Pub Date : 2014-12-12DOI: 10.1109/MCDM.2014.7007194
Hong Zhou, Jie Lu, Guangquan Zhang
Linear bilevel programming has been studied for many years and applied in different domains such as transportation, economics, engineering, environment, and telecommunications. However, there is lack of attention of the impacts on dynamic decision making with abrupt or unusual events caused by unpredictable natural environment or human activities (e.g. Tsunami, earthquake, and malicious or terrorist attacks). In reality these events could happens more often and have more significant impacts on decision making in an increasingly complex and dynamic world. This paper addresses this unique problem by introducing a concept of Virtual Follower (VF). An extended model of bilevel multi-follower programming with a virtual follower (BLMFP-VF) is defined and the kth-best algorithm for solving this problem is proposed. An example is given to illustrate the working of the extended model and approach.
{"title":"An extended bilevel programming model and its kth-best algorithm for dynamic decision making in emergency situations","authors":"Hong Zhou, Jie Lu, Guangquan Zhang","doi":"10.1109/MCDM.2014.7007194","DOIUrl":"https://doi.org/10.1109/MCDM.2014.7007194","url":null,"abstract":"Linear bilevel programming has been studied for many years and applied in different domains such as transportation, economics, engineering, environment, and telecommunications. However, there is lack of attention of the impacts on dynamic decision making with abrupt or unusual events caused by unpredictable natural environment or human activities (e.g. Tsunami, earthquake, and malicious or terrorist attacks). In reality these events could happens more often and have more significant impacts on decision making in an increasingly complex and dynamic world. This paper addresses this unique problem by introducing a concept of Virtual Follower (VF). An extended model of bilevel multi-follower programming with a virtual follower (BLMFP-VF) is defined and the kth-best algorithm for solving this problem is proposed. An example is given to illustrate the working of the extended model and approach.","PeriodicalId":335170,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121817059","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 : 2014-12-01DOI: 10.1109/MCDM.2014.7007202
K. Ortiz, Jean-Michel Richer, D. Lesaint, E. Rodriguez-Tello
In this article we describe a bottom-up implementation of Path-Relinking for Phylogenetic Trees in the context of the resolution of the Maximum Parsimony problem with Fitch optimality criterion. This bottom-up implementation is compared to two versions of an existing top-down implementation. We show that our implementation is more efficient, more interesting to compare trees and to give an estimation of the distance between two trees in terms of the number of transformations.
{"title":"A bottom-up implementation of Path-Relinking for Phylogenetic reconstruction applied to Maximum Parsimony","authors":"K. Ortiz, Jean-Michel Richer, D. Lesaint, E. Rodriguez-Tello","doi":"10.1109/MCDM.2014.7007202","DOIUrl":"https://doi.org/10.1109/MCDM.2014.7007202","url":null,"abstract":"In this article we describe a bottom-up implementation of Path-Relinking for Phylogenetic Trees in the context of the resolution of the Maximum Parsimony problem with Fitch optimality criterion. This bottom-up implementation is compared to two versions of an existing top-down implementation. We show that our implementation is more efficient, more interesting to compare trees and to give an estimation of the distance between two trees in terms of the number of transformations.","PeriodicalId":335170,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115483011","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 : 2014-12-01DOI: 10.1109/MCDM.2014.7007187
Mathias Ngo, R. Labayrade
The first step of any optimization process consists in choosing the Decision Variables (DV) and its relationships that model the problem, system or object to optimize. Many problems cannot be represented by a unique, exhaustive model which would ensure a global best result: in those cases, the model (DV and relationships) choice matters on the quality of the results.
{"title":"Multi-Genomic Algorithms","authors":"Mathias Ngo, R. Labayrade","doi":"10.1109/MCDM.2014.7007187","DOIUrl":"https://doi.org/10.1109/MCDM.2014.7007187","url":null,"abstract":"The first step of any optimization process consists in choosing the Decision Variables (DV) and its relationships that model the problem, system or object to optimize. Many problems cannot be represented by a unique, exhaustive model which would ensure a global best result: in those cases, the model (DV and relationships) choice matters on the quality of the results.","PeriodicalId":335170,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126517465","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 : 2014-12-01DOI: 10.1109/MCDM.2014.7007200
Charalampos N. Moschopoulos, D. Popovic, R. Langone, J. Suykens, B. Moor, Y. Moreau
In recent years, the advent of high-throughput techniques led to significant acceleration of biomarker discovery. In the same time, the popularity of machine learning methods grown in the field, mostly due to inherit analytical problems associated with the data resulting from these massively parallelized experiments. However, learning algorithms are very often utilized in their basic form, hence sometimes failing to consider interactions that are present between biological subjects (i.e. genes). In this context, we propose a new methodology, based on genetic algorithms, that integrates prior information through a novel genetic operator. In this particular application, we rely on a biological knowledge that is captured by the gene interaction networks. We demonstrate the advantageous performance of our method compared to a simple genetic algorithm by testing it on several microarray datasets containing samples of tissue from cancer patients. The obtained results suggest that inclusion of biological knowledge into genetic algorithm in the form of this operator can boost its effectiveness in the biomarker discovery problem.
{"title":"Gene interaction networks boost genetic algorithm performance in biomarker discovery","authors":"Charalampos N. Moschopoulos, D. Popovic, R. Langone, J. Suykens, B. Moor, Y. Moreau","doi":"10.1109/MCDM.2014.7007200","DOIUrl":"https://doi.org/10.1109/MCDM.2014.7007200","url":null,"abstract":"In recent years, the advent of high-throughput techniques led to significant acceleration of biomarker discovery. In the same time, the popularity of machine learning methods grown in the field, mostly due to inherit analytical problems associated with the data resulting from these massively parallelized experiments. However, learning algorithms are very often utilized in their basic form, hence sometimes failing to consider interactions that are present between biological subjects (i.e. genes). In this context, we propose a new methodology, based on genetic algorithms, that integrates prior information through a novel genetic operator. In this particular application, we rely on a biological knowledge that is captured by the gene interaction networks. We demonstrate the advantageous performance of our method compared to a simple genetic algorithm by testing it on several microarray datasets containing samples of tissue from cancer patients. The obtained results suggest that inclusion of biological knowledge into genetic algorithm in the form of this operator can boost its effectiveness in the biomarker discovery problem.","PeriodicalId":335170,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117056752","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 : 2014-12-01DOI: 10.1109/MCDM.2014.7007197
I. Alolyan
In many real-life situations, we come across problems with imprecise input values. Imprecisions are dealt with by various ways. One of them is interval based approach in which we model imprecise quantities by intervals, and suppose that the quantities may vary independently and simultaneously within their intervals. In most optimization problems, they are formulated using imprecise parameters. Such parameters can be considered as fuzzy intervals, and the optimization tasks with interval cost function are obtained. When realistic problems are formulated, a set of intervals may appear as coefficients in the objective function or the constraints of a linear programming problem. In this paper, we introduce a new method for solving linear optimization problems with interval parameters in the objective function and the inequality constraints, and we show the efficiency of the proposed method by presenting a numerical example.
{"title":"Interval linear optimization problems with fuzzy inequality constraints","authors":"I. Alolyan","doi":"10.1109/MCDM.2014.7007197","DOIUrl":"https://doi.org/10.1109/MCDM.2014.7007197","url":null,"abstract":"In many real-life situations, we come across problems with imprecise input values. Imprecisions are dealt with by various ways. One of them is interval based approach in which we model imprecise quantities by intervals, and suppose that the quantities may vary independently and simultaneously within their intervals. In most optimization problems, they are formulated using imprecise parameters. Such parameters can be considered as fuzzy intervals, and the optimization tasks with interval cost function are obtained. When realistic problems are formulated, a set of intervals may appear as coefficients in the objective function or the constraints of a linear programming problem. In this paper, we introduce a new method for solving linear optimization problems with interval parameters in the objective function and the inequality constraints, and we show the efficiency of the proposed method by presenting a numerical example.","PeriodicalId":335170,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM)","volume":"44 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120896639","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 : 2014-12-01DOI: 10.1109/MCDM.2014.7007195
Djamalladine Mahamat Pierre, M. N. Zakaria
The complexity of the Vehicle Routing Problems (VRPs) and their applications in our day to day life has garnered a lot of attentions in the area of optimization. Recently, attentions have turned to multi-objective VRPs with Multi-Objective Genetic Algorithms (MOGAs). MOGAs, thanks to its genetic operators such as selection, crossover, and/or mutation, constantly modify a population of solutions in order to find optimal solutions. However, given the complexity of VRPs, conventional crossover operators have major drawbacks. The Best Cost Route Crossover is lately gaining popularity in solving multi-objective VRPs. It employs a brute force approach to generate new children. Such approach may be unacceptable when presented with a relatively large problem instance. In this paper, we introduce a new crossover operator, called Partially Optimized Cyclic Shift Crossover (POCSX). A comparative study, between a MOGA based on POCSX, and a MOGA which is based on the Best Cost Route Crossover affirms the level of competitiveness of the former.
{"title":"Partially Optimized Cyclic Shift Crossover for Multi-Objective Genetic Algorithms for the multi-objective Vehicle Routing Problem with time-windows","authors":"Djamalladine Mahamat Pierre, M. N. Zakaria","doi":"10.1109/MCDM.2014.7007195","DOIUrl":"https://doi.org/10.1109/MCDM.2014.7007195","url":null,"abstract":"The complexity of the Vehicle Routing Problems (VRPs) and their applications in our day to day life has garnered a lot of attentions in the area of optimization. Recently, attentions have turned to multi-objective VRPs with Multi-Objective Genetic Algorithms (MOGAs). MOGAs, thanks to its genetic operators such as selection, crossover, and/or mutation, constantly modify a population of solutions in order to find optimal solutions. However, given the complexity of VRPs, conventional crossover operators have major drawbacks. The Best Cost Route Crossover is lately gaining popularity in solving multi-objective VRPs. It employs a brute force approach to generate new children. Such approach may be unacceptable when presented with a relatively large problem instance. In this paper, we introduce a new crossover operator, called Partially Optimized Cyclic Shift Crossover (POCSX). A comparative study, between a MOGA based on POCSX, and a MOGA which is based on the Best Cost Route Crossover affirms the level of competitiveness of the former.","PeriodicalId":335170,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117106204","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 : 2014-12-01DOI: 10.1109/MCDM.2014.7007186
Edward Abel, L. Mikhailov, J. Keane
Within a large group of decision makers, varying amounts of both conflicting and harmonious views will be prevalent within the group, but obscured due to group size. When the number of Decision Makers is large, utilizing clustering during the process of aggregation of their views should aid both knowledge discovery - about the group's conflict and consensus - as well as helping to streamline the aggregation process to reach a group consensus. We conjecture that this can be realized by using the similarity of views of a large group of decision makers to define clusters of analogous opinions. From each cluster of decision makers, a representation of the views of its members can then be sought. This set of representations can then be utilized for aggregation to help reach a final whole group consensus.
{"title":"Clustering Decision Makers with respect to similarity of views","authors":"Edward Abel, L. Mikhailov, J. Keane","doi":"10.1109/MCDM.2014.7007186","DOIUrl":"https://doi.org/10.1109/MCDM.2014.7007186","url":null,"abstract":"Within a large group of decision makers, varying amounts of both conflicting and harmonious views will be prevalent within the group, but obscured due to group size. When the number of Decision Makers is large, utilizing clustering during the process of aggregation of their views should aid both knowledge discovery - about the group's conflict and consensus - as well as helping to streamline the aggregation process to reach a group consensus. We conjecture that this can be realized by using the similarity of views of a large group of decision makers to define clusters of analogous opinions. From each cluster of decision makers, a representation of the views of its members can then be sought. This set of representations can then be utilized for aggregation to help reach a final whole group consensus.","PeriodicalId":335170,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130015277","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 : 2014-12-01DOI: 10.1109/MCDM.2014.7007188
J. T. Rickard, J. Aisbett
We introduce a fuzzy neural model which is more intuitive and general than the traditional weighted sum/squashing function neuron model. Positively and negatively causal inputs are separately aggregated using operators that are selected to suit the particular application. The aggregations are then combined using a simple arithmetic transformation. We outline the computational process when inputs and importance weights are vocabulary words modelled as interval type-2 fuzzy sets, and illustrate on predictions of gold price changes.
{"title":"A perceptual fuzzy neural model","authors":"J. T. Rickard, J. Aisbett","doi":"10.1109/MCDM.2014.7007188","DOIUrl":"https://doi.org/10.1109/MCDM.2014.7007188","url":null,"abstract":"We introduce a fuzzy neural model which is more intuitive and general than the traditional weighted sum/squashing function neuron model. Positively and negatively causal inputs are separately aggregated using operators that are selected to suit the particular application. The aggregations are then combined using a simple arithmetic transformation. We outline the computational process when inputs and importance weights are vocabulary words modelled as interval type-2 fuzzy sets, and illustrate on predictions of gold price changes.","PeriodicalId":335170,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121983872","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 : 2014-12-01DOI: 10.1109/MCDM.2014.7007208
Emmanuel Kieffer, A. Stathakis, Grégoire Danoy, P. Bouvry, E. Talbi, G. Morelli
Today's world is a vast network of global communications systems in which satellites provide high-performance and long distance communications. Satellites are able to forward signals after amplification to offer a high level of service to customers. These signals are composed of many different channel frequencies continuously carrying real-time data feeds. Nevertheless, the increasing demands of the market force satellite operators to develop efficient approaches to manage satellite configurations, in which power transmission is one crucial criterion. Not only the signal power sent to the satellite needs to be optimal to avoid large costs but also the power of the downlink signal has to be strong enough to ensure the quality of service. In this work, we tackle for the first time the bi-objective input/output power problem with multi-objective evolutionary algorithms to discover efficient solutions. A problem specific indirect encoding is proposed and the performance of three state-of-the-art multi-objective evolutionary algorithms, i.e. NSGA-II, SPEA2 and MOCell, is compared on real satellite payload instances.
{"title":"Multi-objective evolutionary approach for the satellite payload power optimization problem","authors":"Emmanuel Kieffer, A. Stathakis, Grégoire Danoy, P. Bouvry, E. Talbi, G. Morelli","doi":"10.1109/MCDM.2014.7007208","DOIUrl":"https://doi.org/10.1109/MCDM.2014.7007208","url":null,"abstract":"Today's world is a vast network of global communications systems in which satellites provide high-performance and long distance communications. Satellites are able to forward signals after amplification to offer a high level of service to customers. These signals are composed of many different channel frequencies continuously carrying real-time data feeds. Nevertheless, the increasing demands of the market force satellite operators to develop efficient approaches to manage satellite configurations, in which power transmission is one crucial criterion. Not only the signal power sent to the satellite needs to be optimal to avoid large costs but also the power of the downlink signal has to be strong enough to ensure the quality of service. In this work, we tackle for the first time the bi-objective input/output power problem with multi-objective evolutionary algorithms to discover efficient solutions. A problem specific indirect encoding is proposed and the performance of three state-of-the-art multi-objective evolutionary algorithms, i.e. NSGA-II, SPEA2 and MOCell, is compared on real satellite payload instances.","PeriodicalId":335170,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127394083","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 : 2014-12-01DOI: 10.1109/MCDM.2014.7007183
A. Gaspar-Cunha, J. Ferreira, J. Covas, Gustavo Recio
A multidisciplinary design an optimization framework based on the use of multi-objective evolutionary algorithms, together with decision making and robustness strategies, was used to optimize the polymer extrusion process. This methodology was applied with the aim to select the best solutions from the Pareto set in a multi-objective environment. The application to a complex polymer extrusion case study demonstrated the validity and usefulness of the approach.
{"title":"Selection of solutions in multi-objective optimization: Decision making and robustness","authors":"A. Gaspar-Cunha, J. Ferreira, J. Covas, Gustavo Recio","doi":"10.1109/MCDM.2014.7007183","DOIUrl":"https://doi.org/10.1109/MCDM.2014.7007183","url":null,"abstract":"A multidisciplinary design an optimization framework based on the use of multi-objective evolutionary algorithms, together with decision making and robustness strategies, was used to optimize the polymer extrusion process. This methodology was applied with the aim to select the best solutions from the Pareto set in a multi-objective environment. The application to a complex polymer extrusion case study demonstrated the validity and usefulness of the approach.","PeriodicalId":335170,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128361218","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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