Ruba Almahasneh, Boldizsar Tuu-Szabo, P. Földesi, L. Kóczy
The Time Dependent Traveling Salesman Problem (TD TSP) is an extension of the classic Traveling Salesman Problem towards more realistic conditions. TSP is one of the most extensively studied NP-complete graph search problems. In TD TSP, the edges are assigned different weights, depending on whether they are traveled in the traffic jam regions (such as busy city centers) and during rush hour periods, or not. In such circumstances, edges are assigned higher costs, expressed by a multiplying factor. In this paper, we introduce a novel and even more realistic approach, the Interval Intuitionistic Fuzzy Time Dependent Traveling Salesman Problem (IVIFTD TSP); which is a further extension of the classic TD TSP, with the additional notion of deploying interval valued intuitionistic fuzzy for describing uncertainties. The core concept employs interval valued intuitionistic fuzzy sets for quantifying the traffic jam regions, and the rush hour periods loss (those are additional costs of the travel between nodes), which are always uncertain in real life. Since type-2 (such as inter valued) fuzzy sets have the potential to provide better performance in modeling problems with higher uncertainties than the traditional fuzzy set, the new approach it may be considered as an extended, practically more applicable, extended version of the original abstract problem. The optimization of such a complex model is obviously very difficult; it is a mathematically intractable problem. However, the Discrete Bacterial Memetic Evolutionary Algorithm proposed earlier by the authors' team has shown sufficient efficiency, general applicability for similar type problems and good predictability in terms of problem size, thus it is applied for the optimization of the concrete instances.
{"title":"Extension of the Time Dependent Travelling Salesman Problem with Interval Valued Intuitionistic Fuzzy Model Applying Memetic Optimization Algorithm","authors":"Ruba Almahasneh, Boldizsar Tuu-Szabo, P. Földesi, L. Kóczy","doi":"10.1145/3396474.3396490","DOIUrl":"https://doi.org/10.1145/3396474.3396490","url":null,"abstract":"The Time Dependent Traveling Salesman Problem (TD TSP) is an extension of the classic Traveling Salesman Problem towards more realistic conditions. TSP is one of the most extensively studied NP-complete graph search problems. In TD TSP, the edges are assigned different weights, depending on whether they are traveled in the traffic jam regions (such as busy city centers) and during rush hour periods, or not. In such circumstances, edges are assigned higher costs, expressed by a multiplying factor. In this paper, we introduce a novel and even more realistic approach, the Interval Intuitionistic Fuzzy Time Dependent Traveling Salesman Problem (IVIFTD TSP); which is a further extension of the classic TD TSP, with the additional notion of deploying interval valued intuitionistic fuzzy for describing uncertainties. The core concept employs interval valued intuitionistic fuzzy sets for quantifying the traffic jam regions, and the rush hour periods loss (those are additional costs of the travel between nodes), which are always uncertain in real life. Since type-2 (such as inter valued) fuzzy sets have the potential to provide better performance in modeling problems with higher uncertainties than the traditional fuzzy set, the new approach it may be considered as an extended, practically more applicable, extended version of the original abstract problem. The optimization of such a complex model is obviously very difficult; it is a mathematically intractable problem. However, the Discrete Bacterial Memetic Evolutionary Algorithm proposed earlier by the authors' team has shown sufficient efficiency, general applicability for similar type problems and good predictability in terms of problem size, thus it is applied for the optimization of the concrete instances.","PeriodicalId":408084,"journal":{"name":"Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114262034","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}
Francis Murillo, Tobias Neuenschwander, Rolf Dornberger, T. Hanne
This research work focusses on the optimization of a robotic manipulation problem. The problem is modeled with the robot simulation software V-REP. The objectives are the optimization movement path of the robot and its robotic arm for certain positions and orientations with respect to energy consumption. The paper compares an evolution strategy with particle swarm optimization to minimize deviations of position and orientation while using forward kinematics. Experiments to evaluate the algorithms are presented and discussed.
{"title":"Optimization of a Robotic Manipulation Path by an Evolution Strategy and Particle Swarm Optimization","authors":"Francis Murillo, Tobias Neuenschwander, Rolf Dornberger, T. Hanne","doi":"10.1145/3396474.3396488","DOIUrl":"https://doi.org/10.1145/3396474.3396488","url":null,"abstract":"This research work focusses on the optimization of a robotic manipulation problem. The problem is modeled with the robot simulation software V-REP. The objectives are the optimization movement path of the robot and its robotic arm for certain positions and orientations with respect to energy consumption. The paper compares an evolution strategy with particle swarm optimization to minimize deviations of position and orientation while using forward kinematics. Experiments to evaluate the algorithms are presented and discussed.","PeriodicalId":408084,"journal":{"name":"Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126799981","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}
Collaborative Filtering algorithm is widely used in plentiful personal recommendation system. However, it has low accuracy prediction in sparse data set. Current mainstream collaborative filtering algorithm filter neighbor of target user by calculating similarity between users with co-rated ratings. Nonnegative Matrix factorization (NMF) has a good performance in solving sparsity problem. Manifold learning algorithms can identify and preserve the intrinsic geometrical structure of data. In order to get more accurate recommendation results, we propose a hybrid Slope One algorithm based on NMF. By constraining PNMF with graph regularization term, then we propose a weighted Slope One algorithm combined with neighborhood preserving PNMF. The hybrid algorithm has positive consequences for new data and can reduce computation complexity. Experimental show that optimized method has a good recommendation effect compared with tradition algorithm, it helps to solve the data sparsity problem and can improve the scalability.
{"title":"A Hybrid Slope One Collaborative Filtering Algorithm Based on Nonnegative Matrix Factorization","authors":"Xiaoxi Shi","doi":"10.1145/3396474.3396496","DOIUrl":"https://doi.org/10.1145/3396474.3396496","url":null,"abstract":"Collaborative Filtering algorithm is widely used in plentiful personal recommendation system. However, it has low accuracy prediction in sparse data set. Current mainstream collaborative filtering algorithm filter neighbor of target user by calculating similarity between users with co-rated ratings. Nonnegative Matrix factorization (NMF) has a good performance in solving sparsity problem. Manifold learning algorithms can identify and preserve the intrinsic geometrical structure of data. In order to get more accurate recommendation results, we propose a hybrid Slope One algorithm based on NMF. By constraining PNMF with graph regularization term, then we propose a weighted Slope One algorithm combined with neighborhood preserving PNMF. The hybrid algorithm has positive consequences for new data and can reduce computation complexity. Experimental show that optimized method has a good recommendation effect compared with tradition algorithm, it helps to solve the data sparsity problem and can improve the scalability.","PeriodicalId":408084,"journal":{"name":"Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131454806","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}
For intelligent systems to become autonomous in any real sense, they need an ability to make decisions on situations that were not entirely conceived of at compile-time. Machine learning algorithms are excellent in mimicking the behaviour of some gold standard role model, and this can include decision making by the role model. But once out of familiar contexts, the decision making becomes harder and needs an element of more independent probabilistic reasoning and decision making. This paper presents such a method based on a belief mass interpretation of the decision information, where the components are imprecise and thus uncertain by means of intervals.
{"title":"Meta-Reasoning about Decisions in Autonomous Semi-Intelligent Systems","authors":"M. Danielson, L. Ekenberg","doi":"10.1145/3396474.3396476","DOIUrl":"https://doi.org/10.1145/3396474.3396476","url":null,"abstract":"For intelligent systems to become autonomous in any real sense, they need an ability to make decisions on situations that were not entirely conceived of at compile-time. Machine learning algorithms are excellent in mimicking the behaviour of some gold standard role model, and this can include decision making by the role model. But once out of familiar contexts, the decision making becomes harder and needs an element of more independent probabilistic reasoning and decision making. This paper presents such a method based on a belief mass interpretation of the decision information, where the components are imprecise and thus uncertain by means of intervals.","PeriodicalId":408084,"journal":{"name":"Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121004833","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}
Many optimisation problems have more than three objectives, referred to as many-objective optimisation problems (MaOPs). As the number of objectives increases, the number of solutions that are non-dominated with regards to one another also increases. Therefore, multi-objective optimisation algorithms (MOAs) that use Pareto-dominance struggle to converge to the Pareto-optimal front (POF) and to find a diverse set of solutions on the POF. This article investigates the use of MOAs to solve MaOPs by guiding the search through Pareto-dominance on three randomly selected objectives. This approach is applied to the non-dominated sorting genetic algorithm II (NSGA-II) and a multi-objective particle swarm optimisation (OMOPSO) algorithm, where three objectives are randomly selected at either every iteration or every five iterations. These algorithms are compared against the original versions of these algorithms. The results indicate that the proposed partial dominance approach outperformed the original versions of these algorithms, especially on benchmarks with 8 and 10 objectives.
{"title":"Partial Dominance for Many-Objective Optimization","authors":"Mardé Helbig, A. Engelbrecht","doi":"10.1145/3396474.3396482","DOIUrl":"https://doi.org/10.1145/3396474.3396482","url":null,"abstract":"Many optimisation problems have more than three objectives, referred to as many-objective optimisation problems (MaOPs). As the number of objectives increases, the number of solutions that are non-dominated with regards to one another also increases. Therefore, multi-objective optimisation algorithms (MOAs) that use Pareto-dominance struggle to converge to the Pareto-optimal front (POF) and to find a diverse set of solutions on the POF. This article investigates the use of MOAs to solve MaOPs by guiding the search through Pareto-dominance on three randomly selected objectives. This approach is applied to the non-dominated sorting genetic algorithm II (NSGA-II) and a multi-objective particle swarm optimisation (OMOPSO) algorithm, where three objectives are randomly selected at either every iteration or every five iterations. These algorithms are compared against the original versions of these algorithms. The results indicate that the proposed partial dominance approach outperformed the original versions of these algorithms, especially on benchmarks with 8 and 10 objectives.","PeriodicalId":408084,"journal":{"name":"Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132133294","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}
{"title":"Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","authors":"","doi":"10.1145/3396474","DOIUrl":"https://doi.org/10.1145/3396474","url":null,"abstract":"","PeriodicalId":408084,"journal":{"name":"Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131150726","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}
Oliver Faust, Carlo Mehli, T. Hanne, Rolf Dornberger
This paper discusses the combined application of two metaheuristic algorithms, a Genetic Algorithm (GA) and Ant Colony Optimization (ACO). The GA optimizes ACO parameters to find the optimal parameter settings automatically to solve a given Capacitated Vehicle Routing Problem (CVRP). The research design and the implemented prototype for this experiment are explained in detail and test results are presented. Optimal ACO parameters for the different CVRP are computed and analyzed and the reasonability of the proposed GA-ACO algorithm to solve CVRP is discussed.
{"title":"A Genetic Algorithm for Optimizing Parameters for Ant Colony Optimization Solving Capacitated Vehicle Routing Problems","authors":"Oliver Faust, Carlo Mehli, T. Hanne, Rolf Dornberger","doi":"10.1145/3396474.3396489","DOIUrl":"https://doi.org/10.1145/3396474.3396489","url":null,"abstract":"This paper discusses the combined application of two metaheuristic algorithms, a Genetic Algorithm (GA) and Ant Colony Optimization (ACO). The GA optimizes ACO parameters to find the optimal parameter settings automatically to solve a given Capacitated Vehicle Routing Problem (CVRP). The research design and the implemented prototype for this experiment are explained in detail and test results are presented. Optimal ACO parameters for the different CVRP are computed and analyzed and the reasonability of the proposed GA-ACO algorithm to solve CVRP is discussed.","PeriodicalId":408084,"journal":{"name":"Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"85 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122709578","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}
Item-based collaborative filtering is widely used in industry to build recommendation systems because of its explanatory and efficiency in personalized recommendation. However, item-based collaborative filtering is mostly a shallow linear model, which cannot well mine the complex relationship between items. Therefore, in this work we propose a item-based convolution collaborative filtering model (I_ConvCF). Using a convolution neural network to extract the nonlinear relationship characteristics of Historical interaction/non-interactive items as a low dimensional latent factor. The target item is regarded as another low dimensional latent factor, and their product is regarded as the feature of the target item. We demonstrate their superiority in personalized ranking tasks on two real data sets.
{"title":"I_ConvCF: Item-based Convolution Collaborative Filtering Recommendation","authors":"Chang Su, Tonglu Zhang, Xianzhong Xie","doi":"10.1145/3396474.3396497","DOIUrl":"https://doi.org/10.1145/3396474.3396497","url":null,"abstract":"Item-based collaborative filtering is widely used in industry to build recommendation systems because of its explanatory and efficiency in personalized recommendation. However, item-based collaborative filtering is mostly a shallow linear model, which cannot well mine the complex relationship between items. Therefore, in this work we propose a item-based convolution collaborative filtering model (I_ConvCF). Using a convolution neural network to extract the nonlinear relationship characteristics of Historical interaction/non-interactive items as a low dimensional latent factor. The target item is regarded as another low dimensional latent factor, and their product is regarded as the feature of the target item. We demonstrate their superiority in personalized ranking tasks on two real data sets.","PeriodicalId":408084,"journal":{"name":"Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134287725","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}
Cuckoo search is a bio-inspired algorithm based on the reproduction behavior of some cuckoo species. This metaheuristics seems promising to solve the capacitated vehicle routing problem. This paper analyzes the standard capacitated vehicle routing problem because the cuckoo search enables faster results with fewer parameters than other optimization algorithms. A new approach using a multi-threaded variant of the cuckoo search running on multiple CPU cores is being investigated, which allows the parallelization of optimization cycles. The approach uses a standard Java framework and takes into account multiple eggs per nest. A quantitative analysis investigates the new multi-threading variant compared to the standard one.
{"title":"A Multi-Threaded Cuckoo Search Algorithm for the Capacitated Vehicle Routing Problem","authors":"Dominik Troxler, T. Hanne, Rolf Dornberger","doi":"10.1145/3396474.3396487","DOIUrl":"https://doi.org/10.1145/3396474.3396487","url":null,"abstract":"Cuckoo search is a bio-inspired algorithm based on the reproduction behavior of some cuckoo species. This metaheuristics seems promising to solve the capacitated vehicle routing problem. This paper analyzes the standard capacitated vehicle routing problem because the cuckoo search enables faster results with fewer parameters than other optimization algorithms. A new approach using a multi-threaded variant of the cuckoo search running on multiple CPU cores is being investigated, which allows the parallelization of optimization cycles. The approach uses a standard Java framework and takes into account multiple eggs per nest. A quantitative analysis investigates the new multi-threading variant compared to the standard one.","PeriodicalId":408084,"journal":{"name":"Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114716075","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}
We consider a linear programming problem with uncertain input coefficients. The only information we have are lower and upper bounds for the uncertain values. This gives rise to the so called interval linear programming. The challenging problem here is to characterize and determine the set of all possible optimal solutions. Most of the scholars were focus on computing outer bounds for the optimal solution. Herein, we will be interested with computing inner bounds. We propose a local search algorithm and a genetic algorithm. We compare both methods numerically on random data to ascertain what is their real time complexity and quality of inner estimations.
{"title":"Two Approaches to Inner Estimations of the Optimal Solution Set in Interval Linear Programming","authors":"M. Hladík","doi":"10.1145/3396474.3396479","DOIUrl":"https://doi.org/10.1145/3396474.3396479","url":null,"abstract":"We consider a linear programming problem with uncertain input coefficients. The only information we have are lower and upper bounds for the uncertain values. This gives rise to the so called interval linear programming. The challenging problem here is to characterize and determine the set of all possible optimal solutions. Most of the scholars were focus on computing outer bounds for the optimal solution. Herein, we will be interested with computing inner bounds. We propose a local search algorithm and a genetic algorithm. We compare both methods numerically on random data to ascertain what is their real time complexity and quality of inner estimations.","PeriodicalId":408084,"journal":{"name":"Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123907220","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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