…
Abstract
Purpose
The purpose of this paper is to propose a novel method under the name of genetic simulated annealing algorithm (GSAA) and ant colony optimization (ACO) algorithm for assembly sequence planning (ASP) which is possessed of the competence for assisting the planner in generating a satisfied and effective assembly sequence with respect to large constraint assembly perplexity.
Design/methodology/approach
Based on the genetic algorithm (GA), simulated annealing, and ACO algorithm, the GSAA are put forward. A case study is presented to validate the proposed method.
Findings
This GSAA has better optimization performance and robustness. The degree of dependence on the initial assembly sequence about GSAA is decreased. The optimization assembly sequence still can be obtained even if the assembly sequences of initial population are infeasible. By combining GA and simulated annealing (SA), the efficiency of searching and the quality of solution of GSAA is improved. As for the presented ACO algorithm, the searching speed is further increased.
Originality/value
Traditionally, GA heavily depends on the choosing original sequence, which can result in early convergence in iterative operation, lower searching efficiency in evolutionary process, and non‐optimization of final result for global variable. Similarly, SA algorithms may generate a great deal of infeasible solutions in the evolution process by generating new sequences through exchanging position of the randomly selected two parts, which results in inefficiency of the solution‐searching process. In this paper, the proposed GSAA and ACO algorithm for ASP are possessed of the competence for assisting the planner in generating a satisfied and effective assembly sequence with respect to large constraint assembly perplexity.
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Lynne Butel and Alison Watkins
Entrepreneurs operate in conditions of dynamic uncertainty; identifying and exploiting opportunities presented by the business environment. Opportunistic search is core to…
Abstract
Purpose
Entrepreneurs operate in conditions of dynamic uncertainty; identifying and exploiting opportunities presented by the business environment. Opportunistic search is core to entrepreneurial activity, but its dynamics are rarely explored. Groups of entrepreneurs are attracted to the same potential business opportunities. They have no incentive to cooperate, they may not even know of the existence of others. However, over time, clusters of entrepreneurs interested in the same opportunities develop. Aims to discuss the issues.
Design/methodology/approach
Ant colony optimisation modelling is used to simulate the activities of entrepreneurs in an opportunity rich environment. The entrepreneurs must identify the locations of the appropriate resources. Three simulations were run to observe entrepreneurial success in different environments.
Findings
A random search of the business environment for resources by individual entrepreneurs was unproductive. Once the entrepreneurs learned to read the business environment and so refine their search, they were increasingly efficient. This was even more pronounced when time allowed for search was constrained and weaker entrepreneurs had little influence.
Research limitations/implications
The computer simulations demonstrate how a cluster of entrepreneurial activity may begin. The results raise questions about the appropriateness of policies supporting entrepreneurial activity and about the path dependency of cluster development. Empirical research is now needed to test these research implications.
Originality/value
Focusing on the little explored dynamics of opportunistic search by would‐be entrepreneurs in a spatially defined business environment combines previous research in the fields of entrepreneurial outcomes and cluster development. Using a multi‐agent search model to simulate the dynamic interaction of a number of entrepreneurs in the same business environment demonstrates early cluster formation without the protagonists relying on cooperative, competitive or value chain interaction.
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