Investigating the Impact of Road Roughness on Routing Performance: An Evolutionary Algorithm Approach

Multi-sensor fusion: an Evolutionary algorithm approach

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, 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.

WITHDRAWN: WITHDRAWN: An evolutionary algorithm approach for vehicle routing problems with backhauls

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.

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.

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.

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.

Facility location under uncertain environments is an important and challenging problem. The problem deals with the optimal placement of facilities that serve a set of spatially distributed nodes. One way to deal with this problem is to model uncertainty by means of scenarios and to optimise some robustness criteria such as the average and maximum regrets over these scenarios. We propose to model the robust design as a bi-objective optimisation problem and to use a well-known multi-objective evolutionary algorithm, the NSGA-II, to solve it. We also propose to use the bi-objective optimisation framework to analyse the effects of variations in the number of facilities to install, and of nodes to be served, on the quality of the Pareto solutions. Computational experiments show that the proposal can be used to design robust solutions and to study the effects of changes in the system parameters on the quality of the generated solutions.

The design of hybrid metaheuristics with ideas from the simulated annealing and evolutionary algorithms fields is a fruitful research line. In this paper, we present a new hybrid algorithm based on a genetic algorithm, whose search process simulates several parallel simulated annealing processes. An empirical study comparing the new model with classic simulated annealing, hybrid metaheuristics based on evolutionary algorithms and simulated annealing, and two evolutionary algorithms, concludes that the alternative scheme for combining ideas from simulated annealing and evolutionary algorithms introduced by the proposal may improve the performance of this kind of hybrid algorithms.

There has been a steady growth in interest in niching approaches within the evolutionary computation community, as an increasing number of real world problems are discovered that exhibit multi-modality of varying degrees of intensity (modes). It is often useful to locate and memorise the modes encountered - this is because the optimal decision parameter combinations discovered may not be feasible when moving from a mathematical model emulating the real problem to engineering an actual solution, or the model may be in error in some regions. As such a range of disparate modal solutions is of practical use. This paper investigates the use of a collection of localised surrogate models for niche/mode discovery, and analyses the performance of a novel evolutionary algorithm (EA) which embeds these surrogates into its search process. Results obtained are compared to the published performance of state-of-the-art evolutionary algorithms developed for multi-modal problems. We find that using a collection of localised surrogates not only makes the problem tractable from a model-fitting viewpoint, it also produces competitive results with other EA approaches.

Road roughness is a major factor in pavement condition because of its effects on pavement resurfacing and vehicle operating costs. Rougher pavements require more frequent resurfacing, generate complaints from highway users, reduce optimum travel speeds, disrupt traffic flow, and create safety hazards. Ride specification is one of the tools practiced by almost all state agencies including the Virginia Department of Transportation (DOT) to achieve the desired smoothness on the road. A ride specification dictates the level of smoothness from paving projects and can adjust the contractor's payment, depending on the finished ride. Virginia DOT's experience, accomplishments, and challenges with ride specifications are presented. Through application of the ride specification during the past decade, the ride quality on Virginia's rideability project roads has continued to improve. The average roughness after paving has improved dramatically in the past few years compared with that of the early 2000s. Between 2005 and 2009, 3,068 lane miles were paved according to the ride specification and a net bonus of $4,928,422 was paid to contractors. The benefit of having smoother roads could result in direct savings to an agency from deferring resurfacing activities by about 2 years and substantial savings in terms of fuel efficiency (especially for the trucks), reduced congestion due to deferred resurfacing, reduced vehicle maintenance costs (from smoother roads), and other benefits. The estimated benefits from applying the ride specification during 2005 to 2009 are found to far outweigh the cost.

In this research, an evolutionary genetic algorithm based approach is proposed for designing independent machine cells in cellular manufacturing in the presence of operation based alternative process plans for parts. Manufacturing parameters, such as production volume, usage factor for process plans, machine flexibility and cell size and machine-operation compatibility in terms of a normalised rating factor are also considered during cell design. The problem is formulated as a multi-objective optimisation model and solved using a non-dominated sorting genetic algorithm (NSGA). A unique feature of the proposed method is that it takes into account the unequal efficiency of machines in performing different operations. Some new similarity and diversity measures among operations and machines are also proposed during the clustering of machines. The formation of machine cells has been treated as a minimisation of inter-cell traffic while maximising a defined efficiency function. An illustrated problem and comparisons with existing data are given.

Carbon-emission calculation method during operation period based on asphalt pavement performance

Evolutionary algorithms (EAs) have been widely used to search for optimal strategies for the planning and management of water resources systems, particularly reservoir operation. This study provides a comprehensive diagnostic assessment of state of the art of the non-animal-inspired EA applications to reservoir optimization. This type of EAs does not mimic biologic traits and group strategies of animal (wild) species. A search of pertinent papers was applied to the journal citation reports (JCRs). A bibliometric survey identified 14 pertinent non-animal-inspired EAs, such as the genetic algorithm (GA), simulated annealing (SA), and differential evolution (DE) algorithms, most of which have a number of modified versions. The characteristics of non-animal-inspired EAs and their modified versions were discussed to identify the difference between EAs and how each EA was improved. Additionally, the type of application of non-animal-inspired EAs to different case studies was investigated, and comparisons were made between the performance of the applied EAs in the studied literature. The survey revealed that the GA is the most frequently applied algorithm, followed by the DE algorithm. Non-animal-inspired EAs are superior to the classical methods of reservoir optimization (e.g., the non-linear programming and dynamic programming) due to faster convergence, diverse solution space, and efficient objective function evaluation. Several non-animal-inspired EAs of recent vintage have been shown to outperform the classic GA, which was the first evolutionary algorithm applied to reservoir operation.