Representing and reasoning about time is fundamental in many applications of Artificial Intellige... more Representing and reasoning about time is fundamental in many applications of Artificial Intelligence as well as of other disciplines in computer science, such as scheduling, planning, computational linguistics, database design and molecular biology. The development of a domain-independent temporal reasoning system is then practically important. An important issue when designing such systems is the efficient handling of qualitative and metric time information. We have developed a temporal model, TemPro, based on the Allen interval algebra, to express and manage such information in terms of qualitative and quantitative temporal constraints. TemPro translates an application involving temporal information into a Constraint Satisfaction Problem (CSP). Constraint satisfaction techniques are then used to manage the different time information by solving the CSP. In order for the system to deal with real time applications or those applications where it is impossible or impractical to solve t...
We present a new framework, managing Constraint Satisfaction Problems (CSPs) with preferences in ... more We present a new framework, managing Constraint Satisfaction Problems (CSPs) with preferences in a dynamic environment. Unlike the existing CSP models managing one form of preferences, ours supports four types, namely: unary and binary constraint preferences, composite preferences and conditional preferences. This offers more expressive power in representing a wide variety of dynamic constraint applications under preferences and where the possible changes are known and available a priori. Conditional preferences allow some preference functions to be added dynamically to the problem, during the resolution process, if a given condition on some variables is true. A composite preference is a higher level of preference among the choices of a composite variable. Composite variables are variables whose possible values are CSP variables. In other words, this allows us to represent disjunctive CSP variables. The preferences are viewed as a set of soft constraints using the fuzzy CSP framewor...
A well known approach to managing the numeric and the symbolic aspects of time is to view them as... more A well known approach to managing the numeric and the symbolic aspects of time is to view them as Constraint Satisfaction Problems (CSPs). Our aim is to extend the temporal CSP formalism in order to include activity constraints and composite variables. Indeed, in many real life applications the set of variables involved by the temporal constraint problem to solve is not known in advance. More precisely, while some temporal variables (called events) are available in the initial problem, others are added dynamically to the problem during the resolution process via activity constraints and composite variables. Activity constraints allow some variables to be activated (added to the problem) when activity conditions are true. Composite variables are defined on finite domains of events. We propose in this paper two methods based respectively on constraint propagation and stochastic local search (SLS) for solving temporal constraint problems with activity constraints and composite variable...
In this paper we will present a study of different res-olution techniques for solving Constraint ... more In this paper we will present a study of different res-olution techniques for solving Constraint Satisfaction Problems (CSP) in the case of temporal constraints. This later problem is called Temporal Constraint Sat-isfaction Problem (TCSP). We will mainly focus here on solving TCSPs in real time and in a dynamic en-vironment. Indeed, addressing these two issues is very relevant for many real world applications. Solving a TCSP in real time is an optimization problem that we call MTCSP (Maximal Temporal Constraint Satisfac-tion Problems). The objective function to minimize is the number of temporal constraint violations. The re-sults of the tests we have performed on randomly gen-erated MTCSPs show that the approximation method Min-Conflict-Random-Walk(MCRW) is the algorithm of choice for solving MTCSPs. Comparison study of the different dynamic arc-consistency algorithms for solving dynamic temporal constraint problems in a pre-processing phase demonstrates that the new algorithm we ...
In this paper, we combine a novel Sequential Graph Coloring Heuristic Algorithm (SGCHA) with a no... more In this paper, we combine a novel Sequential Graph Coloring Heuristic Algorithm (SGCHA) with a non-systematic method based on a cultural algorithm to solve the graph coloring problem (GCP). The GCP involves finding the minimum number of colors for coloring the graph vertices such that adjacent vertices have distinct colors. In our solving approach, we first use an estimator which is implemented with SGCHA to predict the minimum colors. Then, in the non-systematic part which has been designed using cultural algorithms, we improve the prediction. Various components of the cultural algorithm have been implemented to solve the GCP with a self adaptive behavior in an efficient manner. As a result of utilizing the SGCHA and a cultural algorithm, the proposed method is capable of finding the solution in a very efficient running time. The experimental results show that the proposed algorithm has a high performance in time and quality of the solution returned for solving graph coloring insta...
A main challenge when designing constraint based systems in general and those involving temporal ... more A main challenge when designing constraint based systems in general and those involving temporal constraints in particular, is the ability to deal with constraints in a dynamic and evolutive environment. That is to check, anytime a new constraint is added, whether a consistent scenario continues to be consistent when a new constraint is added and if not, whether a new scenario satisfying the old and new constraints can be found. We talk then about on line temporal constraint based systems capable of reacting, in an efficient way, to any new external information during the constraint resolution process. In this paper, we will investigate the applicability of systematic versus approximation methods for solving incremental temporal constraint problems. In order to handle both numeric and symbolic constraints, the systematic method is based on constraint propagation performed at both the qualitative and quantitative levels. The approximation methods are respectively based on stochastic ...
Managing symbolic and metric temporal information is fundamental for many real world applications... more Managing symbolic and metric temporal information is fundamental for many real world applications such as scheduling, planning, data base design, computational linguistics and computational models for molecular biology. This motivates us to develop a temporal constraint solving system based on CSPs for handling the two types of temporal information. A main challenge when designing such systems is the ability to deal with temporal constraints in a dynamic and evolutive environment. That is to check, anytime a new constraint is added, whether a solution to the problem (consistent scenario) continues to be a solution when a new constraint is added and if not, whether a new solution satisfying the old and new constraints can be found. We talk then about on line temporal CSP-based systems capable of reacting, in an efficient way, to any new external information during the constraint resolution process. In this paper we will present three different techniques we use to tackle dynamic temp...
In preference-based constrained optimization problems, helping users by providing the most prefer... more In preference-based constrained optimization problems, helping users by providing the most preferable feasible outcome is crucial. The Lexicographic Preference Tree (LP- tree) and the Conditional Preference Network (CP-net) are two fundamental graphical models to represent and reason about user’s qualitative preferences. In this paper, we extend the LP- tree with a set of hard feasibility constraints, and then we propose a recursive backtrack search algorithm that we call Search-LP to find the most preferable feasible outcome for the Constrained LP-tree. Search-LP instantiates the variables with respect to a hierarchical order defined by the LP-tree. Given that the LP-tree represents a total order over the outcomes, Search-LP simply returns the first feasible outcome. We prove that this returned outcome is also preferable to every other feasible outcome. The main advantage of Search-LP is that it does not require dominance testing (the task of comparing two outcomes using preference...
There was a resurgent in research of neural nets during the late 70’s and 80’s due to advances ma... more There was a resurgent in research of neural nets during the late 70’s and 80’s due to advances made in learning algorithms for feed-forward and feedback networks. These advances, coupled with better computer technology, made it possible for practical applications of such networks to be made. In 1985, John Hopfield and David Tank first attempted using neural nets as an approximation method to solve optimization problems, mainly the Traveling Salesman Problem. Since then, there has been wide spread interest in applying neural nets to solve different types of optimization problems. In this paper we will mainly focus on using the Hopfield model to solve the Maximal Temporal Constraint Satisfaction Problem (MTCSP). An MTCSP is an optimization problem that consists of looking for a solution that satisfies the maximal number of temporal constraints. This can be the case of over constrained problems involving time constraints and where a complete solution does not exist, or those problems s...
Optimization and its related solving methods are becoming increasingly important in most academic... more Optimization and its related solving methods are becoming increasingly important in most academic and industrial fields. The goal of the optimization process is to make a system or a design as effective and functional as possible. This is achieved by optimizing a set of objectives while meeting the system requirements. Optimization techniques are classified into exact and approximate algorithms. Nature-inspired (NI) methods, a sub-class of approximate techniques, are widely recognized for providing efficient approaches for solving a wide variety of real-world optimization problems. In this paper, we discuss many scenarios where we can or cannot use different NI methods in tackling real-world optimization problems. We also enrich our survey with many studies for the reader to prove the efficiency and efficacy of using NI methods to tackle many real-world applications. Therefore, NI methods should be considered as alternative reliable approaches in the absence of exact methods to prov...
We present a new nature-inspired approach based on the Focus Group Optimization Algorithm (FGOA) ... more We present a new nature-inspired approach based on the Focus Group Optimization Algorithm (FGOA) for solving Constraint Satisfaction Problems (CSPs). CSPs are NP-complete problems meaning that solving them by classical systematic search methods requires exponential time, in theory. Appropriate alternatives are approximation methods such as metaheuristic algorithms which have shown successful results when solving combinatorial problems. FGOA is a new metaheuristic inspired by a human collaborative problem solving approach. In this paper, the steps of applying FGOA to CSPs are elaborated. More precisely, a new diversification method is devised to enable the algorithm to efficiently find solutions to CSPs, by escaping local optimum. To assess the performance of the proposed Discrete FGOA (DFGOA) in practice, we conducted several experiments on randomly generate hard to solve CSP instances (those near the phase transition) using the RB model. The results clearly show the ability of DFGO...
Cities are complex spatial systems and modeling their dynamics of growth using traditional modeli... more Cities are complex spatial systems and modeling their dynamics of growth using traditional modeling techniques is a challenging task. Cellular automata (CA) have been widely used for modeling urban growth because of their computational simplicity, their explicit representation of time and space and their ability to generate complex patterns from the interaction of simple components of the system using simple rules. Integrating GIS tools and remote sensing data with CA has the potential to provide realistic simulation of the future urban growth of cities. The proposed approach is applied to model the growth of the City of Montreal. Land use/land cover maps derived from Landsat data acquired in 1975 and 1990 were used to train a CA model which was then used to project the land use in 2005. A comparison of the projected and actual land uses for 2005 is presented and discussed.
Topic modeling is a powerful technique for unsupervised analysis of large document collections. T... more Topic modeling is a powerful technique for unsupervised analysis of large document collections. Topic models have a wide range of applications including tag recommendation, text categorization, keyword extraction and similarity search in the text mining, information retrieval and statistical language modeling. The research on topic modeling is gaining popularity day by day. There are various efficient topic modeling techniques available for the English language as it is one of the most spoken languages in the whole world but not for the other spoken languages. Bangla being the seventh most spoken native language in the world by population, it needs automation in different aspects. This paper deals with finding the core topics of Bangla news corpus and classifying news with similarity measures. The document models are built using LDA (Latent Dirichlet Allocation) with bigram.
International Journal of Computational Intelligence and Applications, 2016
We propose a Multi-Phase Hybrid Metaheuristics approach for solving the Exam Timetabling Problem ... more We propose a Multi-Phase Hybrid Metaheuristics approach for solving the Exam Timetabling Problem (ETP). This approach is defined with three phases: pre-processing phase, construction phase and enhancement phase. The pre-processing phase relies on our variable ordering heuristic as well as a form of transitive closure for discovering implicit constraints. The construction phase uses a variant of the Tabu Search with conflicts dictionary. The enhancement phase includes Hill Climbing (HC), Simulated Annealing (SA) and our updated version of the extended “Great Deluge” algorithm. In order to evaluate the performance of the different phases of our proposed approach, we conducted several experiments on instances taken from ITC 2007 benchmarking datasets. The results are very promising and competitive with the well known ETP solvers.
In order to automate the extraction of the criteria and values applied in Land Use Suitability An... more In order to automate the extraction of the criteria and values applied in Land Use Suitability Analysis (LUSA), we developed an Ontology-Based Information Extraction (OBIE) system to extract the required information from bylaw and regulation documents related to the geographic area of interest. The results obtained by our proposed LUSA OBIE system (land use suitability criteria and their values) are presented as an ontology populated with instances of the extracted criteria and property values. This latter output ontology is incorporated into a Multi-Criteria Decision Making (MCDM) model applied for constructing suitability maps for different kinds of land uses. The resulting maps may be the final desired product or can be incorporated into the cellular automata urban modeling and simulation for predicting future urban growth. A case study has been conducted where the output from LUSA OBIE is applied to help produce a suitability map for the City of Regina, Saskatchewan, to assist in the identification of suitable areas for residential development. A set of Saskatchewan bylaw and regulation documents were downloaded and input to the LUSA OBIE system. We accessed the extracted information using both the populated LUSA ontology and the set of annotated documents.
TCP-nets are graphical tools for modeling user's preference and relative importance statement... more TCP-nets are graphical tools for modeling user's preference and relative importance statements. We propose the Prob-abilistic TCP-net (PTCP-net) model that can aggregate a set of TCP-nets, in a compact form, sharing the same set of variables and their domains but having different preference and relative importance statements. In particular, the PTCP-net is able to aggregate the choices of multiple users such as, in recommender systems. The PTCP-net can also be seen as an extension of the TCP-net with uncertainty on preference and relative importance statements. We adopt the Bayesian Network as the reasoning tool for PTCP-nets especially when answering the following two queries (1) finding the most probable TCP-net and (2) finding the most probable optimal outcome. We also show that the PTCP-net is applicable in collaborative filtering type recommender systems.
2018 IEEE Congress on Evolutionary Computation (CEC)
Constraint Satisfaction Problems are regarded as NP-Complete problems which solving them with sys... more Constraint Satisfaction Problems are regarded as NP-Complete problems which solving them with systematic methods requires exponential time. Firefly algorithm is a nature inspired algorithm which has been successfully applied to different combinatorial problems. This paper presents a new Discrete Firefly Algorithm for Solving Constraint Satisfaction problems (CSPs) and investigates its applicability for dealing with such problems. Performance of the proposed method has been assessed through extensive experiments on CSP instances generated by Model RB which is a standard mean for generating CSPs with different tightness. Results of the experiments in comparison with other methods including classical methods and other metaheuristic methods clearly demonstrate the significant performance of proposed discrete firefly algorithm in dealing with CSPs.
The 31st International Conference on Industrial, Engineering and Other Applications of Applied In... more The 31st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE-2018) was held at Concordia University in Montreal, Canada, June 25–28, 2018. This report summarizes the The 31st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE-2018) was held at Concordia University in Montreal, Canada, June 25–28, 2018. IEA/AIE 2018 continued the tradition of emphasizing on applications of applied intelligent systems to solve real-life problems in all areas including engineering, science, industry, automation a robotics, business and finance, medicine and biomedicine, bioinformatics, cyberspace, and human-machine interactions.
Representing and reasoning about time is fundamental in many applications of Artificial Intellige... more Representing and reasoning about time is fundamental in many applications of Artificial Intelligence as well as of other disciplines in computer science, such as scheduling, planning, computational linguistics, database design and molecular biology. The development of a domain-independent temporal reasoning system is then practically important. An important issue when designing such systems is the efficient handling of qualitative and metric time information. We have developed a temporal model, TemPro, based on the Allen interval algebra, to express and manage such information in terms of qualitative and quantitative temporal constraints. TemPro translates an application involving temporal information into a Constraint Satisfaction Problem (CSP). Constraint satisfaction techniques are then used to manage the different time information by solving the CSP. In order for the system to deal with real time applications or those applications where it is impossible or impractical to solve t...
We present a new framework, managing Constraint Satisfaction Problems (CSPs) with preferences in ... more We present a new framework, managing Constraint Satisfaction Problems (CSPs) with preferences in a dynamic environment. Unlike the existing CSP models managing one form of preferences, ours supports four types, namely: unary and binary constraint preferences, composite preferences and conditional preferences. This offers more expressive power in representing a wide variety of dynamic constraint applications under preferences and where the possible changes are known and available a priori. Conditional preferences allow some preference functions to be added dynamically to the problem, during the resolution process, if a given condition on some variables is true. A composite preference is a higher level of preference among the choices of a composite variable. Composite variables are variables whose possible values are CSP variables. In other words, this allows us to represent disjunctive CSP variables. The preferences are viewed as a set of soft constraints using the fuzzy CSP framewor...
A well known approach to managing the numeric and the symbolic aspects of time is to view them as... more A well known approach to managing the numeric and the symbolic aspects of time is to view them as Constraint Satisfaction Problems (CSPs). Our aim is to extend the temporal CSP formalism in order to include activity constraints and composite variables. Indeed, in many real life applications the set of variables involved by the temporal constraint problem to solve is not known in advance. More precisely, while some temporal variables (called events) are available in the initial problem, others are added dynamically to the problem during the resolution process via activity constraints and composite variables. Activity constraints allow some variables to be activated (added to the problem) when activity conditions are true. Composite variables are defined on finite domains of events. We propose in this paper two methods based respectively on constraint propagation and stochastic local search (SLS) for solving temporal constraint problems with activity constraints and composite variable...
In this paper we will present a study of different res-olution techniques for solving Constraint ... more In this paper we will present a study of different res-olution techniques for solving Constraint Satisfaction Problems (CSP) in the case of temporal constraints. This later problem is called Temporal Constraint Sat-isfaction Problem (TCSP). We will mainly focus here on solving TCSPs in real time and in a dynamic en-vironment. Indeed, addressing these two issues is very relevant for many real world applications. Solving a TCSP in real time is an optimization problem that we call MTCSP (Maximal Temporal Constraint Satisfac-tion Problems). The objective function to minimize is the number of temporal constraint violations. The re-sults of the tests we have performed on randomly gen-erated MTCSPs show that the approximation method Min-Conflict-Random-Walk(MCRW) is the algorithm of choice for solving MTCSPs. Comparison study of the different dynamic arc-consistency algorithms for solving dynamic temporal constraint problems in a pre-processing phase demonstrates that the new algorithm we ...
In this paper, we combine a novel Sequential Graph Coloring Heuristic Algorithm (SGCHA) with a no... more In this paper, we combine a novel Sequential Graph Coloring Heuristic Algorithm (SGCHA) with a non-systematic method based on a cultural algorithm to solve the graph coloring problem (GCP). The GCP involves finding the minimum number of colors for coloring the graph vertices such that adjacent vertices have distinct colors. In our solving approach, we first use an estimator which is implemented with SGCHA to predict the minimum colors. Then, in the non-systematic part which has been designed using cultural algorithms, we improve the prediction. Various components of the cultural algorithm have been implemented to solve the GCP with a self adaptive behavior in an efficient manner. As a result of utilizing the SGCHA and a cultural algorithm, the proposed method is capable of finding the solution in a very efficient running time. The experimental results show that the proposed algorithm has a high performance in time and quality of the solution returned for solving graph coloring insta...
A main challenge when designing constraint based systems in general and those involving temporal ... more A main challenge when designing constraint based systems in general and those involving temporal constraints in particular, is the ability to deal with constraints in a dynamic and evolutive environment. That is to check, anytime a new constraint is added, whether a consistent scenario continues to be consistent when a new constraint is added and if not, whether a new scenario satisfying the old and new constraints can be found. We talk then about on line temporal constraint based systems capable of reacting, in an efficient way, to any new external information during the constraint resolution process. In this paper, we will investigate the applicability of systematic versus approximation methods for solving incremental temporal constraint problems. In order to handle both numeric and symbolic constraints, the systematic method is based on constraint propagation performed at both the qualitative and quantitative levels. The approximation methods are respectively based on stochastic ...
Managing symbolic and metric temporal information is fundamental for many real world applications... more Managing symbolic and metric temporal information is fundamental for many real world applications such as scheduling, planning, data base design, computational linguistics and computational models for molecular biology. This motivates us to develop a temporal constraint solving system based on CSPs for handling the two types of temporal information. A main challenge when designing such systems is the ability to deal with temporal constraints in a dynamic and evolutive environment. That is to check, anytime a new constraint is added, whether a solution to the problem (consistent scenario) continues to be a solution when a new constraint is added and if not, whether a new solution satisfying the old and new constraints can be found. We talk then about on line temporal CSP-based systems capable of reacting, in an efficient way, to any new external information during the constraint resolution process. In this paper we will present three different techniques we use to tackle dynamic temp...
In preference-based constrained optimization problems, helping users by providing the most prefer... more In preference-based constrained optimization problems, helping users by providing the most preferable feasible outcome is crucial. The Lexicographic Preference Tree (LP- tree) and the Conditional Preference Network (CP-net) are two fundamental graphical models to represent and reason about user’s qualitative preferences. In this paper, we extend the LP- tree with a set of hard feasibility constraints, and then we propose a recursive backtrack search algorithm that we call Search-LP to find the most preferable feasible outcome for the Constrained LP-tree. Search-LP instantiates the variables with respect to a hierarchical order defined by the LP-tree. Given that the LP-tree represents a total order over the outcomes, Search-LP simply returns the first feasible outcome. We prove that this returned outcome is also preferable to every other feasible outcome. The main advantage of Search-LP is that it does not require dominance testing (the task of comparing two outcomes using preference...
There was a resurgent in research of neural nets during the late 70’s and 80’s due to advances ma... more There was a resurgent in research of neural nets during the late 70’s and 80’s due to advances made in learning algorithms for feed-forward and feedback networks. These advances, coupled with better computer technology, made it possible for practical applications of such networks to be made. In 1985, John Hopfield and David Tank first attempted using neural nets as an approximation method to solve optimization problems, mainly the Traveling Salesman Problem. Since then, there has been wide spread interest in applying neural nets to solve different types of optimization problems. In this paper we will mainly focus on using the Hopfield model to solve the Maximal Temporal Constraint Satisfaction Problem (MTCSP). An MTCSP is an optimization problem that consists of looking for a solution that satisfies the maximal number of temporal constraints. This can be the case of over constrained problems involving time constraints and where a complete solution does not exist, or those problems s...
Optimization and its related solving methods are becoming increasingly important in most academic... more Optimization and its related solving methods are becoming increasingly important in most academic and industrial fields. The goal of the optimization process is to make a system or a design as effective and functional as possible. This is achieved by optimizing a set of objectives while meeting the system requirements. Optimization techniques are classified into exact and approximate algorithms. Nature-inspired (NI) methods, a sub-class of approximate techniques, are widely recognized for providing efficient approaches for solving a wide variety of real-world optimization problems. In this paper, we discuss many scenarios where we can or cannot use different NI methods in tackling real-world optimization problems. We also enrich our survey with many studies for the reader to prove the efficiency and efficacy of using NI methods to tackle many real-world applications. Therefore, NI methods should be considered as alternative reliable approaches in the absence of exact methods to prov...
We present a new nature-inspired approach based on the Focus Group Optimization Algorithm (FGOA) ... more We present a new nature-inspired approach based on the Focus Group Optimization Algorithm (FGOA) for solving Constraint Satisfaction Problems (CSPs). CSPs are NP-complete problems meaning that solving them by classical systematic search methods requires exponential time, in theory. Appropriate alternatives are approximation methods such as metaheuristic algorithms which have shown successful results when solving combinatorial problems. FGOA is a new metaheuristic inspired by a human collaborative problem solving approach. In this paper, the steps of applying FGOA to CSPs are elaborated. More precisely, a new diversification method is devised to enable the algorithm to efficiently find solutions to CSPs, by escaping local optimum. To assess the performance of the proposed Discrete FGOA (DFGOA) in practice, we conducted several experiments on randomly generate hard to solve CSP instances (those near the phase transition) using the RB model. The results clearly show the ability of DFGO...
Cities are complex spatial systems and modeling their dynamics of growth using traditional modeli... more Cities are complex spatial systems and modeling their dynamics of growth using traditional modeling techniques is a challenging task. Cellular automata (CA) have been widely used for modeling urban growth because of their computational simplicity, their explicit representation of time and space and their ability to generate complex patterns from the interaction of simple components of the system using simple rules. Integrating GIS tools and remote sensing data with CA has the potential to provide realistic simulation of the future urban growth of cities. The proposed approach is applied to model the growth of the City of Montreal. Land use/land cover maps derived from Landsat data acquired in 1975 and 1990 were used to train a CA model which was then used to project the land use in 2005. A comparison of the projected and actual land uses for 2005 is presented and discussed.
Topic modeling is a powerful technique for unsupervised analysis of large document collections. T... more Topic modeling is a powerful technique for unsupervised analysis of large document collections. Topic models have a wide range of applications including tag recommendation, text categorization, keyword extraction and similarity search in the text mining, information retrieval and statistical language modeling. The research on topic modeling is gaining popularity day by day. There are various efficient topic modeling techniques available for the English language as it is one of the most spoken languages in the whole world but not for the other spoken languages. Bangla being the seventh most spoken native language in the world by population, it needs automation in different aspects. This paper deals with finding the core topics of Bangla news corpus and classifying news with similarity measures. The document models are built using LDA (Latent Dirichlet Allocation) with bigram.
International Journal of Computational Intelligence and Applications, 2016
We propose a Multi-Phase Hybrid Metaheuristics approach for solving the Exam Timetabling Problem ... more We propose a Multi-Phase Hybrid Metaheuristics approach for solving the Exam Timetabling Problem (ETP). This approach is defined with three phases: pre-processing phase, construction phase and enhancement phase. The pre-processing phase relies on our variable ordering heuristic as well as a form of transitive closure for discovering implicit constraints. The construction phase uses a variant of the Tabu Search with conflicts dictionary. The enhancement phase includes Hill Climbing (HC), Simulated Annealing (SA) and our updated version of the extended “Great Deluge” algorithm. In order to evaluate the performance of the different phases of our proposed approach, we conducted several experiments on instances taken from ITC 2007 benchmarking datasets. The results are very promising and competitive with the well known ETP solvers.
In order to automate the extraction of the criteria and values applied in Land Use Suitability An... more In order to automate the extraction of the criteria and values applied in Land Use Suitability Analysis (LUSA), we developed an Ontology-Based Information Extraction (OBIE) system to extract the required information from bylaw and regulation documents related to the geographic area of interest. The results obtained by our proposed LUSA OBIE system (land use suitability criteria and their values) are presented as an ontology populated with instances of the extracted criteria and property values. This latter output ontology is incorporated into a Multi-Criteria Decision Making (MCDM) model applied for constructing suitability maps for different kinds of land uses. The resulting maps may be the final desired product or can be incorporated into the cellular automata urban modeling and simulation for predicting future urban growth. A case study has been conducted where the output from LUSA OBIE is applied to help produce a suitability map for the City of Regina, Saskatchewan, to assist in the identification of suitable areas for residential development. A set of Saskatchewan bylaw and regulation documents were downloaded and input to the LUSA OBIE system. We accessed the extracted information using both the populated LUSA ontology and the set of annotated documents.
TCP-nets are graphical tools for modeling user's preference and relative importance statement... more TCP-nets are graphical tools for modeling user's preference and relative importance statements. We propose the Prob-abilistic TCP-net (PTCP-net) model that can aggregate a set of TCP-nets, in a compact form, sharing the same set of variables and their domains but having different preference and relative importance statements. In particular, the PTCP-net is able to aggregate the choices of multiple users such as, in recommender systems. The PTCP-net can also be seen as an extension of the TCP-net with uncertainty on preference and relative importance statements. We adopt the Bayesian Network as the reasoning tool for PTCP-nets especially when answering the following two queries (1) finding the most probable TCP-net and (2) finding the most probable optimal outcome. We also show that the PTCP-net is applicable in collaborative filtering type recommender systems.
2018 IEEE Congress on Evolutionary Computation (CEC)
Constraint Satisfaction Problems are regarded as NP-Complete problems which solving them with sys... more Constraint Satisfaction Problems are regarded as NP-Complete problems which solving them with systematic methods requires exponential time. Firefly algorithm is a nature inspired algorithm which has been successfully applied to different combinatorial problems. This paper presents a new Discrete Firefly Algorithm for Solving Constraint Satisfaction problems (CSPs) and investigates its applicability for dealing with such problems. Performance of the proposed method has been assessed through extensive experiments on CSP instances generated by Model RB which is a standard mean for generating CSPs with different tightness. Results of the experiments in comparison with other methods including classical methods and other metaheuristic methods clearly demonstrate the significant performance of proposed discrete firefly algorithm in dealing with CSPs.
The 31st International Conference on Industrial, Engineering and Other Applications of Applied In... more The 31st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE-2018) was held at Concordia University in Montreal, Canada, June 25–28, 2018. This report summarizes the The 31st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE-2018) was held at Concordia University in Montreal, Canada, June 25–28, 2018. IEA/AIE 2018 continued the tradition of emphasizing on applications of applied intelligent systems to solve real-life problems in all areas including engineering, science, industry, automation a robotics, business and finance, medicine and biomedicine, bioinformatics, cyberspace, and human-machine interactions.
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Papers by Malek Mouhoub