Sadok Bouamama

A new distributed particle swarm optimization algorithm for constraint reasoning

Within the framework of constraint reasoning we introduce a newer distributed particle swarm approach. The latter is a new multi-agent approach which addresses additive Constraint Satisfaction problems (ΣCSPs). It is inspired by the dynamic distributed double guided genetic algorithm (D3G2A) for Constraint reasoning. It consists of agents dynamically created and cooperating in order to solve problems. Each agent performs locally its own particle swarm optimization algorithm (PSO). This algorithm is slightly different from other PSO algorithms. As well, not only do the new approach parameters allow diversification but also permit escaping from local optima. Second,. Experimentations are held to show effectiveness of our approach.

A family of distributed double guided genetic algorithm for Max_CSPs

This paper presents, studies and betters distributed Guided Genetic Algorithm (DGGA) dealing with Maximal 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.

Using surrogate information to solve the multidimensional multi-choice knapsack problem

In this paper we present a new heuristic for solving the multidimensional multi-choice knapsack problem called MMKP. The main idea is to explore both sides of the feasibility border that consists in alternating both constructive and destructive phases in a strategic oscillating manner. Performance analysis of the method shows the merits of using surrogate constraint information as choice rules for solving this problem class. A constraint normalization method is also used to strengthen the surrogate constraint information in order to improve the computational results. Numerical results show that the performance of this approach is competitive with previously published results.

D2G2A: A Distributed Double Guided Genetic Algorithm for Max_CSPs

Inspired by the distributed guided genetic algorithm (DGGA), D2G2A 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. D2G2A and DGGA are been applied to a number of randomly generated Max\_CSPs. In order to show D2G2A advantages, experimental comparison is provided. As well, guidance operator is experimentally outlined in order to determine its best given value.

Load Balancing for the Dynamic Distributed Double Guided Genetic Algorithm for MAX-CSPs

The Dynamic Distributed Double Guided Genetic Algorithm (D3G2A) deals with Maximal Constraint Satisfaction Problems. The approach consists in creating agents cooperating together to solve problems. This paper aims to improve the D3G2A. The main purpose is to balance agent loads this distributed approach. The proposed approach will redistribute the load of Species agents more equally in order to improve the CPU time. This improvement allows not only reduction of the number of Species agents but also decreases communications agents cost. In this regard, a sub-population is composed of chromosomes violating a number of constraints in the same interval. Secondly, another proposed approach will redistribute the work load. This improvement allows diminution of inactive Species agents and it results in a balanced workloads. In fact, by analogy with social animal flocks, Species agents cooperate together to do all tasks in a reduced CPU time. Several comparisons are made about new approaches with the old version of the D3G2A. Results are promising.

A family of honey-bee optimization algorithms for Max-CSPs

This paper presents new approaches for maximal constraint satisfaction problems (Max-CSPs). Inspired by the honey- bee marriage process, our approaches try to reach a solution that satisfies as many constraints as possible in a reasonable period of time. Our approaches are a centralized and distributed honey-bee optimization enhanced by a new parameter called local optimum detector. The latter allows detection of the local optimum. In this paper, newer algorithms and their experimental results are presented.

A Lagrangian and surrogate information enhanced tabu search for the MMKP

The multidimensional multi-choice knapsack problem (MMKP) is NP-hard. Within the framework of solving this problem, we suggest newer approaches. We not only propose a multi-starts version of our previous works approach using surrogate constraint informations based choices [31][32], but also we introduce another newer heuristic. The latter uses Lagrangian relaxation informations in place of surrogate informations. Compared with other literature known methods described so far, our approaches experimentations results are competitive.

D/sup 3/G/sup 2/A : the dynamic distributed double guided genetic algorithm and its application for the RLFAP

D3G2A is a new multi-agent approach which addresses additive constraint satisfaction problems (SigmaCSPs). This approach is inspired by the guided genetic algorithm (GGA) and by the dynamic distributed double guided genetic algorithm for Max_CSPs. It consists of agents dynamically created and cooperating in order to solve the problem. Each agent performs its own GA. First, our approach will be enhanced by a new parameter called guidance operator. The latter allows not only diversification but also an escaping from local optima. In the second step, the agents performed GAs will, no longer be the same. This is stirred by NEO-DARWINISM theory and the nature laws. In fact the new algorithm will let the species agents able to count their own GA parameters. In order to show D3G2A advantages, the approach and the GGA are applied on the radio link frequency allocation problem (RLFAP). The experimental comparison is provided

A Multi-objective Mathematical Model for Problems Optimization in Multi-modal Transportation Network.

In order to reach a sustainable planning in a rather complicated transport system, it is of high interest to use methods included in Operations Research areas. This study has been conducted to solve the transportation network planning problems, in accordance with the optimization problem and multi-objective transport network in multi-modal transportation. Firstly, we improve the implementation of the existing literature model proposed in (Cai, Zhang, and Shao, 2010; Zhang and Peng, 2009) because after the conducted experimentation, we show that there are two previously proposed constraints that make the solution unrealizable for the transportation problem solving. Secondly, we develop the proposed multi-objective programming model with linear constraints. Computational experiments are conducted to test the effectiveness of the proposed model. The mathematical formulation is developed to contribute to success solving the optimization problem, taking into account important aspects of the real system which were not included in previous proposals in the literature, and review. Thus, it gives ample new research directions for future studies.

Different parallelism levels using GPU for solving Max-CSPs with PSO

Thanks to the appearance of the General-Purpose computing on Graphics Processing Units (GPGPU), researchers have benefited from the spectacular High Performance Computing (HPC) provided by GPUs. Different research fields, such as combinatorial optimization, have taken advantages from the GPUs HPC. In this context, our paper introduces some different Particle Swarm Optimization (PSO) implementations for solving Maximal-Constraint Satisfaction Problems (Max CSPs) using GPU, based on different parallelism levels. These implementations are then compared. The experimental results, presented at the end, show the effectiveness and the efficiency of using GPU to optimize Max-CSPs by PSO.