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8th International Conference on Intelligent Systems - Theories and Applications (SITA)

作者： Ettaouil, Mohamed Haddouch, Khalid Hami, Youssef Loqman, Chakir Univ Sidi Mohammed ben Abdellah UFR Sci Comp & Comp Sci Engn Sci Modeling & Sci Comp Lab Fac Sci & Technol Fez Box 2202 Fes Morocco Moulay Ismail Univ High Sch Technol Dept Comp Engn Toulal 50000 Morocco

ISBN: (纸本)9781479902996

In this paper, we propose a new approach to solve the maximal constraint satisfaction problems (Max-CSP) using the continuous Hopfield network. This approach is divided into two steps: the first step involves modeling the maximal constraint satisfaction problem as 0-1 quadratic programming subject to linear constraints (QP). The second step concerns applying the continuous Hopfield network (CHN) to solve the QP problem. Therefore, the generalized energy function associated to the CHN and an appropriate parameter-setting procedure about Max-CSP problems are given in detail. Finally, the proposed algorithm and some computational experiments solving the Max-CSP are shown.

关键词： component maximal constraint satisfaction problems quadratic 0-1 programming continuous Hopfield network energy function

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Neural networks approach for solving the maximal constraint satisfaction problems

Neural networks approach for solving the Maximal Constraint ...

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International Conference on Intelligent Systems: Theories and Applications

作者： Mohamed ETTAOUIL khalid HADDOUCH Youssef HAMI. LOQMAN Chakir Scientific Computing and Computer sciences Engineering sciences Modeling and Scientific Computing Laboratory Faculty of Science and Technology of Fez University Sidi Mohammed ben Abdellah Box 2202 Fez Morocco Department of Computer Engineering High School of technology Moulay Isma?l University B. P. 3103 50000 Toulal Meknes Morocco.

ISBN: (纸本)9781479902972

关键词： component maximal constraint satisfaction problems quadratic 0-1 programming continuous Hopfield network energy function

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D²G²A:: A distributed double guided genetic algorithm for Max_CSPs

D<SUP>2</SUP>G<SUP>2</SUP>A:: A distributed double guided ge...

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7th International Conference on Knowledge-Based Intelligent Information and Engineering Systems (KES 2003)

作者： Bouamama, S Jlifi, B Ghédira, K Univ Tunis ISG SOI2E Dept Informat Bouchoucha 2000 Tunisia

ISBN: (纸本)3540408037

Inspired by the distributed guided genetic algorithm (DGGA), D(2)G(2)A is a new multi-agent approach, which addresses maximal constraint satisfaction problems (Max-CSP). GA efficiency provides good solution quality for Max_CSPs in one hand and benefits from multi-agent principles reducing GA temporal complexity. In addition to that the approach will be enhanced by a new parameter called guidance operator. The latter allows not only diversification but also an escaping from local optima. D(2)G(2)A and DGGA are been applied to a number of randomly generated Max_CSPs. In order to show D(2)G(2)A advantages, experimental comparison is provided. As well, guidance operator is experimentally outlined in order to determine its best given value.

关键词： maximal constraint satisfaction problems multi-agent systems genetic algorithms min-conflict-heuristic guidance operator

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A family of distributed double guided genetic algorithm for Max_CSPs

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INTERNATIONAL JOURNAL OF KNOWLEDGE-BASED AND INTELLIGENT ENGINEERING SYSTEMS 2006年第5期10卷 363-376页

作者： Bouamama, Sadok Ghedira, Khaled Univ Tunis SOIE Dept Informat B 20441 Rue Liberte Cite Bouchoucha 2000 Tunisia

This paper presents, studies and betters distributed Guided Genetic Algorithm(DGGA) dealing withmaximal constraint satisfaction problems. This algorithm consists of agents dynamically created and cooperating in order to satisfy the maximal number of constraints. Each agent performs its own GA, guided by both the template concept and the Min-conflict-heuristic, on a sub-population composed of chromosomes violating the same number of constraints. D(2)G(2)A is a new multi-agent approach, which in addition to DGGA will be enhanced by a new parameter called guidance operator. The latter allows not only diversification but also an escaping from local optima. D(2)G(2)A is improved in the second part. This improvement is based on the NEO-DARWINISM theory and on the laws of nature. In fact, the new algorithm will let the species agent able to count its cross-over probability and its mutation probability. This approach is called D(3)G(2)A. In this paper, newer algorithms and their global dynamics are furnished, and experimental results are provided.

关键词： maximal constraint satisfaction problems multi-agent systems genetic algorithms Min-conflict-heuristic guidance operator

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N-Queens optimization by the distributed double guided genetic algorithm

N-Queens optimization by the distributed double guided genet...

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10th World Multi-Conference on Systemics, Cybernetics and Informatics/12th International Conference on Information Systems Analysis and Synthesis

作者： Bouamama, Sadok Ghedira, Khaled Univ Tunis ISG SOIE Tunis Tunisia

ISBN: (纸本)9789806560710

Within the framework of constraint reasoning D(3)G(2)A is a mufti-agent approach which addresses maximal constraint satisfaction problems (Max-CSP). Our approach is a distributed guided emetic algorithm that possesses a semi random mutation process. In the D(3)G(2)A approach, agents are able to determine their own GAs parameters. This operation is based on the species typology na don the Neo-Darwinism theory. The occurrence of these measures not only diversifies the search and explores wholly its space but also helps the GA process escaping from local optima. D(3)G(2)A is enhanced by optimised parameters. In order to show D(3)G(2)A advantages, experimental comparison with GGA is processed on the N-Queens problem. The results show clearly that the new approach improves the given results. In this paper. new algorithms and their global dynamics are furnished. and experimental results are provided.

关键词： maximal constraint satisfaction problems multi-agent systems genetic algorithms min-conflict-heuristic guidance operator

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A New Hybrid GPU-PSO Approach for Solving Max-CSPs

A New Hybrid GPU-PSO Approach for Solving Max-CSPs

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Narjess, Dali Sadok, Bouamama Univ Manouba Natl Sch Comp Sci COSMOS Lab Manouba Tunisia

ISBN: (纸本)9781450343237

Particle swarm optimization (PSO) has been considered as a very efficient swarm intelligence technique used to solve many problems, such as those related to constraint reasoning in particular constraint satisfaction problems (CSPs). In this paper, we introduce a new PSO method for solving maximal satisfaction problems Max-CSPs, which belong to CSPs extensions. Our approach is based on a combination between two concepts: double guidance by both template concept and min-conflict heuristic, and the Triggered mutation proposed by Zhou and Tan. This new proposed approach avoids premature stagnation process in order to improve Max-CSPs solution quality. We resort to the high parallel computing insofar as it has shown high performances in several fields, using GPU architecture as a parallel computing framework. The experimental results, presented at the end, show the efficiency of the introduced technique in the resolution of large size Max-CSPs.

关键词： gpu computing particle swarm optimization maximal constraint satisfaction problems

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Guided genetic algorithm: a new template concept 19th

Guided genetic algorithm: a new template concept

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19th International Conference on Knowledge Based and Intelligent Information and Engineering Systems (KES)

作者： Ben Othman, Hajer Tagina, Moncef Univ Manouba Natl Sch Comp Sci COSMOS Lab Manouba Tunisia

Guided genetic algorithm and dynamic distributed double guided genetic algorithm are based on nature laws and by the Neo-Darwinism theory. These evolutionary approaches were very successful addressing maximal constraint satisfaction problems (Max-CSPs). Our work is inspired by a little mistake when dealing with in these two algorithms guidance. In fact these approaches are guided by the min-conflict heuristic and the template concept. The used template is distorted. So, we introduce a new template concept in order to allow a better guidance. We suggest considering the percentages of violated constraints in place of their number. This concept is, then, applied to guide the genetic algorithms. In this paper, we compare the latter guided genetic algorithm with our new template guided genetic algorithm. The experimentations show that our new template guidance improves the optimization process to find best solutions in better time. (C) 2015 The Authors. Published by Elsevier B.V.

关键词： maximal constraint satisfaction problems template concepts guided genetic algorithm

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GPU-PSO : Parallel Particle Swarm Optimization approaches on Graphical Processing Unit for constraint Reasoning: Case of Max-CSPs 19th

GPU-PSO : Parallel Particle Swarm Optimization approaches on...

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19th International Conference on Knowledge Based and Intelligent Information and Engineering Systems (KES)

作者： Dali, Narjess Bouamama, Sadok Univ Manouba Natl Sch Comp Sci COSMOS Lab Manouba Tunisia

constraint satisfaction problems (CSPs) occur now in different domains. Several methods are used to solve them. In particular, Particle Swarm Optimization (PSO) allows to solve efficiently CSPs by significantly reducing the calculation time to explore the search space of solutions. However, this metaheuristic is excessively costing when facing large instances. In this paper we address the maximal constraint satisfaction problems (Max-CSPs). We introduce a new resolution approach that allows solving efficiently the Max-CSPs even with large instances. Our purpose is to implement a PSO based method by using the GPU architecture as a parallel computing framework. In particular, we focus on the implementation of two parallel novel approaches. The first one is a parallel GPU-PSO for Max-CSPs (GPU-PSO) and the second one is a GPU distributed PSO for Max-CSPs (GPU-DPSO). Our experimental results show the efficiency of the two proposed approaches and their ability to exploit GPU architecture. (C) 2015 The Authors. Published by Elsevier B.V.

关键词： particle swarm optimization maximal constraint satisfaction problems GPU-computing

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Guided Genetic Algorithm: A New Template Concept

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Procedia Computer Science 2015年 60卷 1101-1108页

作者： Hajer BEN Othman Moncef Tagina COSMOS laboratory the national school of computer sciences the university of Manouba Tunisia

关键词： maximal constraint satisfaction problems template concepts guided genetic algorithm

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GPU-PSO: Parallel Particle Swarm Optimization Approaches on Graphical Processing Unit for constraint Reasoning: Case of Max-CSPs

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Procedia Computer Science 2015年 60卷 1070-1080页

作者： Narjess Dali Sadok Bouamama COSMOS Laboratory National School of Computer ScienceUniversity of manouba Tunisia

关键词： particle swarm optimization maximal constraint satisfaction problems GPU- computing

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