1,007 research outputs found
Firefly Algorithm: Recent Advances and Applications
Nature-inspired metaheuristic algorithms, especially those based on swarm
intelligence, have attracted much attention in the last ten years. Firefly
algorithm appeared in about five years ago, its literature has expanded
dramatically with diverse applications. In this paper, we will briefly review
the fundamentals of firefly algorithm together with a selection of recent
publications. Then, we discuss the optimality associated with balancing
exploration and exploitation, which is essential for all metaheuristic
algorithms. By comparing with intermittent search strategy, we conclude that
metaheuristics such as firefly algorithm are better than the optimal
intermittent search strategy. We also analyse algorithms and their implications
for higher-dimensional optimization problems.Comment: 15 page
A simplex-like search method for bi-objective optimization
We describe a new algorithm for bi-objective optimization, similar to the Nelder Mead simplex
algorithm, widely used for single objective optimization. For diferentiable bi-objective functions on
a continuous search space, internal Pareto optima occur where the two gradient vectors point in
opposite directions. So such optima may be located by minimizing the cosine of the angle between
these vectors. This requires a complex rather than a simplex, so we term the technique the \cosine
seeking complex". An extra beneft of this approach is that a successful search identifes the direction
of the effcient curve of Pareto points, expediting further searches. Results are presented for some
standard test functions. The method presented is quite complicated and space considerations here
preclude complete details. We hope to publish a fuller description in another place
A multiobjective Tabu framework for the optimization and evaluation of wireless systems
This chapter will focus on the multiobjective formulation of an optimization
problem and highlight the assets of a multiobjective Tabu implementation for
such problems. An illustration of a specific Multiobjective Tabu heuristic
(referred to as MO Tabu in the following) will be given for 2 particular
problems arising in wireless systems. The first problem addresses the planning
of access points for a WLAN network with some Quality of Service requirements
and the second one provides an evaluation mean to assess the performance
evaluation of a wireless sensor network. The chapter will begin with an
overview of multiobjective (MO) optimization featuring the definitions and
concepts of the domain (e.g. Dominance, Pareto front,...) and the main MO
search heuristics available so far. We will then emphasize on the definition of
a problem as a multiobjective optimization problem and illustrate it by the two
examples from the field of wireless networking. The next part will focus on MO
Tabu, a Tabu-inspired multiobjective heuristic and describe its assets compared
to other MO heuristics. The last part of the chapter will show the results
obtained with this MO Tabu strategy on the 2 wireless networks related
problems. Conclusion on the use of Tabu as a multiobjective heuristic will be
drawn based on the results presented so far
Generalized Lorenz-Mie theory : application to scattering and resonances of photonic complexes
Les structures photoniques complexes permettent de façonner la propagation lumineuse Ă lâĂ©chelle de la longueur dâonde au moyen de processus de diffusion et dâinterfĂ©rence. Cette fonctionnalitĂ© Ă lâĂ©chelle nanoscopique ouvre la voie Ă de multiples applications, allant des communications optiques aux biosenseurs. Cette thĂšse porte principalement sur la modĂ©lisation numĂ©rique de structures photoniques complexes constituĂ©es dâarrangements bidimensionnels de cylindres diĂ©lectriques. Deux applications sont privilĂ©giĂ©es, soit la conception de dispositifs basĂ©s sur des cristaux photoniques pour la manipulation de faisceaux, de mĂȘme que la rĂ©alisation de sources lasers compactes basĂ©es sur des molĂ©cules photoniques. Ces structures optiques peuvent ĂȘtre analysĂ©es au moyen de la thĂ©orie de Lorenz-Mie gĂ©nĂ©ralisĂ©e, une mĂ©thode numĂ©rique permettant dâexploiter la symĂ©trie cylindrique des diffuseurs sous-jacents. Cette dissertation dĂ©bute par une description de la thĂ©orie de Lorenz-Mie gĂ©nĂ©ralisĂ©e, obtenue des Ă©quations de Maxwell de lâĂ©lectromagnĂ©tisme. Dâautres outils thĂ©oriques utiles sont Ă©galement prĂ©sentĂ©s, soit une nouvelle formulation des Ă©quations de Maxwell-Bloch pour la modĂ©lisation de milieux actifs appelĂ©e SALT (steady state ab initio laser theory). Une description sommaire des algorithmes dâoptimisation dits mĂ©taheuristiques conclut le matĂ©riel introductif de la thĂšse. Nous prĂ©sentons ensuite la conception et lâoptimisation de dispositifs intĂ©grĂ©s permettant la gĂ©nĂ©ration de faisceaux dâamplitude, de phase et de degrĂ© de polarisation contrĂŽlĂ©s. Le problĂšme dâoptimisation combinatoire associĂ© est solutionnĂ© numĂ©riquement au moyen de deux mĂ©taheuristiques, lâalgorithme gĂ©nĂ©tique et la recherche tabou. Une Ă©tude thĂ©orique des propriĂ©tĂ©s de micro-lasers basĂ©s sur des molĂ©cules photoniques â constituĂ©es dâun arrangement simple de cylindres actifs â est finalement prĂ©sentĂ©e. En combinant la thĂ©orie de Lorenz-Mie et SALT, nous dĂ©montrons que les propriĂ©tĂ©s physiques de ces lasers, plus spĂ©cifiquement leur seuil, leur spectre et leur profil dâĂ©mission, peuvent ĂȘtre affectĂ©s de façon nontriviale par les paramĂštres du milieu actif sous-jacent. Cette conclusion est hors dâatteinte de lâapproche Ă©tablie qui consiste Ă calculer les Ă©tatsmĂ©ta-stables de lâĂ©quation de Helmholtz et leur facteur de qualitĂ©. Une perspective sur la modĂ©lisation de milieux photoniques dĂ©sordonnĂ©s conclut cette dissertation.Complex photonic media mold the flow of light at the wavelength scale using multiple scattering and interference effects. This functionality at the nano-scale level paves the way for various applications, ranging from optical communications to biosensing. This thesis is mainly concerned with the numerical modeling of photonic complexes based on twodimensional arrays of cylindrical scatterers. Two applications are considered, namely the use of photonic-crystal-like devices for the design of integrated beam shaping elements, as well as active photonic molecules for the realization of compact laser sources. These photonic structures can be readily analyzed using the 2D Generalized Lorenz-Mie theory (2D-GLMT), a numerical scheme which exploits the symmetry of the underlying cylindrical structures. We begin this thesis by presenting the electromagnetic theory behind 2D-GLMT.Other useful frameworks are also presented, including a recently formulated stationary version of theMaxwell-Bloch equations called steady-state ab initio laser theory (SALT).Metaheuristics, optimization algorithms based on empirical rules for exploring large solution spaces, are also discussed. After laying down the theoretical content, we proceed to the design and optimization of beam shaping devices based on engineered photonic-crystal-like structures. The combinatorial optimization problem associated to beam shaping is tackled using the genetic algorithm (GA) as well as tabu search (TS). Our results show the possibility to design integrated beam shapers tailored for the control of the amplitude, phase and polarization profile of the output beam. A theoretical and numerical study of the lasing characteristics of photonic molecules â composed of a few coupled optically active cylinders â is also presented. Using a combination of 2D-GLMT and SALT, it is shown that the physical properties of photonic molecule lasers, specifically their threshold, spectrum and emission profile, can be significantly affected by the underlying gain medium parameters. These findings are out of reach of the established approach of computing the meta-stable states of the Helmholtz equation and their quality factor. This dissertation is concluded with a research outlook concerning themodeling of disordered photonicmedia
A variable neighborhood search simheuristic for project portfolio selection under uncertainty
With limited nancial resources, decision-makers in rms and governments face the task of selecting the best portfolio of projects to invest in. As the pool of project proposals increases and more realistic constraints are considered, the problem becomes NP-hard. Thus, metaheuristics have been employed for solving large instances of the project portfolio selection problem (PPSP). However, most of the existing works do not account for uncertainty. This paper contributes to close this gap by analyzing a stochastic version of the PPSP: the goal is to maximize the expected net present value of the inversion, while considering random cash ows and discount rates in future periods, as well as a rich set of constraints including the maximum risk allowed. To solve this stochastic PPSP, a simulation-optimization algorithm is introduced. Our approach integrates a variable neighborhood search metaheuristic with Monte Carlo simulation. A series of computational experiments contribute to validate our approach and illustrate how the solutions vary as the level of uncertainty increases
Recent trends of the most used metaheuristic techniques for distribution network reconfiguration
Distribution network reconfiguration (DNR) continues to be a good option to reduce technical losses in a distribution
power grid. However, this non-linear combinatorial problem is not easy to assess by exact methods when solving for
large distribution networks, which requires large computational times. For solving this type of problem, some researchers
prefer to use metaheuristic techniques due to convergence speed, near-optimal solutions, and simple programming. Some
literature reviews specialize in topics concerning the optimization of power network reconfiguration and try to cover
most techniques. Nevertheless, this does not allow detailing properly the use of each technique, which is important to
identify the trend. The contributions of this paper are three-fold. First, it presents the objective functions and constraints
used in DNR with the most used metaheuristics. Second, it reviews the most important techniques such as particle swarm
optimization (PSO), genetic algorithm (GA), simulated annealing (SA), ant colony optimization (ACO), immune
algorithms (IA), and tabu search (TS). Finally, this paper presents the trend of each technique from 2011 to 2016. This
paper will be useful for researchers interested in knowing the advances of recent approaches in these metaheuristics
applied to DNR in order to continue developing new best algorithms and improving solutions for the topi
A controlled migration genetic algorithm operator for hardware-in-the-loop experimentation
In this paper, we describe the development of an extended migration operator, which combats the negative effects of noise on the effective search capabilities of genetic algorithms. The research is motivated by the need to minimize the num- ber of evaluations during hardware-in-the-loop experimentation, which can carry a significant cost penalty in terms of time or financial expense. The authors build on previous research, where convergence for search methods such as Simulated Annealing and Variable Neighbourhood search was accelerated by the implementation of an adaptive decision support operator. This methodology was found to be effective in searching noisy data surfaces. Providing that noise is not too significant, Genetic Al- gorithms can prove even more effective guiding experimentation. It will be shown that with the introduction of a Controlled Migration operator into the GA heuristic, data, which repre- sents a significant signal-to-noise ratio, can be searched with significant beneficial effects on the efficiency of hardware-in-the- loop experimentation, without a priori parameter tuning. The method is tested on an engine-in-the-loop experimental example, and shown to bring significant performance benefits
Ant colony system-based applications to electrical distribution system optimization
Chapter 16, February 201
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