Pub Date : 2017-03-01DOI: 10.1109/CSIEC.2017.7940169
Zeynab Javidi, R. Akbari, O. Bushehrian
The quality of software design always has a significant impact on the extendibility and maintainability of the final product. Automatic techniques may help designers to achieve better design. There are several ways for software design automation. Generally Search-based methods such as GA, ant colony, and ICA are used for problems with large search space in which finding the optimal solution is hard. In this paper a hybrid algorithm called ICA-TS (Imperialist Competitive Algorithm-Tabu Search) is presented to generate class diagram of the under design system automatically. The method has three phases: First, formal concept analysis (FCA) for preprocessing phase of the method is used as a mean to generate initial solution. Next a hybrid of ICA and TS is used to update solutions. The relationships between classes are determined in third phase. Three standard case studies are used for performance evaluation and the results are compared with results of genetic and simple ICA. The results show that the presented method has competitive results and it can generate more efficient class diagram in terms of cohesion, coupling and complexity of system.
{"title":"Semi-automatic object-oriented software design using metaheuristic algorithms","authors":"Zeynab Javidi, R. Akbari, O. Bushehrian","doi":"10.1109/CSIEC.2017.7940169","DOIUrl":"https://doi.org/10.1109/CSIEC.2017.7940169","url":null,"abstract":"The quality of software design always has a significant impact on the extendibility and maintainability of the final product. Automatic techniques may help designers to achieve better design. There are several ways for software design automation. Generally Search-based methods such as GA, ant colony, and ICA are used for problems with large search space in which finding the optimal solution is hard. In this paper a hybrid algorithm called ICA-TS (Imperialist Competitive Algorithm-Tabu Search) is presented to generate class diagram of the under design system automatically. The method has three phases: First, formal concept analysis (FCA) for preprocessing phase of the method is used as a mean to generate initial solution. Next a hybrid of ICA and TS is used to update solutions. The relationships between classes are determined in third phase. Three standard case studies are used for performance evaluation and the results are compared with results of genetic and simple ICA. The results show that the presented method has competitive results and it can generate more efficient class diagram in terms of cohesion, coupling and complexity of system.","PeriodicalId":166046,"journal":{"name":"2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114360768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-03-01DOI: 10.1109/CSIEC.2017.7940158
Somayyeh Asadifar, M. Kahani
the amount of ontologies and semantic annotations available on the Web is constantly growing and heterogeneous data raises new challenges for the data mining community. Yet there are still many problems causing users extra problems in discovering knowledge or even failing to obtain the real and useful knowledge they need. In this paper, we survey some semantic data mining methods specifically focusing on association rules. However, there are few works that have focused in mining semantic web data itself. For extracting rules in semantic data, we present an intelligent data mining approach incorporated with domain. The paper contributes a new algorithm for discovery of new type of patterns from semantic data. This new type of patterns is appropriate for some data such as stock market. We take advantage of the knowledge encoded in the ontology and MICF measure to inference in three steps to prune the search space and generated rules to derive appropriate rules from thousands of rules. Some experiments performed on stock market data and show the usefulness and efficiency of the approach.
{"title":"Semantic association rule mining: A new approach for stock market prediction","authors":"Somayyeh Asadifar, M. Kahani","doi":"10.1109/CSIEC.2017.7940158","DOIUrl":"https://doi.org/10.1109/CSIEC.2017.7940158","url":null,"abstract":"the amount of ontologies and semantic annotations available on the Web is constantly growing and heterogeneous data raises new challenges for the data mining community. Yet there are still many problems causing users extra problems in discovering knowledge or even failing to obtain the real and useful knowledge they need. In this paper, we survey some semantic data mining methods specifically focusing on association rules. However, there are few works that have focused in mining semantic web data itself. For extracting rules in semantic data, we present an intelligent data mining approach incorporated with domain. The paper contributes a new algorithm for discovery of new type of patterns from semantic data. This new type of patterns is appropriate for some data such as stock market. We take advantage of the knowledge encoded in the ontology and MICF measure to inference in three steps to prune the search space and generated rules to derive appropriate rules from thousands of rules. Some experiments performed on stock market data and show the usefulness and efficiency of the approach.","PeriodicalId":166046,"journal":{"name":"2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115908665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-03-01DOI: 10.1109/CSIEC.2017.7940152
Hasan Heydari, S. Taheri
A maximal independent set (MIS) on a graph is an inclusion-maximal set of mutually non-adjacent nodes. The problem of computing an MIS is one of the fundamental problems in the area of parallel and distributed algorithms. In this paper, we investigate the distributed maximal independent set problem on inhomogeneous random graphs by which the scale-free networks can be produced. Such a particular problem has been solved by state-of-the-art algorithms with time complexity of O(log n). We prove that on inhomogeneous random graphs with n nodes and power law exponent β ≥ 3, the arboricity and the degeneracy is less than 2(log n)1/3 with high probability (w.h.p.). Thus, the time complexity of finding an MIS on these graphs is O(log2/3 n). Furthermore, we propose a new algorithm for computing an MIS on inhomogeneous random graphs with power law exponent β < 3. The results of simulation studies show that the time complexity of the proposed algorithm is O(log2/3 n) for β < 3, which is better than O(log n).
{"title":"Distributed maximal independent set on inhomogeneous random graphs","authors":"Hasan Heydari, S. Taheri","doi":"10.1109/CSIEC.2017.7940152","DOIUrl":"https://doi.org/10.1109/CSIEC.2017.7940152","url":null,"abstract":"A maximal independent set (MIS) on a graph is an inclusion-maximal set of mutually non-adjacent nodes. The problem of computing an MIS is one of the fundamental problems in the area of parallel and distributed algorithms. In this paper, we investigate the distributed maximal independent set problem on inhomogeneous random graphs by which the scale-free networks can be produced. Such a particular problem has been solved by state-of-the-art algorithms with time complexity of O(log n). We prove that on inhomogeneous random graphs with n nodes and power law exponent β ≥ 3, the arboricity and the degeneracy is less than 2(log n)1/3 with high probability (w.h.p.). Thus, the time complexity of finding an MIS on these graphs is O(log2/3 n). Furthermore, we propose a new algorithm for computing an MIS on inhomogeneous random graphs with power law exponent β < 3. The results of simulation studies show that the time complexity of the proposed algorithm is O(log2/3 n) for β < 3, which is better than O(log n).","PeriodicalId":166046,"journal":{"name":"2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)","volume":"194 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129741026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-03-01DOI: 10.1109/CSIEC.2017.7940180
Sina Ebrahimi Farsangi, E. Rashedi, M. Farsangi
In this study, fuzzy logic technique and Gravitational Search Algorithm (GSA) are applied to place Static VAr Compensator (SVC) to improve voltage stability through a multi-objective placement problem. The VAr planning is formulated to maximize indexes of fuzzy performance including: deviation of bus voltage, loss of system, and the cost of installation. The results obtained are compared with fuzzy Real Genetic Algorithm (RGA). The results obtained show that the GSA has better convergence rate comparing to fuzzy RGA in finding the best solution.
{"title":"Multi-objective VAr planning using fuzzy-GSA","authors":"Sina Ebrahimi Farsangi, E. Rashedi, M. Farsangi","doi":"10.1109/CSIEC.2017.7940180","DOIUrl":"https://doi.org/10.1109/CSIEC.2017.7940180","url":null,"abstract":"In this study, fuzzy logic technique and Gravitational Search Algorithm (GSA) are applied to place Static VAr Compensator (SVC) to improve voltage stability through a multi-objective placement problem. The VAr planning is formulated to maximize indexes of fuzzy performance including: deviation of bus voltage, loss of system, and the cost of installation. The results obtained are compared with fuzzy Real Genetic Algorithm (RGA). The results obtained show that the GSA has better convergence rate comparing to fuzzy RGA in finding the best solution.","PeriodicalId":166046,"journal":{"name":"2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124080696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-03-01DOI: 10.1109/CSIEC.2017.7940163
A. Rouhi, H. Nezamabadi-pour
Nowadays, with the emergence of high-dimensional data, feature selection plays an important role in the domain of machine learning, particularly, classification problems, such that feature selection can be known as its vital and irremovable component. With the increase in the number of data dimensions, simple traditional methods show poor performance and cannot be used for effective and proper feature selection. Using embedded methods, this study first discusses data dimension reduction using a filter based approach. Two state-of-the-art meta-heuristic methods are then applied on the selected features and final desirable features are selected from the aggregation of their selected features. The proposed method is evaluated on 5 high-dimensional micro-array datasets and results are compared with several state-of-the-art feature selection approaches for high-dimensional data. Experimental results confirm the efficiency of the proposed method.
{"title":"A hybrid feature selection approach based on ensemble method for high-dimensional data","authors":"A. Rouhi, H. Nezamabadi-pour","doi":"10.1109/CSIEC.2017.7940163","DOIUrl":"https://doi.org/10.1109/CSIEC.2017.7940163","url":null,"abstract":"Nowadays, with the emergence of high-dimensional data, feature selection plays an important role in the domain of machine learning, particularly, classification problems, such that feature selection can be known as its vital and irremovable component. With the increase in the number of data dimensions, simple traditional methods show poor performance and cannot be used for effective and proper feature selection. Using embedded methods, this study first discusses data dimension reduction using a filter based approach. Two state-of-the-art meta-heuristic methods are then applied on the selected features and final desirable features are selected from the aggregation of their selected features. The proposed method is evaluated on 5 high-dimensional micro-array datasets and results are compared with several state-of-the-art feature selection approaches for high-dimensional data. Experimental results confirm the efficiency of the proposed method.","PeriodicalId":166046,"journal":{"name":"2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126521165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-03-01DOI: 10.1109/CSIEC.2017.7940170
Mohammad Shahabi, S. Badiei, S. E. Beheshtian, R. Akbari, S. M. R. Moosavi
Nowadays software has a major role in our everyday life. Many critical tasks are done by software systems. The increasing complexity of software systems compels providing techniques and tools to design correct and well-functioning software in safety-critical systems. Up to 50% of the total software project costs are devoted to testing; hence, increased concern of automated software testing in recent years. The automation of software testing reduces costs and improves the effectiveness of tests that are generated in order to detect defects in the software under test. Various techniques are adopted for automated software testing including metaheuristic search algorithms. In this paper, we propose the EvoPSO algorithm using swarm intelligence paradigm. The algorithm is implemented in EvoSuite tool for the purpose of test data generation. The performance of EvoPSO has been investigated on SF110 dataset. The promising performance shows that EvoPSO is efficient and can give competitive results.
{"title":"On the performance of EvoPSO: A PSO based algorithm for test data generation in EvoSuite","authors":"Mohammad Shahabi, S. Badiei, S. E. Beheshtian, R. Akbari, S. M. R. Moosavi","doi":"10.1109/CSIEC.2017.7940170","DOIUrl":"https://doi.org/10.1109/CSIEC.2017.7940170","url":null,"abstract":"Nowadays software has a major role in our everyday life. Many critical tasks are done by software systems. The increasing complexity of software systems compels providing techniques and tools to design correct and well-functioning software in safety-critical systems. Up to 50% of the total software project costs are devoted to testing; hence, increased concern of automated software testing in recent years. The automation of software testing reduces costs and improves the effectiveness of tests that are generated in order to detect defects in the software under test. Various techniques are adopted for automated software testing including metaheuristic search algorithms. In this paper, we propose the EvoPSO algorithm using swarm intelligence paradigm. The algorithm is implemented in EvoSuite tool for the purpose of test data generation. The performance of EvoPSO has been investigated on SF110 dataset. The promising performance shows that EvoPSO is efficient and can give competitive results.","PeriodicalId":166046,"journal":{"name":"2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126058574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-03-01DOI: 10.1109/CSIEC.2017.7940156
Hossein Shahinzadeh, M. Moazzami
Accurate and realistic Economic Load Dispatch (ELD) is one of the most important issues in power systems. In real conditions, ELD is limited to the different non-equal constraints that make ELD a non-convex and non-smooth problem. This makes it difficult to find a global optimized solution, even with the aid of classic mathematical methods. In this paper, a novel procedure is presented for solving ELD problems, which uses Hybrid Big Bang-Big Crunch Algorithm (HBB-BC) as an optimization tool. This algorithm has appropriate speed and accuracy compared with the most regular optimizing methods. The proposed algorithm is applied on an IEEE 30-bus test system. Simulation results demonstrate the algorithm's capability for successful optimization of ELD problem.
{"title":"Hybrid Big Bang-Big Crunch Algorithm for solving non-convex Economic Load Dispatch problems","authors":"Hossein Shahinzadeh, M. Moazzami","doi":"10.1109/CSIEC.2017.7940156","DOIUrl":"https://doi.org/10.1109/CSIEC.2017.7940156","url":null,"abstract":"Accurate and realistic Economic Load Dispatch (ELD) is one of the most important issues in power systems. In real conditions, ELD is limited to the different non-equal constraints that make ELD a non-convex and non-smooth problem. This makes it difficult to find a global optimized solution, even with the aid of classic mathematical methods. In this paper, a novel procedure is presented for solving ELD problems, which uses Hybrid Big Bang-Big Crunch Algorithm (HBB-BC) as an optimization tool. This algorithm has appropriate speed and accuracy compared with the most regular optimizing methods. The proposed algorithm is applied on an IEEE 30-bus test system. Simulation results demonstrate the algorithm's capability for successful optimization of ELD problem.","PeriodicalId":166046,"journal":{"name":"2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)","volume":"1980 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125696032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-03-01DOI: 10.1109/CSIEC.2017.7940177
Mansoore Saeedzarandi
spectrum sensing is an important component of cognitive radio technology which enables secondary users to sense the environment and find the spectrum holes. In this study, a multiband sensing-time-adaptive framework is used for wideband spectrum sensing in order to maximize the aggregate throughput capacity of the cognitive radios and reduce their interference to the primary users. The optimization problem is non-convex, and convex optimization can solve the problem with restrictions. in this paper we use the artificial immune algorithm based on the clonal selection theory to obtain the optimal solutions without any reformulations or mathematical costs.
{"title":"Optimization of multiband sensing-time-adaptive detection in cognitive radio networks using artificial immune algorithm","authors":"Mansoore Saeedzarandi","doi":"10.1109/CSIEC.2017.7940177","DOIUrl":"https://doi.org/10.1109/CSIEC.2017.7940177","url":null,"abstract":"spectrum sensing is an important component of cognitive radio technology which enables secondary users to sense the environment and find the spectrum holes. In this study, a multiband sensing-time-adaptive framework is used for wideband spectrum sensing in order to maximize the aggregate throughput capacity of the cognitive radios and reduce their interference to the primary users. The optimization problem is non-convex, and convex optimization can solve the problem with restrictions. in this paper we use the artificial immune algorithm based on the clonal selection theory to obtain the optimal solutions without any reformulations or mathematical costs.","PeriodicalId":166046,"journal":{"name":"2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116164518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-03-01DOI: 10.1109/CSIEC.2017.7940153
Hamidreza Keshavarz, M. S. Abadeh
One of the substantial tasks of opinion mining is to find semantic orientation and intensity of opinion words and phrases. This research tries to find numeric values for sentiment words and phrases by introducing a novel algorithm. The opinion about an object or its aspects is often expressed through sentiment phrases, and having a measure of quantification for them is essential for processing sentiments. In simple terms, semantic orientation or opinion intensity is a building block of opinion mining. This paper tries to (i) identify the sentiment phrases, and (ii) by means of a memetic algorithm, assign scores to each phrase and build a sentiment lexicon. This score shows the place of the phrase on a spectrum, ranging from very negative to very positive (0 to 10). The proposed method assigns real numbers to sentiment phrases and these scores show the intensity of each sentiment phrase. Three datasets were created and used in this paper: Movie, Music and Camera datasets which consist of reviews in each category. The intensity and polarity of words are calculated for each database, and compared to each other. The results show that (i) some words are not as positive or negative as previously thought; (ii) what is the effect of using adverbs, such as “very” and “not”; and (iii) sentiment phrases in different contexts have different intensities.
{"title":"SOMA: Semantic Orientation inference using Memetic Algorithm","authors":"Hamidreza Keshavarz, M. S. Abadeh","doi":"10.1109/CSIEC.2017.7940153","DOIUrl":"https://doi.org/10.1109/CSIEC.2017.7940153","url":null,"abstract":"One of the substantial tasks of opinion mining is to find semantic orientation and intensity of opinion words and phrases. This research tries to find numeric values for sentiment words and phrases by introducing a novel algorithm. The opinion about an object or its aspects is often expressed through sentiment phrases, and having a measure of quantification for them is essential for processing sentiments. In simple terms, semantic orientation or opinion intensity is a building block of opinion mining. This paper tries to (i) identify the sentiment phrases, and (ii) by means of a memetic algorithm, assign scores to each phrase and build a sentiment lexicon. This score shows the place of the phrase on a spectrum, ranging from very negative to very positive (0 to 10). The proposed method assigns real numbers to sentiment phrases and these scores show the intensity of each sentiment phrase. Three datasets were created and used in this paper: Movie, Music and Camera datasets which consist of reviews in each category. The intensity and polarity of words are calculated for each database, and compared to each other. The results show that (i) some words are not as positive or negative as previously thought; (ii) what is the effect of using adverbs, such as “very” and “not”; and (iii) sentiment phrases in different contexts have different intensities.","PeriodicalId":166046,"journal":{"name":"2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129102493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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