Asbestos mining has left a legacy of pollution in former mining areas that continues to negatively affect both the environment and local communities. In 2007, the Rehabilitation Prioritisation Index was developed as a...
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Asbestos mining has left a legacy of pollution in former mining areas that continues to negatively affect both the environment and local communities. In 2007, the Rehabilitation Prioritisation Index was developed as a scientific tool to indicate the preferred sequence for mine site rehabilitation and served as a departure point for the present investigation in which a database for the rehabilitation success of asbestos sites was developed. Broad-based quantitative and qualitative data, typically used for monitoring rehabilitation success, including amongst others, soil cover depth, physical and chemical soil properties, microbial activity, vegetation properties and small mammal abundance were analysed using multivariate statistics, specifically a redundancy analysis. The most representative model was subsequently selected for the classification of the rehabilitated sites. The multivariate analysis revealed those factors typically associated with rehabilitation success or failure, as well as essentials to be addressed. The feasibility of development of a rule set for rehabilitated site classification was firstly investigated using neural networks which also assisted in the selection of significant parameters. Results from the neural network approach were then used to guide parameter selection for the evolutionary algorithm software. The coordinate scores for the first two axes of the redundancy analysis served as targets for the evolutionary algorithms. Overall, a targeting match of 71 % for the first axis coordinates and 38 % for the second axis coordinates were obtained. Contributing parameters for the rule set included: Cl, K, pH, percentage organic carbon, Zn, NH4 and SO4 content of the sites.
Fuzzy cognitive maps have been widely used as abstract models for complex networks. Traditional ways to construct fuzzy cognitive maps rely on domain knowledge. In this paper, we propose to use fuzzy cognitive map lea...
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Fuzzy cognitive maps have been widely used as abstract models for complex networks. Traditional ways to construct fuzzy cognitive maps rely on domain knowledge. In this paper, we propose to use fuzzy cognitive map learning algorithms to discover domain knowledge in the form of causal networks from data. More specifically, we propose to infer gene regulatory networks from gene expression data. Furthermore, a new efficient fuzzy cognitive map learning algorithm based on a decomposed genetic algorithm is developed to learn large scale networks. In the proposed algorithm, the simulation error is used as the objective function, while the model error is expected to be minimized. Experiments are performed to explore the feasibility of this approach. The high accuracy of the generated models and the approximate correlation between simulation errors and model errors suggest that it is possible to discover causal networks using fuzzy cognitive map learning. We also compared the proposed algorithm with ant colony optimization, differential evolution, and particle swarm optimization in a decomposed framework. Comparison results reveal the advantage of the decomposed genetic algorithm on datasets with small data volumes, large network scales, or the presence of noise. (C) 2015 Elsevier B.V. All rights reserved.
The work in this paper proposes the hybridisation of the well-established strength Pareto evolutionary algorithm (SPEA2) and some commonly used surrogate models. The surrogate models are introduced to an evolutionary ...
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The work in this paper proposes the hybridisation of the well-established strength Pareto evolutionary algorithm (SPEA2) and some commonly used surrogate models. The surrogate models are introduced to an evolutionary optimisation process to enhance the performance of the optimiser when solving design problems with expensive function evaluation. Several surrogate models including quadratic function, radial basis function, neural network, and Kriging models are employed in combination with SPEA2 using real codes. The various hybrid optimisation strategies are implemented on eight simultaneous shape and sizing design problems of structures taking into account of structural weight, lateral bucking, natural frequency, and stress. Structural analysis is carried out by using a finite element procedure. The optimum results obtained are compared and discussed. The performance assessment is based on the hypervolume indicator. The performance of the surrogate models for estimating design constraints is investigated. It has been found that, by using a quadratic function surrogate model, the optimiser searching performance is greatly improved.
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.
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.
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.
This paper presents a theoretical analysis of the convergence conditions for evolutionary algorithms. The necessary and sufficient conditions, necessary conditions, and sufficient conditions for the convergence of evo...
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This paper presents a theoretical analysis of the convergence conditions for evolutionary algorithms. The necessary and sufficient conditions, necessary conditions, and sufficient conditions for the convergence of evolutionary algorithms to the global optima are derived, which describe their limiting behaviors. Their relationships are explored. Upper and lower bounds of the convergence rates of the evolutionary algorithms are given. (C) 2001 Elsevier Science B.V. All rights reserved.
Hybrid renewable energy system has been introduced as a green and reliable power system for remote areas. There is a steady increase in usage of hybrid renewable energy units and consequently optimization problem solv...
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Hybrid renewable energy system has been introduced as a green and reliable power system for remote areas. There is a steady increase in usage of hybrid renewable energy units and consequently optimization problem solving for this system is a necessity. In recent years, researchers are interested in using multi-objective optimization methods for this issue. Therefore, in the present study, an overview of applied multi-objective methods by using evolutionary algorithms for hybrid renewable energy systems was proposed to help the present and future research works. The result shows that there are a few studies about optimization of many objects in a hybrid system by these algorithms and the most popular applied methods are genetic algorithm and particle swarm optimization. (C) 2012 Elsevier Ltd. All rights reserved.
Memetic (evolutionary) algorithms integrate local search into the search process of evolutionary algorithms. As computational resources have to be spread adequately among local and evolutionary search, one has to care...
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Memetic (evolutionary) algorithms integrate local search into the search process of evolutionary algorithms. As computational resources have to be spread adequately among local and evolutionary search, one has to care about when to apply local search and how much computational effort to devote to local search. Often local search is called with a fixed frequency and run for a fixed number of iterations, the local search depth. There is empirical evidence that these parameters have a significant impact on performance, but a theoretical understanding as well as concrete design guidelines are missing. We initiate the rigorous theoretical analysis of memetic algorithms. To this end, we consider a simple memetic algorithm for pseudo-Boolean optimization that captures basic working principles of memetic algorithms-the interplay of genetic operators like mutation and selection with local search. We present function classes where even small changes of the parametrization have a strong impact on performance. For almost every reasonable parameter setting we construct a function that, with high probability, can be optimized in polynomial time. However, changing the local search depth by a small additive term in any direction yields a superpolynomial optimization time, with high probability. For another class of functions altering the local search frequency by a factor of 2 even yields exponential optimization times. Our results show exemplarily that parametrizing memetic evolutionary algorithms can be extremely hard. Moreover, this work yields insights into the dynamic behavior of memetic algorithms and contributes to a theoretical foundation of hybrid metaheuristics. (C) 2009 Elsevier B.V. All rights reserved.
evolutionary algorithms have been used recently as an alternative in image registration, especially in cases where the similarity function is non-convex with many local optima. However, their drawback is that they ten...
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evolutionary algorithms have been used recently as an alternative in image registration, especially in cases where the similarity function is non-convex with many local optima. However, their drawback is that they tend to be computationally expensive. Trying to avoid local minima can increase the computational cost. The purpose of authors' research is to minimise the duration of the image registration process. This paper presents a method to minimise the computational cost by introducing a machine learning-based variant of Harmony Search. To this end, a series of machine-learning regression methods are tested in order to find the most appropriate that minimises the cost without degrading the quality of the results. The best regression method is then incorporated in the optimisation process and is compared with two well-known ITK image registration methods. The comparison of authors' image registration method with ITK concerns both the quality of the results and the duration of the registration experiments. The comparison is done on a set of random image pairs of various sources (e.g. medical or satellite images), and the encouraging results strongly indicate that authors' method can be used in a variety of image registration applications producing quality results in significantly less time.
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