32 Result(s)
within
Bilel Derbel
Computer Science
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Chapter and Conference Paper
Efficient Distributed Handshake Using Mobile Agents
There is a handshake between two nodes in a network, if the two nodes are communicating with one another in an exclusive mode. In this paper, we give a mobile agent algorithm that allows to decide whether two ...
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Chapter and Conference Paper
Fast Deterministic Distributed Algorithms for Sparse Spanners
This paper concerns the efficient construction of sparse and low stretch spanners for unweighted arbitrary graphs with n nodes. All previous deterministic distributed algorithms, for constant stretch spanner of o
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Chapter and Conference Paper
Deterministic Distributed Construction of Linear Stretch Spanners in Polylogarithmic Time
The paper presents a deterministic distributed algorithm that given an n node unweighted graph constructs an O(n 3/2) edge 3-spanner for it in O(logn) time. This algorithm is then extended into a ...
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Chapter and Conference Paper
Local Maps: New Insights into Mobile Agent Algorithms
We address the problem of computing with mobile agents having small local maps. Several trade-offs concerning the radius of the local maps, the number of agents, the time complexity and the number of agent mov...
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Chapter and Conference Paper
Mobile Agents Implementing Local Computations in Graphs
Mobile agents are a well-known paradigm for the design and implementation of distributed systems. However, whilst their popularity continues to grow, a uniform theory of mobile agent systems is not yet suffici...
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Chapter and Conference Paper
Local Computation of Nearly Additive Spanners
An (α,β)-spanner of a graph G is a subgraph H that approximates distances in G within a multiplicative factor α and an additive error β, ensuring that for any two nodes u,v, d H
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Chapter and Conference Paper
Radio Network Distributed Algorithms in the Unknown Neighborhood Model
The paper deals with radio network distributed algorithms where initially no information about node degrees is available. We show that the lack of such an information affects the time complexity of existing fu...
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Article
Sublinear Fully Distributed Partition with Applications
We present new efficient deterministic and randomized distributed algorithms for decomposing a graph with n nodes into a disjoint set of connected clusters with radius at most k−1 and having O(n ...
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Chapter and Conference Paper
Force-Based Cooperative Search Directions in Evolutionary Multi-objective Optimization
In order to approximate the set of Pareto optimal solutions, several evolutionary multi-objective optimization (EMO) algorithms transfer the multi-objective problem into several independent single-objective on...
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Chapter and Conference Paper
Adaptive Dynamic Load Balancing in Heterogeneous Multiple GPUs-CPUs Distributed Setting: Case Study of B&B Tree Search
The emergence of new hybrid and heterogenous multi-GPUs multi-CPUs large scale platforms offers new opportunities and poses new challenges when solving difficult optimization problems. This paper targets irreg...
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Chapter and Conference Paper
Shake Them All!
In this paper, we build upon the previous efforts to enhance the search ability of Moea/d (a multi-objective decomposition-based algorithm), by investigating the idea of evolving the whole population simultaneous...
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Chapter and Conference Paper
An Analysis of Differential Evolution Parameters on Rotated Bi-objective Optimization Functions
Differential evolution (DE) is a very powerful and simple algorithm for single- and multi-objective continuous optimization problems. However, its success is highly affected by the right choice of parameters. ...
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Chapter and Conference Paper
On the Impact of Multiobjective Scalarizing Functions
Recently, there has been a renewed interest in decomposition-based approaches for evolutionary multiobjective optimization. However, the impact of the choice of the underlying scalarizing function(s) is still ...
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Chapter and Conference Paper
Geometric Differential Evolution in MOEA/D: A Preliminary Study
The multi-objective evolutionary algorithm based on decomposition (MOEA/D) is an aggregation-based algorithm which has became successful for solving multi-objective optimization problems (MOPs). So far, for th...
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Chapter and Conference Paper
A Fitness Cloud Model for Adaptive Metaheuristic Selection Methods
Designing portfolio adaptive selection strategies is a promising approach to gain in generality when tackling a given optimization problem. However, we still lack much understanding of what makes a strategy ef...
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Chapter and Conference Paper
Multi-objective Local Search Based on Decomposition
It is generally believed that Local search (Ls) should be used as a basic tool in multi-objective evolutionary computation for combinatorial optimization. However, not much effort has been made to investigate how...
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Chapter and Conference Paper
Distributed Adaptive Metaheuristic Selection: Comparisons of Selection Strategies
In Distributed Adaptive Metaheuristics Selection (DAMS) methods, each computation node can select, at run-time during the optimization process, one metaheuristic to be executed from a portfolio of available me...
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Chapter and Conference Paper
A Fitness Landscape Analysis of Pareto Local Search on Bi-objective Permutation Flowshop Scheduling Problems
We study the difficulty of solving different bi-objective formulations of the permutation flowshop scheduling problem by adopting a fitness landscape analysis perspective. Our main goal is to shed the light on...
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Chapter and Conference Paper
An Approach for the Local Exploration of Discrete Many Objective Optimization Problems
Multi-objective optimization problems with more than three objectives, which are also termed as many objective optimization problems, play an important role in the decision making process. For such problems, i...
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Chapter and Conference Paper
Using Parallel Strategies to Speed up Pareto Local Search
Pareto Local Search (PLS) is a basic building block in many state-of-the-art multiobjective combinatorial optimization algorithms. However, the basic PLS requires a long time to find high-quality solutions. In...
