Ali Daanoun

Bio: Ali Daanoun is an academic researcher from Abdelmalek Essaâdi University. The author has contributed to research in topics: Supply chain & Metaheuristic. The author has an hindex of 2, co-authored 3 publications receiving 11 citations.

Parallel approach for genetic algorithm to solve the Asymmetric Traveling Salesman Problems

Abstract: Metaheuristics methods are the most efficient if not the only way to solve difficult problems in both science and industry. In computer science, these methods are being used to find a good answer for NP-hard optimization problems in moderate execution times by shrinking the size of the search space to focus on regions with the height change of having an acceptable solution. Nevertheless, if we consider a large problem instance as in the real word, finding a good solution with traditional implementation of metaheuristics needs a huge computational power (in term of processing capability and memory usage) as well as time to solve, even the best known machines in our time cannot handle the massive work load to just initials a real word scenario. For that reason, implementing a parallel computing of these methods is number one priority to speed up the search giving that in most cases, the biggest limitation is the time; one of the newest techniques to achieve the best results is by using Graphical processing units (GPUs). However, taking advantage of GPU's parallel nature to compute Metaheuristics is rarely studied in the literature. In this paper, we present a new approach for the design and implementation of effective metaheuristics algorithms on GPU by using the latest technologies like CUDA. To accelerate the search mechanism even more, we have introduce new functions like host-device data transfer optimization, thread control, Coalesced memory access.

Iterative Method to Solve a Data Completion Problem for Biharmonic Equation for Rectangular Domain

Abstract: Abstract In this work, we are interested in a class of problems of great importance in many areas of industry and engineering. It is the invese problem for the biharmonic equation. It consists to complete the missing data on the inaccessible part from the measured data on the accessible part of the boundary. To solve this ill-posed problem, we opted for the alternative iterative method developed by Kozlov, Mazya and Fomin which is a convergent method for the elliptical Cauchy problems in general. The numerical implementation of the iterative algorithm is based on the application of the boundary element method (BEM) for a sequence of mixed well-posed direct problems. Numerical results are performed for a square domain showing the effectiveness of the algorithm by BEM to produce accurate and stable numerical results.

A Parallel Approach to Optimize the Supply Chain Management

Abstract: The worldwide economic progression in the last century and the Demographic growth has given rise to a huge consumption in the market of goods and services, while globalization decreased the cost of shipping and transportation. The production, transportation, storage and consumption of all these goods, however, have created big environmental problems. Nowadays, global warming, created by large-scale emissions of greenhouse gasses, is a top environmental concern. In this matter, the number of organizations planning to integrate the environmental practices into their future strategic plans is continuously increasing to counter this threat. The environmental benefits of the trend are clear: fewer vehicles burning fuel, crowding urban streets, and taking up valuable parking areas. However, the problem with transportation is that it can be so difficult to choose the perfect path for the vehicle to take if there is many stops to be taking in consideration. Due to the complexity of real world problems, such as supply chain management, finding a good path for vehicles with traditional ways (by using human capabilities) require a long time to satisfy all constraints. Even with machines, this particular problem needs a huge computational power (in term of processing power and memory usage) as well as time to solve. Actually, Parallelism is an approach that not only reduce the resolution time but also improve the quality of the provided solutions. The purpose of this paper is to evaluate the Travelling Salesman Problem (TSP) as a function of forming and optimizing transport networks using an efficient parallelization strategy for the Ant Colony Optimization (ACO) taking the maximum advantage of the parallel architecture offered by NVidia’s Graphics Processing Units (GPUs).

An efficient parallel genetic algorithm solution for vehicle routing problem in cloud implementation of the intelligent transportation systems

Abstract: A novel parallelization method of genetic algorithm (GA) solution of the Traveling Salesman Problem (TSP) is presented. The proposed method can considerably accelerate the solution of the equivalent TSP of many complex vehicle routing problems (VRPs) in the cloud implementation of intelligent transportation systems. The solution provides routing information besides all the services required by the autonomous vehicles in vehicular clouds. GA is considered as an important class of evolutionary algorithms that can solve optimization problems in growing intelligent transport systems. But, to meet time criteria in time-constrained problems of intelligent transportation systems like routing and controlling the autonomous vehicles, a highly parallelizable GA is needed. The proposed method parallelizes the GA by designing three concurrent kernels, each of which running some dependent effective operators of GA. It can be straightforwardly adapted to run on many-core and multi-core processors. To best use the valuable resources of such processors in parallel execution of the GA, threads that run any of the triple kernels are synchronized by a low-cost switching mechanism. The proposed method was experimented for parallelizing a GA-based solution of TSP over multi-core and many-core systems. The results confirm the efficiency of the proposed method for parallelizing GAs on many-core as well as on multi-core systems.

In Pursuit of the Traveling Salesman: Mathematics at the Limits of Computation

Abstract: New Century Maths raises the benchmark for mathematics in New South Wales. Each text contains work from a number of stages to accommodate the mixed-ability classroom and to cater for studentsa individual differences. Texts structured in this way encourage flexible teaching and learning plans and truly reflect the intention of an outcomes-based syllabus. To fully cater for a wide range of abilities and needs, each text at years 9 and 10 is published in two versions, stages 5.1/5.2 and stages 5.2/5.3, both providing different pathways of learning. This structure enables students to follow the pathway into the stage 6 mathematics course that best suits their abilities and needs.

Ant Colony Optimization

Abstract: Ant Colony Optimization (ACO) is a stochastic local search method that has been inspired by the pheromone trail laying and following behavior of some ant species [1]. Artificial ants in ACO essentially are randomized construction procedures that generate solutions based on (artificial) pheromone trails and heuristic information that are associated to solution components. Since the first ACO algorithm has been proposed in 1991, this algorithmic method has attracted a large number of researchers and in the meantime it has reached a significant level of maturity. In fact, ACO is now a well-established search technique for tackling a wide variety of computationally hard problems.

On GPU Implementation of the Island Model Genetic Algorithm for Solving the Unequal Area Facility Layout Problem

Abstract: Facility layout problem (FLP) is one of the hottest research areas in industrial engineering. A good facility layout can achieve efficient production management, improve production efficiency, and create high economic values. Because FLP is an NP-hard problem, meaning it is impossible to find the optimal solution when problem becomes sufficiently large, various evolutionary algorithms (EAs) have been proposed to find a sub-optimal solution within a reasonable time interval. Recently, a genetic algorithm (GA) was proposed for unequal area FLP (UA-FLP), where the areas of facilities are not identical. More precisely, the GA is an island model based, which is called IMGA. Since EAs are still very time consuming, many efforts have been devoted to how to parallelize various EAs including IMGA. In recent work, Steffen and Dietmar proposed how to parallelize island models of EAs. However, their parallelization approaches are preliminary because they focused mainly on comparing the performances between different parallel architectures. In addition, they used one mathematical function to model the problem. To further investigate on how to parallelize the IMGA by GPU, in this paper we propose multiple parallel algorithms, for each individual step in the IMGA when solving the industrial engineering problem, UA-FLP, and conduct experiments to compare their performances. After integrating better algorithms for all steps into the IMGA, our GPU implementation outperforms the CPU counterpart and the best speedup can be as high as 84.