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Chapter and Conference Paper
Decomposition-Based Multi-objective Landscape Features and Automated Algorithm Selection
Landscape analysis is of fundamental interest for improving our understanding on the behavior of evolutionary search, and for develo** general-purpose automated solvers based on techniques from statistics an...
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Chapter and Conference Paper
Dynamic Compartmental Models for Large Multi-objective Landscapes and Performance Estimation
Dynamic Compartmental Models are linear models inspired by epidemiology models to study Multi- and Many-Objective Evolutionary Algorithms dynamics. So far they have been tested on small MNK-Landscapes problems...
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Chapter and Conference Paper
The Role of Virtual Reality in the Training for Carotid Artery Stenting: The Perspective of Trainees
INTRODUCTION: Virtual reality (VR) simulators have been proven to be a reliable tool to achieve experience for stenting of the carotid artery (CAS). We describe our experience in the use of virtual reality for...
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Chapter and Conference Paper
On Stochastic Fitness Landscapes: Local Optimality and Fitness Landscape Analysis for Stochastic Search Operators
Fitness landscape analysis is a well-established tool for gaining insights about optimization problems and informing about the behavior of local and evolutionary search algorithms. In the conventional definiti...
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Chapter and Conference Paper
On the Design of a Partition Crossover for the Quadratic Assignment Problem
We conduct a study on the design of a partition crossover for the QAP. On the basis of a bipartite graph representation, we propose to recombine the unshared components from parents, while enabling their fast ...
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Chapter and Conference Paper
On the Combined Impact of Population Size and Sub-problem Selection in MOEA/D
This paper intends to understand and to improve the working principle of decomposition-based multi-objective evolutionary algorithms. We review the design of the well-established Moea/d framework to support the s...
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Chapter and Conference Paper
Dominance, Indicator and Decomposition Based Search for Multi-objective QAP: Landscape Analysis and Automated Algorithm Selection
We investigate the properties of large-scale multi-objective quadratic assignment problems (mQAP) and how they impact the performance of multi-objective evolutionary algorithms. The landscape of a diversified ...
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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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Article
CHRA: a coloring based hierarchical routing algorithm
Graph coloring was exploited in wireless sensor networks to solve many optimization problems, mainly related to channel assignment. In this paper, we propose to use coloring to jointly manage channel access an...
