An experimental analysis of the p-median problem under uncertainty: an evolutionary algorithm approach

Multi-sensor fusion: an Evolutionary algorithm approach

This paper discusses a bi-objective reliability optimization of a switch-mode active and cold standby k-out-of-n series–parallel system in which a switch is installed to add a redundant component when one of the components fails. The system failure rate is not only dependent on the number of its working components, but reduces when more monitory resources are allocated. As the bi-objective optimization problem is shown to belong to the class of NP-hard problems, a multi-objective meta-heuristic algorithm, namely multi-objective evolutionary algorithm based on decomposition (MOEA/D) with a novel solution structure is developed to solve large-scale problems. Since there is no benchmark available in the literature, the well-known multi-objective evolutionary algorithm called the non-dominated sorting genetic algorithm (NSGA-II) is utilized to validate the solutions obtained. The parameters of both algorithms are tuned by the Taguchi method where the AHP–TOPSIS method is applied to compare the performances of the parameter-tuned algorithms in terms of several multi-objective performance measures. The results are in support of MOEA/D.

In this paper, the evolutionary algorithms approach is applied to the parameterization of a mathematical model describing the Mössbauer spectra of nanogranular (or nanoparticle) magnetic systems. These systems exhibit physical properties very different from bulk specimens being of great interest for material science and its use as biosensors, magneto sensors, data storage, and magnetic fluids. The purpose of this work is to compare the performance between the Differential Evolution and the Evolutionary Strategies algorithms to optimize the model parameters which best fit the experimental Mössbauer spectra of nanoscale magnetic particles. Spectra of two samples (??iron foil and NiFe2O4 nanoparticles) were recorded, at room temperature, by a conventional Mössbauer spectrometer using a scintillation detector in transmission geometry with a 57Co/Rh source. Fits to Mössbauer spectra were done using spin hamiltonians to describe both the electronic and nuclear interactions; a model of superparamagnetic relaxation of two levels (spin ½) and stochastic theory; a lognormal particle size distribution function as well as a dependency of the magnetic transition temperature and the anisotropy constant on particle diameter. The evolutionary algorithms have been implemented using Python programming language. For comparison, the two algorithms obey the termination criterion of 6,000 evaluations of the objective function. The results presented show the efficiency of these algorithms in the optimization of the parameters and on the fits of the spectra.

A novel evolutionary algorithm (EA) approach is developed for the implementation of rule based controllers, which increases the diversity of individuals to reduce the opportunities of falling into local optima. The rule based controller is simply composed of a look up table whose dimensions are determined by the numbers of the subranges of error variables required for operation. Many experiments are successfully made on the evolution of the rule based controllers by the novel EA for three illustrative examples: DC servomotor control, ball and beam control, and cart pendulum control. The computer simulations show that the proposed EA is an effective approach to generating control rules which manipulate nonlinearly dynamical systems exceedingly well. It reveals that the EA's learning behavior is very like that of a human expert.

In this paper, we compare two multi-objective evolutionary algorithms by solving bi-objective linear and nonlinear constrained optimization problems. The problems considered are three instances of a realistic crop planning problem. The multiobjective algorithms compared are a well-known multi-objective evolutionary algorithm NSGAII and our own algorithm MCA. We discuss the solutions obtained and analyse the sensitivity of variables for multi-objective solutions. From our analysis, it can be concluded that there is still room for improvement in the performance of the evolutionary optimization algorithms for some of these optimization problems

Evolutionary algorithm approaches to solving multiobjective optimization problems have received much attention in recent years. This is one of three books published in the last year or so on the subject, and Multiobjective Evolutionary Algorithms and Applications is distinguished by its claim to being written for a wide readership of engineers, researchers, senior undergraduate students and graduate students. The book is organized into 14 chapters, with the first and second chapters providing a brief introduction into multi-objective optimization and a historical summary of the basic similarities and differences between several significant multiobjective evolutionary algorithm (MOEA) approaches. Chapter 3 provides a more in-depth look at the important subject of distribution preservation. Chapters 4, 5 and 6 introduce the author's own MOEA features and approaches. In Chapter 4, the author's suggestions for implementing hard and soft goal prioritizations and problem constraints and an adaptive fitness sharing method are presented. Chapters 5 and 6 do the same for adaptive dynamic population sizes and distributed cooperative coevolution respectively. Chapter 7 introduces potential advantages from adaptive adjustment of the size of the search space.

The heat sink is one of the most widely used devices for thermal management of electronic devices and automotive systems. The present study approaches the design of the heat sink with the aim of enhancing their efficiency and keeping the material cost to a minimum. The above-mentioned purpose is achieved by posing the heat sink design problem as a bi-objective optimization problem where entropy generation rate and material cost are the two conflicting objective functions. The minimum entropy generation rate reduces irreversibilities inherent in the system, thus leading to improved performance, while the reduction in material cost ensures its economic feasibility. This bi-objective optimization problem is solved using Non-dominated Sorting Genetic Algorithm (NSGA-II) in the presence of geometric restrictions and functional requirements. Heat sinks with two different flow directions, namely flow-through air cooling system and impingement-flow air cooling system, are optimized to identify the best geometric and flow parameters. Subsequently a knowledge extraction exercise is carried out over non-dominated solutions obtained from the multi-objective optimization, to establish a relationship between the objective function and involved design parameters. The knowledge extracted has significant potential to simplify the calculations performed by thermal engineering experts in the selection of the heat sink for a specific application.KeywordsEvolutionary computation and Bi-objective optimizationKnowledge extractionElectronic and automotive coolingPlate-Fin Heat Sink

PurposeThis paper aims to present an evolutionary algorithm (EA) to accelerate the convergence for the radiative transfer equation (RTE) numerical solution using high-order and high-resolution schemes by the relaxation coefficients optimization.Design methodology/approachThe objective function minimizes the residual value difference between iterations in each control volume until its difference is lower than the convergence criterion. The EA approach is evaluated in two configurations, a two-dimensional cavity with scattering media and absorbing media.FindingsExperimental results show the capacity to obtain the numerical solution for both cases on all interpolation schemes tested by the EA approach. The EA approach reduces CPU time for the RTE numerical solution using SUPERBEE, SWEBY and MUSCL schemes until 97% and 135% in scattering and absorbing media cases, respectively. The relaxation coefficients optimized every two numerical solution iterations achieve a significant reduction of the CPU time compared to the deferred correction procedure with fixed relaxation coefficients.Originality/valueThe proposed EA approach for the RTE numerical solution effectively reduces the CPU time compared to the DC procedure with fixed relaxation coefficients.

WITHDRAWN: WITHDRAWN: An evolutionary algorithm approach for vehicle routing problems with backhauls

A bi-objective optimization framework for three-dimensional road alignment design

Reliability analysis and design of a single diode solar cell model using polynomial chaos and active subspace

Pareto front generation for bridge deck management system using bi-objective optimization

Tin monoxide, SnO, and its analog, lead monoxide, PbO, have the same tetragonal P4/nmm structure, shaped by nonbonding dispersion forces and lone pairs. The high-pressure phases of SnO and PbO have been explored in several experimental and theoretical studies, with conflicting results. In this study, the high-pressure structures of SnO and PbO are investigated using density functional theory calculations combined with an evolutionary algorithm to identify novel high-pressure phases. We propose that the monoclinic P21/m SnO and orthorhombic Pmmn PbO phases, which are metastable at 0 GPa, are a slight rearrangement of the tetragonal P4/nmm-layered structure. These orthorhombic (and their closely related monoclinic) phases become more favored than the tetragonal phase upon compression. In particular, the transition pressures to the orthorhombic γ-phase Pmn21 of SnO/PbO and the monoclinic phase P21/m of SnO are found to be consistent with experimental studies. Two new high-pressure SnO/PbO polymorphs are predicted: the orthorhombic Pbcm phase of SnO and the monoclinic C2/m of PbO. These phases are stabilized in our calculations when P > 65 GPa and P > 50 GPa, respectively. The weakening of the lone pair localization and elastic instability are the main drivers of pressure-induced phase transitions. Modulations of the SnO/PbO electronic structure due to structural transitions upon compression are also discussed.

This paper investigates the use of evolutionary and other meta-heuristic algorithms for routing problems where vehicle operating cost (VOC) and specifically, road roughness, has a significant impact. Three algorithms were implemented, namely a greedy heuristic, simulated annealing and CMA-ES. Simulated annealing delivered statistically significant results and was used to evaluate routes with and without VOC.

We attempt to find solutions to the fully fuzzified linear program where all the parameters and variables are fuzzy numbers. We first change the problem of maximizing a fuzzy number, the value of the objective function, into a multiobjective fuzzy linear programming problem. We prove that fuzzy flexible programming can be used to explore the whole undominated set to the multiobjective fuzzy linear program. An evolutionary algorithm is designed to solve the fuzzy flexible program. The program was applied to two applications with good solutions.