A methodology for the selection of the optimal land uses of the reclamation of mined areas is proposed. It takes into consideration several multi-nature criteria and constraints, including spatial constrains related t...
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A methodology for the selection of the optimal land uses of the reclamation of mined areas is proposed. It takes into consideration several multi-nature criteria and constraints, including spatial constrains related to the permissible land uses in certain parts of the mined area. The methodology combines desirability functions and evolution searching algorithms for selection of the optimal reclamation scheme. Its application for the reclamation planning of the Amynteon lignite surface mine in Greece indicated that it handles effectively spatial and non-spatial constraints and incorporates easily the decision-makers preferences regarding the reclamation strategy in the optimization procedure.
Uncertainty and risks have been the inherent characteristics of large-scale projects. Although practitioners have applied different project risk management standards, numerous uncertainties, and risks in large-scale c...
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Uncertainty and risks have been the inherent characteristics of large-scale projects. Although practitioners have applied different project risk management standards, numerous uncertainties, and risks in large-scale construction projects have led to significant failures in fulfilling a project's goals. Therefore, in this study, a hybrid approach based on failure mode effects analysis (FMEA)/ISO 31000 has been proposed to identify, evaluate, and control the problem effectively. This hybrid approach is not a very accurate approach in providing an appropriate risk response;hence, a mixed-integer programming (MIP) model has been proposed to select the optimized risk response strategies for the project. In the present study, a model based on synergies among project risk responses was developed that is capable of considering the various criteria in the objective function and optimizing them based on the defined projects. Risk response selection for a large-scale project is a complex problem. Because of the nondeterministic polynomial time (NP)-hardness of the presented model, two metaheuristic algorithms, namely, the self-adaptive imperialist competitive algorithm and invasive weed optimization, were developed to solve the proposed MIP model. A large-scale high-rise residential building was evaluated as a case study to investigate the model proposed in this study empirically.
Threats against the internet and computer networks are becoming more sophisticated, with attackers using new attacks or modifying existing ones. Security teams have major difficulties in dealing with large numbers of ...
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Multipopulation is an effective optimization strategy which is often used in evolutionary algorithms (EAs) to improve optimization performance. However, it is of remarkable difficulty to determine the number of subpop...
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Multipopulation is an effective optimization strategy which is often used in evolutionary algorithms (EAs) to improve optimization performance. However, it is of remarkable difficulty to determine the number of subpopulations during the evolution process for a given problem, which may significantly affect optimization ability of EAs. This paper proposes a simple multipopulation management strategy to dynamically adjust the subpopulation number in different evolution phases throughout the evolution. The proposed method makes use of individual distances in the same subpopulation as well as the population distances between multiple subpopulations to determine the subpopulation number, which is substantial in maintaining population diversity and enhancing the exploration ability. Furthermore, the proposed multipopulation management strategy is embedded into popular EAs to solve real-world complex automated warehouse scheduling problems. Experimental results show that the proposed multipopulation EAs can easily be implemented and outperform other regular single-population algorithms to a large extent.
In this paper, five models based on evolutionary algorithms (EAs) are introduced and compared for the optimization of the design and rehabilitation of water distribution networks. These EAs include the genetic algorit...
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In this paper, five models based on evolutionary algorithms (EAs) are introduced and compared for the optimization of the design and rehabilitation of water distribution networks. These EAs include the genetic algorithm (GA), the particle swarm optimization (PSO), the ant colony optimization (ACO), the memetic algorithm (MA), and the modified shuffled frog leaping algorithm (SFLA). A brief description of each algorithm is introduced to explain its application. A methodology is applied for the rigorous comparison of the models in terms of the optimum solution obtained, the number of objective function evaluations corresponding to the optimum solution, the effect of starting seeds on the optimum solution, and the quality of the results. A statistical analysis is carried out and then an efficiency-rate metric is determined to assess the performance of each model. The five EAs are applied to two popular benchmark networks, the two-loop network and the New York tunnels. In addition, the models are applied to a real water distribution network of El-Mostakbal City, Egypt. The results show that the PSO outperformed the other evolutionary algorithms in terms of the efficiency-rate metric and the rapid convergence to the best solution. (c) 2017 American Society of Civil Engineers.
This paper explores the performance of three evolutionary optimization methods, differential evolution (DE), evolutionary strategy (ES) and biogeography based optimization algorithm (BBO), for nonlinear constrained op...
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This paper explores the performance of three evolutionary optimization methods, differential evolution (DE), evolutionary strategy (ES) and biogeography based optimization algorithm (BBO), for nonlinear constrained optimum design of a cantilever retaining wall. These algorithms are based on biological contests for survival and reproduction. The retaining wall optimization problem consists of two criteria, geotechnical stability and structural strength, while the final design minimizes an objective function. The objective function is defined in terms of both cost and weight. Constraints are applied using the penalty function method. The efficiency of the proposed method is examined by means of two numerical retaining wall design examples, one with a base shear key and one without a base shear key. The final designs are compared to the ones determined by genetic algorithms as classical metaheuristic optimization methods. The design results and convergence rate of the BBO algorithm show a significantly better performance than the other algorithms in both design cases.
This paper discusses the thermal and energy performance of a detached lightweight building. The building was monitored with hygrothermal sensors to collect data for building energy model calibration. The calibration w...
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This paper discusses the thermal and energy performance of a detached lightweight building. The building was monitored with hygrothermal sensors to collect data for building energy model calibration. The calibration was performed using a dynamic simulation through EnergyPlus (R) (EP) (Version 8.5, United States Department of Energy (DOE), Washington, DC, USA) with a hybrid evolutionary algorithm to minimise the root mean square error of the differences between the predicted and real recorded data. The results attained reveal a good agreement between predicted and real data with a goodness of fit below the limits imposed by the guidelines. Then, the evolutionary algorithm was used to meet the compliance criteria defined by the Passive House standard for different regions in Portugal's mainland using different approaches in the overheating evaluation. The multi-objective optimisation was developed to study the interaction between annual heating demand and overheating rate objectives to assess their trade-offs, tracing the Pareto front solution for different climate regions throughout the whole of Portugal. However, the overheating issue is present, and numerous best solutions from multi-objective optimisation were determined, hindering the selection of a single best option. Hence, the life cycle cost of the Pareto solutions was determined, using the life cycle cost as the final criterion to single out the optimal solution or a combination of parameters.
A simple island model with islands and migration occurring after every iterations is studied on the dynamic fitness function Maze. This model is equivalent to a EA if , i. e., migration occurs during every iteration. ...
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A simple island model with islands and migration occurring after every iterations is studied on the dynamic fitness function Maze. This model is equivalent to a EA if , i. e., migration occurs during every iteration. It is proved that even for an increased offspring population size up to , the EA is still not able to track the optimum of Maze. If the migration interval is chosen carefully, the algorithm is able to track the optimum even for logarithmic . The relationship of , and the ability of the island model to track the optimum is then investigated more closely. Finally, experiments are performed to supplement the asymptotic results, and investigate the impact of the migration topology.
In this paper, an Aircraft Research Flight Simulator equipped with Flight Dynamics Level D (highest level) was used to collect flight test data and develop new controller methodologies. The changes in the aircraft'...
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In this paper, an Aircraft Research Flight Simulator equipped with Flight Dynamics Level D (highest level) was used to collect flight test data and develop new controller methodologies. The changes in the aircraft's mass and center of gravity position are affected by the fuel burn, leading to uncertainties in the aircraft dynamics. A robust controller was designed and optimized using the H-infinity method and two different metaheuristic algorithms;in order to ensure acceptable flying qualities within the specified flight envelope despite the presence of uncertainties. The H 1 weighting functions were optimized by using both the genetic algorithm, and the differential evolution algorithm. The differential evolution algorithm revealed high efficiency and gave excellent results in a short time with respect to the genetic algorithm. Good dynamic characteristics for the longitudinal and lateral stability control augmentation systems with a good level of flying qualities were achieved. The optimal controller was used on the Cessna Citation X aircraft linear model for several flight conditions that covered the whole aircraft's flight envelope. The novelty of the new objective function used in this research is that it combined both time-domain performance criteria and frequency-domain robustness criterion, which led to good level aircraft flying qualities specifications. The use of this new objective function helps to reduce considerably the calculation time of both algorithms, and avoided the use of other computationally more complicated methods. The same fitness function was used in both evolutionary algorithms (differential evolution and genetic algorithm), then their results for the validation of the linear model in the flight points were compared. Finally, robustness analysis was performed to the nonlinear model by varying mass and gravity center position. New tools were developed to validate the results obtained for both linear and nonlinear aircraft models. It was co
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