evolutionary algorithms (EAs) are fast and robust computation methods for global optimization, and have been widely used in many real-world applications. We first conceptually discuss the equivalences of various popul...
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evolutionary algorithms (EAs) are fast and robust computation methods for global optimization, and have been widely used in many real-world applications. We first conceptually discuss the equivalences of various popular EAs including genetic algorithm (GA), biogeography-based optimization (BBO), differential evolution (DE), evolution strategy (ES) and particle swarm optimization (PSO). We find that the basic versions of BBO, DE, ES and PSO are equal to the GA with global uniform recombination (GA/GUR) under certain conditions. Then we discuss their differences based on biological motivations and implementation details, and point out that their distinctions enhance the diversity of EA research and applications. To further study the characteristics of various EAs, we compare the basic versions and advanced versions of GA, BBO, DE, ES and PSO to explore their optimization ability on a set of real-world continuous optimization problems. Empirical results show that among the basic versions of the algorithms, BBO performs best on the benchmarks that we studied. Among the advanced versions of the algorithms, DE and ES perform best on the benchmarks that we studied. However, our main conclusion is that the conceptual equivalence of the algorithms is supported by the fact that algorithmic modifications result in very different performance levels. (C) 2013 Elsevier Ltd. All fights reserved.
Floor slabs represent a large volume of concrete in buildings. The goal of this research is to achieve a structure that has an optimized bearing capacity. The optimization implies economic efficiency and sustainabilit...
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Floor slabs represent a large volume of concrete in buildings. The goal of this research is to achieve a structure that has an optimized bearing capacity. The optimization implies economic efficiency and sustainability. This paper describes a bionic optimization process that is applied in a project of the German Research Foundation (DFG) Priority Programme called "Concrete light. Future concrete structures using bionic, mathematical and engineering formfinding principles". The project involves adaption of three different natural structures that lead to a natural flow of forces. These natural structures are (a) spider webs, (b) hollow parts of bones and (c) geometries of structures such as the bottom side of water lilies or seashells. This scientific paper deals with the implementation of an optimization process for a configuration of reinforcement inspired by a spider web. evolutionary algorithms (EAs) are used for the development and optimization of an innovative and useful configuration of reinforcement. The EAs use reproduction, mutation and selection as mechanisms, inspired by biological evolution, to solve technical problems gradient-free. In this project the EA is combined with physical nonlinear finite element analyses. The EA is embedded into a C# application, in which the slab structure is generated and the finite element programme is started. The quality of the results is characterized by the fitness of each individual (reinforcement configuration), which is, for this example, the midspan displacement of the generated slab multiplied by the steel volume per slab. Accordingly, the midspan displacement is to be minimized during the process, with the minimum possible amount of reinforcement. The optimization variables are the angles and the number of rebars per slab. Several constrains need to be included to get comparable results between the developed slabs and the conventional slabs with orthogonally configured reinforcement. This paper presents the results
evolutionary algorithms, EA's, try to imitate, in some way, the principles of natural evolution and genetics. They evolve a population of potential solutions to the problem using operators such as mutation, crosso...
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evolutionary algorithms, EA's, try to imitate, in some way, the principles of natural evolution and genetics. They evolve a population of potential solutions to the problem using operators such as mutation, crossover and selection. In general, the mutation operator is responsible for the diversity of the population and helps to avoid the problem of premature convergence to local optima (a premature stagnation of the search caused by the lack of population diversity). In this paper we present a new mutation operator in the context of Multi-Objective evolutionary algorithms, MOEA's, which makes use of the definition of Pareto optimality and manages the maximal amplitude or maximal step size of the mutation according to the Pareto layer of the individual and also of the iteration number. The behaviour of our mutation operator reveals that the use of variation operators which take into consideration the quality of the solutions, in terms of Pareto dominance or Pareto layers, can help to improve them. The Pareto based mutation operator proposed is compared with four well established and extensively used mutation operators: random mutation, non-uniform mutation, polynomial mutation and Gaussian mutation. The accomplished experiments reveal that our mutation operator performs, in most of the test problems considered, better than the others.
In this paper we propose a crossover operator for evolutionary algorithms with real values that is based on the statistical theory of population distributions. The operator is based on the theoretical distribution of ...
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In this paper we propose a crossover operator for evolutionary algorithms with real values that is based on the statistical theory of population distributions. The operator is based on the theoretical distribution of the values of the genes of the best individuals in the population. The proposed operator takes into account the localization and dispersion features of the best individuals of the population with the objective that these features would be inherited by the offspring. Our aim is the optimization of the balance between exploration and exploitation in the search process. In order to test the efficiency and robustness of this crossover, we have used a set of functions to be optimized with regard to different criteria, such as, multimodality, separability, regularity and epistasis. With this set of functions we can extract conclusions in function of the problem at hand. We analyze the results using ANOVA and multiple comparison statistical tests. As an example of how our crossover can be used to solve artificial intelligence problems, we have applied the proposed model to the problem of obtaining the weight of each network in a ensemble of neural networks. The results obtained are above the performance of standard methods.
A fast and accurate fit program is presented for deconvolution of one-dimensional solid-state quadrupolar NMR spectra of powdered materials. Computational costs of the synthesis of theoretical spectra are reduced by t...
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A fast and accurate fit program is presented for deconvolution of one-dimensional solid-state quadrupolar NMR spectra of powdered materials. Computational costs of the synthesis of theoretical spectra are reduced by the use of libraries containing simulated time/frequency domain data. These libraries are calculated once and with the use of second-party simulation software readily available in the NMR community, to ensure a maximum flexibility and accuracy with respect to experimental conditions. EASY-GOING deconvolution (EGdeconv) is equipped with evolutionary algorithms that provide robust many-parameter fitting and offers efficient parallellised computing. The program supports quantification of relative chemical site abundances and (dis)order in the solid-state by incorporation of (extended) Czjzek and order parameter models. To illustrate EGdeconv's current capabilities, we provide three case studies. Given the program's simple concept it allows a straightforward extension to include other NMR interactions. The program is available as is for 64-bit Linux operating systems. (C) 2011 Elsevier Inc. All rights reserved.
In the current study, the performance of three evolutionary algorithms, differential algorithm (DE), evolution strategy (ES), and biogeography-based optimization algorithm (BBO), is examined for foundation design opti...
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In the current study, the performance of three evolutionary algorithms, differential algorithm (DE), evolution strategy (ES), and biogeography-based optimization algorithm (BBO), is examined for foundation design optimization. Moreover, four recent variations of evolutionary-based algorithms [i.e., improved differential evolution algorithm based on an adaptive mutation scheme, weighted differential evolution algorithm (WDE), linear population size reduction success-history-based adaptive differential evolution algorithm, and biogeography-based optimization with covariance matrix-based migration] have been tackled for handling the current problem. The objective function is based on the cost of shallow foundation designs that satisfy ACI 318-05 requirements is formulated as the objective function. This study addresses shallow footing optimization with two attitudes, routine optimization, and sensitivity analysis. As a further study, the effect of the location of the column at the top of the foundation is examined by adding two additional design variables. Three numerical case studies are used for both routine and sensitivity analysis. Moreover, the most common evolutionary-based technique, genetic algorithm (GA), is considered as a benchmark to evaluate the proposed methods' efficiency. Based on the results, there is no algorithm which works as the most efficient solver over all the cases;while, BBO and WDE showed an acceptable performance because of satisfying records in most cases. There were several cases in which GA, DE, and ES were incapable of finding a valid solution which meets all the constraints simultaneously.
In multi-objective optimization, standard evolutionary algorithms, such as NSGA-II, are computationally expensive, particularly when handling complex constraints. Constraint evaluations, often the bottleneck, require ...
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In multi-objective optimization, standard evolutionary algorithms, such as NSGA-II, are computationally expensive, particularly when handling complex constraints. Constraint evaluations, often the bottleneck, require substantial resources. Pre-trained surrogate models have been used to improve computational efficiency, but they often rely heavily on the model's accuracy and require large datasets. In this study, we use active learning to accelerate multi-objective optimization. Active learning is a machine learning approach that selects the most informative data points to reduce the computational cost of labeling data. It is employed in this study to reduce the number of constraint evaluations during optimization by dynamically querying new data points only when the model is uncertain. Incorporating machine learning into this framework allows the optimization process to focus on critical areas of the search space adaptively, leveraging predictive models to guide the algorithm. This reduces computational overhead and marks a significant advancement in using machine learning to enhance the efficiency and scalability of multi-objective optimization tasks. This method is applied to six challenging benchmark problems and demonstrates more than a 50% reduction in constraint evaluations, with varying savings across different problems. This adaptive approach significantly enhances the computational efficiency of multi-objective optimization without requiring pre-trained models.
Urban-planning authorities continually face the problem of optimising the allocation of green space over time in developing urban environments. The problem is essentially a sequential decision-making task involving se...
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Urban-planning authorities continually face the problem of optimising the allocation of green space over time in developing urban environments. The problem is essentially a sequential decision-making task involving several interconnected and non-linear uncertainties, and requires time-intensive computation to evaluate the potential consequences of individual decisions. We explore the application of two very distinct frameworks incorporating evolutionary algorithm approaches for this problem: (i) an offline' approach, in which a candidate solution encodes a complete set of decisions, which is then evaluated by full simulation and (ii) an online' approach which involves a sequential series of optimisations, each making only a single decision, and starting its simulations from the endpoint of the previous run. We study the outcomes, in each case, in the context of a simulated urban development model, and compare their performance in terms of speed and quality. Our results show that the online version is considerably faster than the offline counterpart, without significant loss in performance.
Photographic supra-projection is a forensic process that aims to identify a missing person from a photograph and a skull found. One of the crucial tasks throughout all this process is the craniofacial superimposition ...
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Photographic supra-projection is a forensic process that aims to identify a missing person from a photograph and a skull found. One of the crucial tasks throughout all this process is the craniofacial superimposition which tries to find a good fit between a 3D model of the skull and the 2D photo of the face. This photographic supra-projection stage is usually carried out manually by forensic anthropologists. It is thus very time consuming and presents several difficulties. In this paper, we aim to demonstrate that real-coded evolutionary algorithms are suitable approaches to tackle craniofacial superimposition. To do so, we first formulate this complex task in forensic identification as a numerical optimization problem. Then, we adapt three different evolutionary algorithms to solve it: two variants of a real-coded genetic algorithm and the state of the art evolution strategy CMA-ES. We also consider an existing binary-coded genetic algorithm as a baseline. Results on several superimposition problems of real-world identification cases solved by the Physical Anthropology lab at the University of Granada (Spain) are considered to test our proposals. (C) 2009 Elsevier Inc. All rights reserved.
A two-level, two-objective optimization scheme based on evolutionary algorithms (EAs) is proposed for solving power generating Unit Commitment (UC) problems by considering stochastic power demand variations. Apart fro...
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A two-level, two-objective optimization scheme based on evolutionary algorithms (EAs) is proposed for solving power generating Unit Commitment (UC) problems by considering stochastic power demand variations. Apart from the total operating cost to cover a known power demand distribution over the scheduling horizon, which is the first objective, the risk of not fulfilling possible demand variations forms the second objective to be minimized. For this kind of problems with a high number of decision variables, conventional EAs become inefficient optimization tools, since they require a high number of evaluations before reaching the optimal solution(s). To considerably reduce the computational burden, a two-level algorithm is proposed. At the low level, a coarsened UC problem is defined and solved using EAs to locate promising solutions at low cost: a strategy for coarsening the UC problem is proposed. Promising solutions migrate upwards to be injected into the high level EA population for further refinement. In addition, at the high level, the scheduling horizon is partitioned in a small number of subperiods of time which are optimized iteratively using EAs, based on objective function(s) penalized to ensure smooth transition from/to the adjacent subperiods. Handling shorter chromosomes due to partitioning increases method's efficiency despite the need for iterating. The proposed two-level method and conventional EAs are compared on representative test problems. (C) 2008 Elsevier Ltd. All rights reserved.
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