23 Result(s)
within
Bilel Derbel
Computer Science
Algorithm Analysis and Problem Complexity
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Chapter and Conference Paper
Approximating Pareto Set Topology by Cubic Interpolation on Bi-objective Problems
Difficult Pareto set topology refers to multi-objective problems with geometries of the Pareto set such that neighboring optimal solutions in objective space differ in several or all variables in decision spac...
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Chapter and Conference Paper
Estimating Relevance of Variables for Effective Recombination
Dominance, extensions of dominance, decomposition, and indicator functions are well-known approaches used to design MOEAs. Algorithms based on these approaches have mostly sought to enhance parent selection an...
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Chapter and Conference Paper
On Pareto Local Optimal Solutions Networks
Pareto local optimal solutions (PLOS) are believed to highly influence the dynamics and the performance of multi-objective optimization algorithms, especially those based on local search and Pareto dominance. ...
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Chapter and Conference Paper
On the Design of a Master-Worker Adaptive Algorithm Selection Framework
We investigate the design of a master-worker schemes for adaptive algorithm selection with the following two-fold goal: (i) choose accurately from a given portfolio a set of operators to be executed in paralle...
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Chapter and Conference Paper
A Surrogate Model Based on Walsh Decomposition for Pseudo-Boolean Functions
Extensive efforts so far have been devoted to the design of effective surrogate models aiming at reducing the computational cost for solving expensive black-box continuous optimization problems. There are, how...
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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...
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Chapter and Conference Paper
Towards Landscape-Aware Automatic Algorithm Configuration: Preliminary Experiments on Neutral and Rugged Landscapes
The proper setting of algorithm parameters is a well-known issue that gave rise to recent research investigations from the (offline) automatic algorithm configuration perspective. Besides, the characteristics ...
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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
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
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
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
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
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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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
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...
