Pub Date : 2016-12-01DOI: 10.1109/SSCI.2016.7849913
Alexander Krall, M. Kuhl, Stephen Moskal, S. Yang
Network infiltration is one of many types of cyber-based attacks that may be of interest to a cyber security analyst. Sufficient observation of particular events that may be uncommon during network infiltration requires special simulation techniques. This paper presents an application of the importance sampling method to estimate the likelihood of a successful network infiltration, given that sufficiently many network alerts have not been generated to achieve said success. The benefits of utilizing importance sampling within this context are assessed against the use of standard simulation.
{"title":"Assessing the likelihood of cyber network infiltration using rare-event simulation","authors":"Alexander Krall, M. Kuhl, Stephen Moskal, S. Yang","doi":"10.1109/SSCI.2016.7849913","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7849913","url":null,"abstract":"Network infiltration is one of many types of cyber-based attacks that may be of interest to a cyber security analyst. Sufficient observation of particular events that may be uncommon during network infiltration requires special simulation techniques. This paper presents an application of the importance sampling method to estimate the likelihood of a successful network infiltration, given that sufficiently many network alerts have not been generated to achieve said success. The benefits of utilizing importance sampling within this context are assessed against the use of standard simulation.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132059816","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 : 2016-12-01DOI: 10.1109/SSCI.2016.7850021
Haizhou Qu, D. Kazakov
Financial news and stocks appear linked to the point where the use of online news to forecast the markets has become a major selling point for some traders. The correlation between news content and stock returns is clearly of interest, but has been mostly centred on news meta-data, such as volume and popularity. We address this question here by measuring the correlation between the returns of 27 publicly traded companies and news about them as collected from Yahoo Financial News for the period 1 Oct 2014 to 30 Apr 2015. In all reported experiments, two metrics are defined, one to measure the distance between two time series, the other to quantify the difference between two collections of news items. Two 27 × 27 distance matrices are thus produced, and their correlation measured with the Mantel test. This allows us to estimate the correlation of stock market data (returns, change, volume and close price) with the content of published news in a given period of time. A number of representations for the news are tested, as well as different distance metrics between time series. Clear, statistically significant, moderate level correlations are detected in most cases. Lastly, the impact of the length of the period studied on the observed correlation is also investigated.
{"title":"Quantifying correlation between Financial News and stocks","authors":"Haizhou Qu, D. Kazakov","doi":"10.1109/SSCI.2016.7850021","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7850021","url":null,"abstract":"Financial news and stocks appear linked to the point where the use of online news to forecast the markets has become a major selling point for some traders. The correlation between news content and stock returns is clearly of interest, but has been mostly centred on news meta-data, such as volume and popularity. We address this question here by measuring the correlation between the returns of 27 publicly traded companies and news about them as collected from Yahoo Financial News for the period 1 Oct 2014 to 30 Apr 2015. In all reported experiments, two metrics are defined, one to measure the distance between two time series, the other to quantify the difference between two collections of news items. Two 27 × 27 distance matrices are thus produced, and their correlation measured with the Mantel test. This allows us to estimate the correlation of stock market data (returns, change, volume and close price) with the content of published news in a given period of time. A number of representations for the news are tested, as well as different distance metrics between time series. Clear, statistically significant, moderate level correlations are detected in most cases. Lastly, the impact of the length of the period studied on the observed correlation is also investigated.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132470289","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 : 2016-12-01DOI: 10.1109/SSCI.2016.7850026
Yilan Li, Zhe Li, Qinru Qiu
Recognizing handwritten texts is a challenging task due to many different writing styles and lack of clear boundary between adjacent characters. This problem has been tackled by many previous researchers using techniques such as deep learning networks and hidden Markov Models (HMM), etc. In this work we aim at offline fuzzy recognition of handwritten texts. A probabilistic inference network that performs recurrent belief propagation is developed to post process the recognition results of deep convolutional neural network (CNN) (e.g. LeNet) and form individual characters to words. The post processing has the capability of correcting deletion, insertion and replacement errors in a noisy input. The output of the inference network is a set of words with their probability of being the correct one. To limit the size of candidate words, a series of improvements have been made to the probabilistic inference network, including using a post Gaussian Mixture Estimation model to prune insignificant words. The experiments show that this model gives a competitively average accuracy of 85.5%, and the improvements provides 46.57% reduction of invalid candidate words.
{"title":"Assisting fuzzy offline handwriting recognition using recurrent belief propagation","authors":"Yilan Li, Zhe Li, Qinru Qiu","doi":"10.1109/SSCI.2016.7850026","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7850026","url":null,"abstract":"Recognizing handwritten texts is a challenging task due to many different writing styles and lack of clear boundary between adjacent characters. This problem has been tackled by many previous researchers using techniques such as deep learning networks and hidden Markov Models (HMM), etc. In this work we aim at offline fuzzy recognition of handwritten texts. A probabilistic inference network that performs recurrent belief propagation is developed to post process the recognition results of deep convolutional neural network (CNN) (e.g. LeNet) and form individual characters to words. The post processing has the capability of correcting deletion, insertion and replacement errors in a noisy input. The output of the inference network is a set of words with their probability of being the correct one. To limit the size of candidate words, a series of improvements have been made to the probabilistic inference network, including using a post Gaussian Mixture Estimation model to prune insignificant words. The experiments show that this model gives a competitively average accuracy of 85.5%, and the improvements provides 46.57% reduction of invalid candidate words.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132526442","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 : 2016-12-01DOI: 10.1109/SSCI.2016.7850025
Seng-Beng Ho, Fiona Liausvia
Open domain causal learning involves learning and establishing causal connections between events directly from sensory experiences. It has been established in psychology that this often requires background knowledge. However, background knowledge has to be built from first experiences, which we term ground level causal learning, which basically involves observing temporal correlations. Subsequent knowledge level causal learning can then be based on this ground level causal knowledge. The causal connections between events, such as between lightning and thunder, are often hard to discern based on simple temporal correlations because there might be noise - e.g., wind, headlights, sounds of vehicles, etc. - that intervene between lightning and thunder. In this paper, we adopt the position that causal learning is inductive and pragmatic, and causal connections exist on a scale of graded strength. We describe a method that is able to filter away noise in the environment to obtain likely causal connections between events.
{"title":"A ground level causal learning algorithm","authors":"Seng-Beng Ho, Fiona Liausvia","doi":"10.1109/SSCI.2016.7850025","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7850025","url":null,"abstract":"Open domain causal learning involves learning and establishing causal connections between events directly from sensory experiences. It has been established in psychology that this often requires background knowledge. However, background knowledge has to be built from first experiences, which we term ground level causal learning, which basically involves observing temporal correlations. Subsequent knowledge level causal learning can then be based on this ground level causal knowledge. The causal connections between events, such as between lightning and thunder, are often hard to discern based on simple temporal correlations because there might be noise - e.g., wind, headlights, sounds of vehicles, etc. - that intervene between lightning and thunder. In this paper, we adopt the position that causal learning is inductive and pragmatic, and causal connections exist on a scale of graded strength. We describe a method that is able to filter away noise in the environment to obtain likely causal connections between events.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130017096","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 : 2016-12-01DOI: 10.1109/SSCI.2016.7850271
M. D. Erbas
Imitation is a social learning method in which an individual observes and mimics another's actions. To implement imitation on robots, a number of questions should be answered, including what information should be copied during imitation, how to choose the behaviors to be copied and how to translate the observed behaviors. In this research, we aim to answer the first two questions in an experiment scenario with simulated agents. First, based on the content of information that is copied during imitation, we compare two different imitation methods, namely, imitation of actions only and imitation of actions and perceptions. It is shown that if the observed behaviors are highly context specific, imitating perceptions along with actions is beneficial compared to imitating actions only. Second, to answer the question of which behaviors to copy, we compared different selection strategies. It is shown that the agents can choose which behaviors to copy by checking the utility of observed behaviors by a trial and error mechanism.
{"title":"On the mechanisms of imitation in multi-agent systems","authors":"M. D. Erbas","doi":"10.1109/SSCI.2016.7850271","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7850271","url":null,"abstract":"Imitation is a social learning method in which an individual observes and mimics another's actions. To implement imitation on robots, a number of questions should be answered, including what information should be copied during imitation, how to choose the behaviors to be copied and how to translate the observed behaviors. In this research, we aim to answer the first two questions in an experiment scenario with simulated agents. First, based on the content of information that is copied during imitation, we compare two different imitation methods, namely, imitation of actions only and imitation of actions and perceptions. It is shown that if the observed behaviors are highly context specific, imitating perceptions along with actions is beneficial compared to imitating actions only. Second, to answer the question of which behaviors to copy, we compared different selection strategies. It is shown that the agents can choose which behaviors to copy by checking the utility of observed behaviors by a trial and error mechanism.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130222189","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 : 2016-12-01DOI: 10.1109/SSCI.2016.7850251
Xiaoliang Xu, Xin Jin, Rui Yan, Xun Cao
Several conventional methods have been implemented in pattern recognition, but few of them have biological plausibility. This paper mimics the hierarchical visual system and uses the precise-spike-driven (PSD) synaptic plasticity rule to learn. The well-known HMAX model imitates the visual cortex and uses Gabor filter and max pooling to extract features. Compared with the traditional HMAX model, our modified model combines with the characteristics of sparse coding, and retains the features of the image in each orientation. In learning layer, it is an effective preparation for the PSD rule that temporal coding conveys precise spatio-temporal information. The PSD rule is simple and efficient in synaptic adaptation, and calculates directly. The results show our scheme provides a powerful approach for handwritten digit recognition in noisy conditions.
{"title":"A hierarchical visual recognition model with precise-spike-driven synaptic plasticity","authors":"Xiaoliang Xu, Xin Jin, Rui Yan, Xun Cao","doi":"10.1109/SSCI.2016.7850251","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7850251","url":null,"abstract":"Several conventional methods have been implemented in pattern recognition, but few of them have biological plausibility. This paper mimics the hierarchical visual system and uses the precise-spike-driven (PSD) synaptic plasticity rule to learn. The well-known HMAX model imitates the visual cortex and uses Gabor filter and max pooling to extract features. Compared with the traditional HMAX model, our modified model combines with the characteristics of sparse coding, and retains the features of the image in each orientation. In learning layer, it is an effective preparation for the PSD rule that temporal coding conveys precise spatio-temporal information. The PSD rule is simple and efficient in synaptic adaptation, and calculates directly. The results show our scheme provides a powerful approach for handwritten digit recognition in noisy conditions.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133866454","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 : 2016-12-01DOI: 10.1109/SSCI.2016.7850227
O. Maler, Abhinav Srivastav
Many real-world problems have multiple, conflicting objectives and a large complex solution space. The conflicting objectives give rise to a set of non-dominating solutions, known as the Pareto front. In the absence of any prior information on the relative importance of the objectives, none of these solutions can be said to be better than others, and they should all be presented to the decision maker as alternatives. In most cases, the number of Pareto solutions can be huge and we would like to provide a good representative approximation of the Pareto front. Moreover, the search space can be too large and complex for the problem to be solved by exact methods. Therefore efficient heuristic search algorithms are needed that can handle such problems. In this paper, we propose a double archive based Pareto local search. The two archives of our algorithm are used to maintain (i) the current set of non-dominated solutions, and (ii) the set of promising candidate solutions whose neighbors have not been explored yet. Our selection criteria is based on choosing the candidate solutions from the second archive. This method improves upon the existing Pareto local search and queued Pareto local search methods for bi-objective and tri-objective quadratic assignment problem.
{"title":"Double archive Pareto local search","authors":"O. Maler, Abhinav Srivastav","doi":"10.1109/SSCI.2016.7850227","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7850227","url":null,"abstract":"Many real-world problems have multiple, conflicting objectives and a large complex solution space. The conflicting objectives give rise to a set of non-dominating solutions, known as the Pareto front. In the absence of any prior information on the relative importance of the objectives, none of these solutions can be said to be better than others, and they should all be presented to the decision maker as alternatives. In most cases, the number of Pareto solutions can be huge and we would like to provide a good representative approximation of the Pareto front. Moreover, the search space can be too large and complex for the problem to be solved by exact methods. Therefore efficient heuristic search algorithms are needed that can handle such problems. In this paper, we propose a double archive based Pareto local search. The two archives of our algorithm are used to maintain (i) the current set of non-dominated solutions, and (ii) the set of promising candidate solutions whose neighbors have not been explored yet. Our selection criteria is based on choosing the candidate solutions from the second archive. This method improves upon the existing Pareto local search and queued Pareto local search methods for bi-objective and tri-objective quadratic assignment problem.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133933629","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 : 2016-12-01DOI: 10.1109/SSCI.2016.7849839
Jingwei Hu, Qiuye Sun, F. Teng
This paper designs a distributed trading mechanism for Energy Internet in which the uncertainty of distributed generations (DGs) is considered. In order to match up Energy Internet, the new energy frame, a novel energy accessing mode called We-Energy (WE) is proposed for the convenience of energy regulation, energy trading and information interaction. First, multiple interconnected WEs are considered in a region where, at a given time, some WEs have superfluous energy for sale to make profits called Surplus-WEs, but some WEs need to buy additional energy to meet local demands called Short-WEs. Under the trading mechanism, the market clearing price (MCP) is determined by supplies as well as demands. Then, a Bayesian game model considering distributed generations (DGs) uncertainty is established to analyze the strategies among WEs, which are assumed as the bidding supplies and demands. A unique Bayesian-Nash equilibrium among Surplus-WEs and Short-WEs are proved respectively according to hessian matrix, and solved by using the Karush-Kuhn-Tucker (KKT) conditions. Numerical results show that the designed MPC can reflect the relationship of supply and demand better, and maximize the utility of all the WEs.
{"title":"A game-theoretic pricing model for Energy Internet in day-ahead trading market considering distributed generations uncertainty","authors":"Jingwei Hu, Qiuye Sun, F. Teng","doi":"10.1109/SSCI.2016.7849839","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7849839","url":null,"abstract":"This paper designs a distributed trading mechanism for Energy Internet in which the uncertainty of distributed generations (DGs) is considered. In order to match up Energy Internet, the new energy frame, a novel energy accessing mode called We-Energy (WE) is proposed for the convenience of energy regulation, energy trading and information interaction. First, multiple interconnected WEs are considered in a region where, at a given time, some WEs have superfluous energy for sale to make profits called Surplus-WEs, but some WEs need to buy additional energy to meet local demands called Short-WEs. Under the trading mechanism, the market clearing price (MCP) is determined by supplies as well as demands. Then, a Bayesian game model considering distributed generations (DGs) uncertainty is established to analyze the strategies among WEs, which are assumed as the bidding supplies and demands. A unique Bayesian-Nash equilibrium among Surplus-WEs and Short-WEs are proved respectively according to hessian matrix, and solved by using the Karush-Kuhn-Tucker (KKT) conditions. Numerical results show that the designed MPC can reflect the relationship of supply and demand better, and maximize the utility of all the WEs.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131493604","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 : 2016-12-01DOI: 10.1109/SSCI.2016.7850284
C. Klüver, J. Klüver
We describe the regulatory algorithm (RGA), a two dimensional extension of standard evolutionary algorithms. Its possibilities are shown by an application to the problem of optimizing timetabling for exams with real data from the University Duisburg-Essen (Germany). The results of the RGA application show that the room allocation problem for written exams can be satisfactory solved in a few minutes. In addition we compared the RGA with a standard GA. The RGA was significantly better in all experiments; in particular the GA could not fulfill all distribution demands in contrast to the RGA.
{"title":"A regulatory algorithm (RGA) for optimizing examination timetabling","authors":"C. Klüver, J. Klüver","doi":"10.1109/SSCI.2016.7850284","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7850284","url":null,"abstract":"We describe the regulatory algorithm (RGA), a two dimensional extension of standard evolutionary algorithms. Its possibilities are shown by an application to the problem of optimizing timetabling for exams with real data from the University Duisburg-Essen (Germany). The results of the RGA application show that the room allocation problem for written exams can be satisfactory solved in a few minutes. In addition we compared the RGA with a standard GA. The RGA was significantly better in all experiments; in particular the GA could not fulfill all distribution demands in contrast to the RGA.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132566770","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 : 2016-12-01DOI: 10.1109/SSCI.2016.7850108
Aram Vroom, M. D. Carlo, Juan Manuel Romero Martin, M. Vasile
In this paper, a combinatorial optimisation algorithm inspired by the Physarum Polycephalum mould is presented and applied to the optimal trajectory planning of a multiple asteroid tour mission. The Automatic Incremental Decision Making And Planning (AIDMAP) algorithm is capable of solving complex discrete decision making problems with the use of the growth and exploration of the decision network. The stochastic AIDMAP algorithm has been tested on two discrete astrodynamic decision making problems of increased complexity and compared in terms of accuracy and computational cost to its deterministic counterpart. The results obtained for a mission to the Atira asteroids and to the Main Asteroid Belt show that this non-deterministic algorithm is a good alternative to the use of traditional deterministic combinatorial solvers, as the computational cost scales better with the complexity of the problem.
本文提出了一种受多头绒泡菌模型启发的组合优化算法，并将其应用于多小行星漫游任务的最优轨迹规划。AIDMAP (Automatic Incremental Decision Making And Planning)算法利用决策网络的生长和探索来解决复杂的离散决策问题。在两个日益复杂的离散天体动力学决策问题上，对随机AIDMAP算法进行了测试，并在精度和计算成本方面与确定性算法进行了比较。对Atira小行星和主小行星带任务的结果表明，该非确定性算法是传统确定性组合求解方法的一个很好的替代方案，因为计算成本与问题的复杂性成比例。
{"title":"Optimal trajectory planning for multiple asteroid tour mission by means of an incremental bio-inspired tree search algorithm","authors":"Aram Vroom, M. D. Carlo, Juan Manuel Romero Martin, M. Vasile","doi":"10.1109/SSCI.2016.7850108","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7850108","url":null,"abstract":"In this paper, a combinatorial optimisation algorithm inspired by the Physarum Polycephalum mould is presented and applied to the optimal trajectory planning of a multiple asteroid tour mission. The Automatic Incremental Decision Making And Planning (AIDMAP) algorithm is capable of solving complex discrete decision making problems with the use of the growth and exploration of the decision network. The stochastic AIDMAP algorithm has been tested on two discrete astrodynamic decision making problems of increased complexity and compared in terms of accuracy and computational cost to its deterministic counterpart. The results obtained for a mission to the Atira asteroids and to the Main Asteroid Belt show that this non-deterministic algorithm is a good alternative to the use of traditional deterministic combinatorial solvers, as the computational cost scales better with the complexity of the problem.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133820967","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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