70 research outputs found
Metaheuristics and cooperative approaches for the Bi-objective Ring Star Problem
International audienceThis paper presents and investigates different approaches to solve a new bi-objective routing problem called the ring star problem. It consists of locating a simple cycle through a subset of nodes of a graph while optimizing two kinds of cost. The first objective is the minimization of a ring cost that is related to the length of the cycle. The second one is the minimization of an assignment cost from non-visited nodes to visited ones. In spite of its obvious bi-objective formulation, this problem has always been investigated in a single-objective way. To tackle the bi-objective ring star problem, we first investigate different stand-alone search methods. Then, we propose two cooperative strategies that combine two multi-objective metaheuristics: an elitist evolutionary algorithm and a population-based local search. We apply these new hybrid approaches to well-known benchmark test instances and demonstrate their effectiveness in comparison to non-hybrid algorithms and to state-of-the-art methods
Metaheuristics and Their Hybridization to Solve the Bi-objective Ring Star Problem: a Comparative Study
This paper presents and experiments approaches to solve a new bi-objective
routing problem called the ring star problem. It consists of locating a simple
cycle through a subset of nodes of a graph while optimizing two kinds of cost.
The first objective is the minimization of a ring cost that is related to the
length of the cycle. The second one is the minimization of an assignment cost
from non-visited nodes to visited ones. In spite of its obvious bi-objective
formulation, this problem has always been investigated in a single-objective
way. To tackle the bi-objective ring star problem, we first investigate
different stand-alone search methods. Then, we propose two cooperative
strategies that combines two multiple objective metaheuristics: an elitist
evolutionary algorithm and a population-based local search. We apply this new
hybrid approaches to well-known benchmark test instances and demonstrate their
effectiveness in comparison to non-hybrid algorithms and to state-of-the-art
methods
Metaheuristics and cooperative approaches for the Bi-objective Ring Star Problem
International audienceThis paper presents and investigates different approaches to solve a new bi-objective routing problem called the ring star problem. It consists of locating a simple cycle through a subset of nodes of a graph while optimizing two kinds of cost. The first objective is the minimization of a ring cost that is related to the length of the cycle. The second one is the minimization of an assignment cost from non-visited nodes to visited ones. In spite of its obvious bi-objective formulation, this problem has always been investigated in a single-objective way. To tackle the bi-objective ring star problem, we first investigate different stand-alone search methods. Then, we propose two cooperative strategies that combine two multi-objective metaheuristics: an elitist evolutionary algorithm and a population-based local search. We apply these new hybrid approaches to well-known benchmark test instances and demonstrate their effectiveness in comparison to non-hybrid algorithms and to state-of-the-art methods
A Multi-objective Genetic Algorithm for Peptide Optimization
The peptide-based drug design process requires the identification of a wide
range of candidate molecules with specific biological, chemical and physical
properties. The laboratory analysis in terms of in vitro methods for the discovery of several physiochemical properties of theoretical candidate molecules is time- and cost-intensive. Hence, in silico methods are required for this purpose. Metaheuristics like evolutionary algorithms are considered to be adequate in silico methods providing good approximate solutions to the underlying multiobjective optimization problems. The general issue in this area is the design of a multi-objective evolutionary algorithm to achieve a maximum number of high-quality candidate peptides that differ in their genetic material, in a minimum number of generations.
A multi-objective evolutionary algorithm as an in silico method of discovering
a large number of high-quality peptides within a low number of generations
for a broad class of molecular optimization problems of different dimensions is
challenging, and the development of such a promising multi-objective evolutionary
algorithm based on theoretical considerations is the major contribution of this thesis. The design of this algorithm is based on a qualitative landscape
analysis applied on a three- and four-dimensional biochemical optimization
problem.
The conclusions drawn from the empirical landscape analysis of the
three- and four-dimensional optimization problem result in the formulation of hypotheses regarding the types of evolutionary algorithm components which
lead to an optimized search performance for the purpose of peptide optimization.
Starting from the established types of variation operators and selection strategies,
different variation operators and selection strategies are proposed and
empirically verified on the three- and four-dimensional molecular optimization
problem with regard to an optimized interaction and the identification of
potential interdependences as well as a fine-tuning of the parameters. Moreover,
traditional issues in the field of evolutionary algorithms such as selection
pressure and the influence of multi-parent recombination are investigated
Advances and applications in high-dimensional heuristic optimization
“Applicable to most real-world decision scenarios, multiobjective optimization is an area of multicriteria decision-making that seeks to simultaneously optimize two or more conflicting objectives. In contrast to single-objective scenarios, nontrivial multiobjective optimization problems are characterized by a set of Pareto optimal solutions wherein no solution unanimously optimizes all objectives. Evolutionary algorithms have emerged as a standard approach to determine a set of these Pareto optimal solutions, from which a decision-maker can select a vetted alternative. While easy to implement and having demonstrated great efficacy, these evolutionary approaches have been criticized for their runtime complexity when dealing with many alternatives or a high number of objectives, effectively limiting the range of scenarios to which they may be applied. This research introduces mechanisms to improve the runtime complexity of many multiobjective evolutionary algorithms, achieving state-of-the-art performance, as compared to many prominent methods from the literature. Further, the investigations here presented demonstrate the capability of multiobjective evolutionary algorithms in a complex, large-scale optimization scenario. Showcasing the approach’s ability to intelligently generate well-performing solutions to a meaningful optimization problem.
These investigations advance the concept of multiobjective evolutionary algorithms by addressing a key limitation and demonstrating their efficacy in a challenging real-world scenario. Through enhanced computational efficiency and exhibited specialized application, the utility of this powerful heuristic strategy is made more robust and evident”--Abstract, page iv
Handling High-Level Model Changes Using Search Based Software Engineering
Model-Driven Engineering (MDE) considers models as first-class artifacts during the software
lifecycle. The number of available tools, techniques, and approaches for MDE is increasing as its
use gains traction in driving quality, and controlling cost in evolution of large software systems.
Software models, defined as code abstractions, are iteratively refined, restructured, and evolved.
This is due to many reasons such as fixing defects in design, reflecting changes in requirements,
and modifying a design to enhance existing features.
In this work, we focus on four main problems related to the evolution of software models: 1) the
detection of applied model changes, 2) merging parallel evolved models, 3) detection of design
defects in merged model, and 4) the recommendation of new changes to fix defects in software
models.
Regarding the first contribution, a-posteriori multi-objective change detection approach has been
proposed for evolved models. The changes are expressed in terms of atomic and composite
refactoring operations. The majority of existing approaches detects atomic changes but do not
adequately address composite changes which mask atomic operations in intermediate models.
For the second contribution, several approaches exist to construct a merged model by
incorporating all non-conflicting operations of evolved models. Conflicts arise when the
application of one operation disables the applicability of another one. The essence of the problem
is to identify and prioritize conflicting operations based on importance and context – a gap in
existing approaches. This work proposes a multi-objective formulation of model merging that
aims to maximize the number of successfully applied merged operations.
For the third and fourth contributions, the majority of existing works focuses on refactoring at
source code level, and does not exploit the benefits of software design optimization at model
level. However, refactoring at model level is inherently more challenging due to difficulty in
assessing the potential impact on structural and behavioral features of the software system. This requires analysis of class and activity diagrams to appraise the overall system quality, feasibility,
and inter-diagram consistency. This work focuses on designing, implementing, and evaluating a
multi-objective refactoring framework for detection and fixing of design defects in software
models.Ph.D.Information Systems Engineering, College of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/136077/1/Usman Mansoor Final.pdfDescription of Usman Mansoor Final.pdf : Dissertatio
Advances in Evolutionary Algorithms
With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field
Evolutionary Computation 2020
Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms
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