Khaled Ghedira

Ant Colony Optimization for Multi-Objective Optimization Problems

We propose in this paper a generic algorithm based on ant colony optimization to solve multi-objective optimization problems. The proposed algorithm is parameterized by the number of ant colonies and the number of pheromone trails. We compare different variants of this algorithm on the multi-objective knapsack problem. We compare also the obtained results with other evolutionary algorithms from the literature.

Searching for knee regions in multi-objective optimization using mobile reference points

Evolutionary algorithms have amply demonstrated their effectiveness and efficiency in approximating the Pareto front of different multi-objective optimization problems. Fewer attentions have been paid to search for the preferred parts of the Pareto front according to the decision maker preferences. Knee regions are special portions of the Pareto front containing solutions having the maximum marginal rates of return, i.e., solutions for which an improvement in one objective implies a severe degradation in at least another one. Such characteristic makes knee regions of particular interest in practical applications from the decision maker perspective. In this paper, we propose a new updating strategy for a reference points based multi-objective evolutionary algorithm which forces this latter to focus on knee regions. The proposed idea uses a set of mobile reference points guiding the search towards knee regions. The extent of the obtained regions could be controlled by the means of a user-defined parameter. The verification of the proposed approach is assessed on two- and three-objective knee-based test problems a priori and interactively. The obtained results are promising.

Ant algorithm for the graph matching problem

This paper describes a new Ant Colony Optimization (ACO) algorithm for solving Graph Matching Problems, the goal of which is to find the best matching between vertices of multi-labeled graphs. This new ACO algorithm is experimentally compared with greedy and reactive tabu approaches on subgraph isomorphism problems and on multivalent graph matching problems.

Multi-agent simulation model of pedestrians crowd based on psychological theories

The simulation of pedestrian crowd that reflects reality is a major challenge for scientists and research. Several simulation pedestrian crowd models have been proposed such as cellular automata models, agent-based models, regression models, etc. It is very important to note that agent based models are able, over others approaches to provide a natural description of the system and then to capture emergent phenomena and complex human behaviors. Usually, these models include psychological theories in order to obtain a more realistic simulation of pedestrians behavior. However, a great number of them do not cover all the psychological factors necessary for a pedestrian located in a crowd. This causes a lack of realism. Thus, we propose a multi-agent simulation model which is based on psychological theories. It takes into account the major normal conditions of a simple pedestrian situated in a crowd such as his preferences, a realistic perception of the environment in which he is situated, etc. Our objective is to simulate realistically the pedestrian crowd phenomenon towards a simulation of a believable pedestrian behavior. The conducted experiments show that our model is able to produce realistic pedestrian behaviors.

Exact resolution of the one-machine sequencing problem with no machine idle time

This paper investigates the one-machine sequencing problem in a workshop where the machine has to satisfy the no-idle constraint, that is, the machine must process jobs without interruption. The objective is to minimize the makespan. Each job has a release date for which it is available for processing on the machine and a delivery time during which it must remain in the system after being processed by the machine. This problem has been studied without adding the no-idle constraint. It is solved in polynomial time, when the preemption of jobs is allowed, applying Jacksons rule. But, when the preemption of jobs is not allowed, it becomes strongly NP-hard. Although, it can be solved in a very short time using Carliers branch and bound algorithm. Below, we propose to adapt Carliers branch and bound method in order to calculate an optimal nonpreemptive schedule for the problem when adding the no-idle constraint.

Evolutionary method to optimize workplan mobile agent for the transport network application

This paper explains how to optimize the workplans of mobile agents (MAs) in order to enhance their performance. A MA travels through transport network looking for useful information and services. This information should be available daily to assist travelers to use multimodal transportation system. Our objective is to maximize the number of satisfied transport travelers. In other words, we attempt to increase the satisfaction of transport customers during their travels. So, we intend to optimize task assignment to a set of servers which can offer information with preset cost and processing time. With this intention, we adopted an approach based on evolutionary programs in order to solve our combinatorial problem. Therefore, it is very important to design an efficient representational scheme of a chromosome and develop effective genetic operators. We create a new representation of the chromosome where we integrated the constraints of our problem

Distributed decision evaluation model in public transportation systems

Due to several external and internal disturbances affecting public transportation systems, some regulation measures have to be undertaken. In the regulation process, the regulator has to evaluate a number of possible decisions in order to determine best compromise of some regulation criteria. The complexity of this task increases when numerous disturbances appear simultaneously and mainly when regulation criteria are contradictory. For these reasons, we propose in this paper a multi-agent model that deals with decision evaluation step as a multicriteria optimization problem. In this model, the best compromise is determined by means of the following concepts: Pareto optimality, a-efficiency and plurality voting. The process of the proposed approach is shown through an illustrative example.

Simultaneous scheduling of machines and transport robots in flexible job shop environment using hybrid metaheuristics based on clustered holonic multiagent model

Display Omitted Hybrid metaheuristics is proposed to schedule machines and transport robots.A genetic algorithm is applied by a scheduler agent to explore the search space.A local search is used by cluster agents to guide the search in promising regions.A new disjunctive graph is presented to model simultaneously this problem.Computational results are presented using three sets of benchmark instances. In real manufacturing environments, the control of some elements in systems based on robotic cells, such as transport robots has some difficulties when planning operations dynamically. The Flexible Job Shop scheduling Problem with Transportation times and Many Robots (FJSPT-MR) is a generalization of the classical Job Shop scheduling Problem (JSP) where a set of jobs have to be processed on a set of alternative machines and additionally have to be transported between them by several transport robots. Hence, the FJSPT-MR is more computationally difficult than the JSP presenting two NP-hard problems simultaneously: the flexible job shop scheduling problem and the robot routing problem. This paper proposes hybrid metaheuristics based on clustered holonic multiagent model for the FJSPT-MR. Firstly, a scheduler agent applies a Neighborhood-based Genetic Algorithm (NGA) for a global exploration of the search space. Secondly, a set of cluster agents uses a tabu search technique to guide the research in promising regions. Computational results are presented using three sets of benchmark literature instances. New upper bounds are found, showing the effectiveness of the presented approach.

Variable Neighborhood Search based Set Covering ILP Model for the Vehicle Routing Problem with Time Windows

Abstract In this paper we propose a hybrid metaheuristic based on General Variable Neighborhood search and Integer Linear Programming for solving the vehicle routing problem with time windows (VRPTW). The problem consists in determining the minimum cost routes for a homogeneous fleet of vehicles to meet the demand of a set of customers within a specified time windows. The proposed heuristic, called VNS-SCP is considered as a matheuristic where the hybridization of heuristic (VNS) and exact (Set Covering Problem (SCP)) method is used in this approach as an intertwined collaborative cooperation manner. In this approach an initial solution is first created using Solomon route-construction heuristic, the nearest neighbor algorithm. In the second phase the solutions are improved in terms of the total distance traveled using VNS-SCP. The algorithm is tested using Solomon benchmark. Our findings indicate that the proposed procedure outperforms other local searches and metaheuristics.

Combining Tabu Search and Genetic Algorithm in a Multi-agent System for Solving Flexible Job Shop Problem

The Flexible Job Shop problem (FJSP) is an important extension of the classical job shop scheduling problem, in that each operation can be processed by a set of resources and has a processing time depending on the resource used. The objective is to minimize the make span, i.e., the time needed to complete all the jobs. This works aims to propose a new promising approach using multi-agent systems in order to solve the FJSP. Our model combines a local optimization approach based on Tabu Search (TS) meta-heuristic and a global optimization approach based on genetic algorithm (GA).