Almost all analyses of time complexity of evolutionary algorithms (EAs) have been conducted for (1 + 1) EAs only. Theoretical results on the average computation time of population-based EAs are few. However, the vast ...
详细信息
Almost all analyses of time complexity of evolutionary algorithms (EAs) have been conducted for (1 + 1) EAs only. Theoretical results on the average computation time of population-based EAs are few. However, the vast majority of applications of EAs use a population size that is greater than one. The use of population has been regarded as one of the key features of EAs. It is important to understand in depth what the real utility of population is in terms of the time complexity of EAs, when EAs are applied to combinatorial optimization problems. This paper compares (1 + 1) EAs and ( V + N) EAs theoretically by deriving their first hitting time on the same problems. It is shown that a population can have a drastic impact on an EA's average computation time, changing an exponential time to a polynomial time (in the input size) in some cases. It is also shown that the first hitting probability can be improved by introducing a population. However, the results presented in this paper do not imply that population-based EAs will always be better than (1 + 1) EAs for all possible problems.
A new procedure for the building and selection of supersaturated design matrices is presented. The procedure is useful in generating screening experimental designs in the range 8-22 runs. An evolutionary algorithm is ...
详细信息
A new procedure for the building and selection of supersaturated design matrices is presented. The procedure is useful in generating screening experimental designs in the range 8-22 runs. An evolutionary algorithm is used to select between all possible candidate columns, which in turn, are a Function of the selected run number, those producing the optimal matrix. Optimality, as defined by three sequentially applied common criteria (Es-2. n0, m0), is used as fitness functions in the evolution algorithm. The problem in the construction of an optimal design matrix as a particular subset of a much larger universal set of potential solutions needs specially problem-adapted genetic operators. Several have been tested and applied. To make the procedure practical, a toolkit has bern developed which allows, in a reasonable computation time, to build and select well characterised experimental supersaturated designs for a given run and factor numbers. (C) 2000 Elsevier Science B.V. All rights reserved.
This paper presents an adaptive selection scheme for use in evolutionary algorithms (EAs). The proposed algorithm adjusts the stochastic noise level in the determination of the mating pool in order to regulate the sel...
详细信息
This paper presents an adaptive selection scheme for use in evolutionary algorithms (EAs). The proposed algorithm adjusts the stochastic noise level in the determination of the mating pool in order to regulate the selection pressure. This eliminates the fitness scaling problem and allows optimization of the selection pressure throughout the learning phase, overcoming the major pitfalls of most popular EA selection procedures. Experimental evidence is given to prove the superior performance of the proposed technique compared with conventional EA procedures. The results also highlight how the application of windowing techniques to the roulette wheel procedure can increase the likelihood of premature convergence.
The main objective of the present study is to introduce a novel predictive model that combines evolutionary algorithms and machine learning (ML) models, so as to construct a landslide susceptibility map. Genetic algor...
详细信息
The main objective of the present study is to introduce a novel predictive model that combines evolutionary algorithms and machine learning (ML) models, so as to construct a landslide susceptibility map. Genetic algorithms (GA) are used as a feature selection method, whereas the particle swarm optimization (PSO) method is used to optimize the structural parameters of two ML models, support vector machines (SVM) and artificial neural network (ANN). A well-defined spatial database, which included 335 landslides and twelve landslide-related variables (elevation, slope angle, slope aspect, curvature, plan curvature, profile curvature, topographic wetness index, stream power index, distance to faults, distance to river, lithology, and hydrological cover) are considered for the analysis, in the Achaia Regional Unit located in Northern Peloponnese, Greece. The outcome of the study illustrates that both ML models have an excellent performance, with the SVM model achieving the highest learning accuracy (0.977 area under the receiver operating characteristic curve value (AUC)), followed by the ANN model (0.969). However, the ANN model shows the highest prediction accuracy (0.800 AUC), followed by the SVM (0.750 AUC) model. Overall, the proposed ML models highlights the necessity of feature selection and tuning procedures via evolutionary optimization algorithms and that such approaches could be successfully used for landslide susceptibility mapping as an alternative investigation tool.
The growing distributed energy resource (DER) penetration into distribution networks, such as through residential and commercial photovoltaics (PV), has emerged through a transition from passive to active networks, wh...
详细信息
The growing distributed energy resource (DER) penetration into distribution networks, such as through residential and commercial photovoltaics (PV), has emerged through a transition from passive to active networks, which takes the complexity of planning and operations to the next level. Optimal PV allocation (sizing and location) is challenging because it involves mixed-integer non-linear programming with three-phase non-linear unbalanced power flow equations. Meta-heuristic algorithms have proven their effectiveness in many complex engineering problems. Thus, in this study, we propose to achieve optimal PV allocation by using several basic evolutionary algorithms (EAs), particle swarm optimization (PSO), artificial bee colony (ABC), differential evolution (DE), and their variants, all of which are applied for a study of their performance levels. Two modified unbalanced IEEE test feeders (13 and 37 bus) are developed to evaluate these performance levels, with two objectives: one is to maximize PV penetration, and the other is to minimize the voltage deviation from 1.0 p.u. To handle the computational burden of the sequential power flow and unbalanced network, we adopt an efficient iterative load flow algorithm instead of the commonly used and yet highly simplified forward-backward sweep method. A comparative study of these basic EAs shows their general success in finding a near-optimal solution, except in the case of the DE, which is known for solving continuous optimization problems efficiently. From experiments run 30 times, it is observed that PSO-related algorithms are more efficient and robust in the maximum PV penetration case, while ABC-related algorithms are more efficient and robust in the minimum voltage deviation case.
The integration of Deep Reinforcement Learning (DRL) with evolutionary algorithms (EAs) represents a significant advancement in optimizing smart city energy operations, addressing the inherent uncertainties and dynami...
详细信息
The integration of Deep Reinforcement Learning (DRL) with evolutionary algorithms (EAs) represents a significant advancement in optimizing smart city energy operations, addressing the inherent uncertainties and dynamic conditions of urban environments. This study explores how the synergy between DRL and EAs, including Genetic algorithms (GAs) and Differential Evolution (DE), can enhance the efficiency and sustainability of smart city energy systems. DRL, known for its adaptive learning capabilities in complex environments, is combined with EAs, which excel in exploring diverse solution spaces and managing multi-objective optimization problems. The proposed methodology leverages DRL's ability to learn optimal policies through interaction with the environment and EAs' robust search mechanisms to address stochastic elements in energy consumption and generation. This integration is applied to various components of smart city energy operations, such as demand response, energy storage management, and renewable energy integration. The results from simulated smart city environments demonstrate significant improvements in energy efficiency, cost reduction, and emission control. This study highlights the potential of combining DRL with EAs to provide a comprehensive approach to tackling the challenges of stochastic optimization, offering a promising solution for achieving adaptive and resilient urban energy management in the face of uncertainty. Application of this integrated approach to demand response, energy storage management, and renewable energy integration in simulated smart city environments resulted in a 15% improvement in energy efficiency, a 12% reduction in operational costs, and a 20% decrease in emissions. These numeric results underscore the effectiveness of combining DRL with EAs in achieving significant gains in energy management. The study highlights the potential of this integrated approach for addressing the challenges of stochastic optimization, offering
This paper explores the possibility of using evolutionary algorithms (EAs) to automatically generate efficient and stable strategies for complicated bargaining problems. This idea is elaborated by means of case studie...
详细信息
This paper explores the possibility of using evolutionary algorithms (EAs) to automatically generate efficient and stable strategies for complicated bargaining problems. This idea is elaborated by means of case studies. We design artificial players whose learning and self-improving capabilities are powered by EAs, while neither game-theoretic knowledge nor human expertise in game theory is required. The experimental results show that a co-evolutionary algorithm (CO-EA) selects those solutions which are identical or statistically approximate to the known game-theoretic solutions. Moreover, these evolved solutions clearly demonstrate the key game-theoretic properties on efficiency and stability. The performance of CO-EA and that of a multi-objective evolutionary algorithm (MOEA) on the same problems are analyzed and compared. Our studies suggest that for real-world bargaining problems, EAs should automatically design bargaining strategies bearing the attractive properties of the solution concepts in game theory. (C) 2011 Elsevier B.V. All rights reserved.
This paper presents a new evolutionary algorithm for solving multi-objective optimization problems. The proposed algorithm simulates the infection of the endosymbiotic bacteria Wolbachia to improve the evolutionary se...
详细信息
This paper presents a new evolutionary algorithm for solving multi-objective optimization problems. The proposed algorithm simulates the infection of the endosymbiotic bacteria Wolbachia to improve the evolutionary search. We conducted a series of computational experiments to contrast the results of the proposed algorithm to those obtained by state of the art multi-objective evolutionary algorithms (MOEAs). We employed two widely used test problem benchmarks. Our experimental results show that the proposed model outperforms established MOEAs at solving most of the test problems.
Optimisation is particularly important in the case of CO2 storage in saline aquifers, where there are various operational objectives to be achieved. The storage operation design process must also take various uncertai...
详细信息
Optimisation is particularly important in the case of CO2 storage in saline aquifers, where there are various operational objectives to be achieved. The storage operation design process must also take various uncertainties into account, which result in adding computational overheads to the optimisation calculations. To circumvent this problem upscaled models with which computations are orders of magnitude less time-consuming can be used. Nevertheless, a grid resolution, which does not compromise the accuracy, reliability and robustness of the optimisation in an upscaled model must be carefully determined. In this study, a 3D geological model based on the Forties and Nelson hydrocarbon fields and the adjacent saline aquifer, is built to examine the use of coarse grid resolutions to design an optimal CO2 storage solution. The optimisation problem is to find optimal allocation of total CO2 injection rate between existing wells. A simulation template of an area encompassing proximal-type reservoirs of the Forties-Montrose High is considered. The detailed geological model construction leads to computationally intensive simulations for CO2 storage design, so that upscaling is rendered unavoidable. Therefore, an optimal grid resolution that successfully trades accuracy against computational run-time is sought after through a thorough analysis of the optimisation results for different resolution grids. The analysis is based on a back-substitution of the optimisation solutions obtained from coarse-scale models into the fine-scale model, and comparison between these back-substitution models and direct use of fine-scale model to conduct optimisation. (C) 2016 The Authors. Published by Elsevier Ltd.
Multicriteria sorting involves assigning the objects of decisions (actions) into $a$ priori known ordered classes considering the preferences of a decision maker (DM). Two new multicriteria sorting methods were recent...
详细信息
Multicriteria sorting involves assigning the objects of decisions (actions) into $a$ priori known ordered classes considering the preferences of a decision maker (DM). Two new multicriteria sorting methods were recently proposed by the authors. These methods are based on a novel approach called interval-based outranking which provides the methods with attractive practical and theoretical characteristics. However, as is well known, defining parameter values for methods based on the outranking approach is often very difficult. This difficulty arises not only from the large number of parameters and the DM's lack of familiarity with them, but also from imperfectly known (even missing) information. Here, we address: i) how to elicit the parameter values of the two new methods, and ii) how to incorporate imperfect knowledge during the elicitation. We follow the preference disaggregation paradigm and use evolutionary algorithms to address it. Our proposal performs very well in a wide range of computational experiments. Interesting findings are: i) the method restores the assignment examples with high effectiveness using only three profiles in each limiting boundary or representative actions per class;and ii) the ability to appropriately assign unknown actions can be greatly improved by increasing the number of limiting profiles.
暂无评论