We address the placement of emergency exits to facilitate evacuation from supermarket-like environments. Using a simulation-based approach, evolutionary algorithms are shown to provide good results compared to a greed...
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This paper uses evolutionary optimization algorithms to study the multi-objective optimization of mechanically stabilized earth (MSE) retaining walls. Five multi-objective optimization algorithms, including the non-do...
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This paper uses evolutionary optimization algorithms to study the multi-objective optimization of mechanically stabilized earth (MSE) retaining walls. Five multi-objective optimization algorithms, including the non-dominated sorting genetic algorithm II (NSGA-II), strength Pareto evolutionary algorithm II (SPEA-II), multi-objective particle swarm optimization (MOPSO), multi-objective multi-verse optimization (MVO), and Pareto envelope-based selection algorithm II (PESA-II), are applied to the design procedure. MSE wall design requires two major requirements: external stability and internal stability. In this study, the optimality criterion is to minimize cost and its trade-off with the factor of safety (FOS). To this end, two objectives are defined: (1) minimum cost, (2) maximum FOS. Three different strategies are considered for reinforcement combinations in the numerical simulations. Moreover, a sensitivity analysis was conducted on the variation of significant parameters, including backfill slope, wall height, horizontal earthquake coefficient, and surcharge load. The efficiency of the utilized algorithms was assessed through three well-known coverage set measures, diversity, and hypervolume. These measures were further examined using basic statistical measures (i.e., min, max, standard deviation) and the Friedman test with a 95% confidence level.
Final productive fitness is an a posteriori fitness estimate for evolutionary algorithms that takes into account the fitness of an individual's descendants. We use that metric in the context of surrogate-based evo...
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Multi-objective evolutionary algorithms have been shown to solve multi-objective optimization problems well and have been very widely used, but there are still drawbacks such as failure to develop sufficient environme...
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We analyze the performance of panmictic evolutionary algorithms in byzantine environments in which fitness can be computed by malicious agents. We measure the influence of the rate of unreliability of the environment,...
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ISBN:
(纸本)9798400701207
We analyze the performance of panmictic evolutionary algorithms in byzantine environments in which fitness can be computed by malicious agents. We measure the influence of the rate of unreliability of the environment, and the effect that a simple mechanism based on redundant computation can have on the results attained.
Game economy design significantly shapes the player experience and progression speed. Modern game economies are becoming increasingly complex and can be very sensitive to even minor numerical adjustments, which may ha...
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Large Language Models (LLMs) excel in various tasks, but they rely on carefully crafted prompts that often demand substantial human effort. To automate this process, in this paper, we propose a novel framework for dis...
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The paper presents an approach to the optimal design of actively lubricated hybrid bearings with non-circular bore geometry. The novelty of the proposed approach is the automated and joint assessment of the performanc...
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We analyze the energy consumption of running evolutionary algorithms in batch as a function of the rest time between runs. It is shown that energy consumption can be reduced by 5%-8% by inserting short pauses between ...
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We present a computational approach combining evolutionary algorithms with calculation software nextnano++ to optimize the design of a nitride-based ultraviolet light-emitting diode (UV LED). Our findings reveal a sig...
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ISBN:
(纸本)9798350347227
We present a computational approach combining evolutionary algorithms with calculation software nextnano++ to optimize the design of a nitride-based ultraviolet light-emitting diode (UV LED). Our findings reveal a significant improvement of the theoretical internal quantum efficiency of the nanostructure at the target wavelength 300 nm.
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