Hydraulic-actuated legs for quadruped robots excel in producing high force and offering precise control. Although the overall efficiency of hydraulic servo systems can be limited by pump and valve losses, the local me...
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Hydraulic-actuated legs for quadruped robots excel in producing high force and offering precise control. Although the overall efficiency of hydraulic servo systems can be limited by pump and valve losses, the local mechanical efficiency from the actuator to the leg mechanism can be relatively high. This study introduces an optimization driven methodology for designing and validating robotic leg mechanisms using evolutionary algorithms. By solving three distinct optimization problems, the study addresses trajectory tracking accuracy and force transmission efficiency. The resulting design was experimentally validated, demonstrating reliable motion reproduction with minimal deviation and achieving a force transmission efficiency of 94%. These results demonstrate the feasibility of translating optimization outcomes into high-performing physical prototypes, providing a robust framework for future robotic mechanism development.
Increasing sediment yield is one of the important environmental challenges in river basins resulting from changing land *** current study develops an adaptive neuro fuzzy inference system(ANFIS)hy-bridized with evolut...
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Increasing sediment yield is one of the important environmental challenges in river basins resulting from changing land *** current study develops an adaptive neuro fuzzy inference system(ANFIS)hy-bridized with evolutionary algorithms to predict annual sediment yield at the catchment scale consid-ering some key factors affecting the alteration of the sediment *** key factors consist of the area of the sub-catchments,average slope of the sub-catchments,rainfall,and forest index,and the output of the model is sediment *** indices such as the Nash-Sutcliffe efficiency(NSE),root mean square error and vulnerability index(Ⅵ)were applied to evaluate the performance of the ***,hybrid models were compared in terms of complexities to select the best *** on the results in Talar River basin in Iran,several hybrid models in which particle swarm optimization(PSO),genetic algorithm,invasive weed optimization,biogeography-based optimization,and shuffled complex evolu-tion used to train the neuro fuzzy network are able to generate reliable sediment yield *** NSE of all previously listed models is more than 0.8 which means they are robust for assessing sediment yield resulting from land use change with a focus on *** proposed models are fairly similar in terms of computational complexities which implies no priority for selecting the best ***,PSO-ANFIS performed slightly better than the other models especially in terms of accuracy of the outputs due to a high NSE(0.92)and a low Ⅵ(1.9 Mg/ha).Using the proposed models is recommended due to the lower required time and data compared to a physically based models such as the The Soil and Water Assessment ***,some drawbacks restrict the application of the proposed *** example,the proposed models cannot be used for small temporal scales.
Monte Carlo Tree Search (MCTS) is a best-first sampling/planning method used to find optimal decisions. The effectiveness of MCTS depends on the construction of its statistical tree, with the selection policy playing ...
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Monte Carlo Tree Search (MCTS) is a best-first sampling/planning method used to find optimal decisions. The effectiveness of MCTS depends on the construction of its statistical tree, with the selection policy playing a crucial role. A particularly effective selection policy in MCTS is the Upper Confidence Bounds for Trees (UCT). While MCTS/UCT generally performs well, there may be variants that outperform it, leading to efforts to evolve selection policies for use in MCTS. However, these efforts have often been limited in their ability to demonstrate when these evolved policies might be beneficial. They frequently rely on single, poorly understood problems or on new methods that are not fully comprehended. To address these limitations, we use three evolutionary-inspired methods: evolutionary Algorithm (EA)-MCTS, Semantically-inspired EA (SIEA)-MCTS as well as Self-adaptive (SA)-MCTS, which evolve online selection policies to be used in place of UCT. We compare these three methods against five variants of the standard MCTS on ten test functions of varying complexity and nature, including unimodal, multimodal, and deceptive features. By using well-defined metrics, we demonstrate how the evolution of MCTS/UCT can yield benefits in multimodal and deceptive scenarios, while MCTS/UCT remains robust across all functions used in this work.
This paper introduces the Square Shape Slope Index (SSSI), a novel post-optimization multi-criteria decisionmaking (MCDM) approach for analyzing Pareto fronts generated from bi-objective optimization problems. SSSI le...
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This paper introduces the Square Shape Slope Index (SSSI), a novel post-optimization multi-criteria decisionmaking (MCDM) approach for analyzing Pareto fronts generated from bi-objective optimization problems. SSSI leverages multiple Utopia and Nadir points-guided by a user-defined priority scale-to form a dynamic square region around particular segments of the Pareto front. Within this region, slope-based evaluations are used to rank solutions based on user preferences and criteria. The method's effectiveness is demonstrated through empirical tests on diverse benchmark functions and real-world scenarios, such as energy distribution and portfolio optimization, each encompassing various shapes and patterns of the Pareto front. In addition, SSSI is compared against established decision-making approaches both geometrically and analytically using different aggregation methods. To account for the stochastic nature of evolutionary algorithms, the Non-Dominated Sorting Genetic Algorithm (NSGA-II) is employed to generate Pareto fronts for each test function. Results confirm the robustness and adaptability of SSSI, offering a clear and flexible framework for balancing conflicting objectives in multi-objective decision-making contexts.
Many conventional search engines satisfy the need of information retrieval from WWW, but the results obtained still hold a chance for refinement and accuracy. This problem of getting irrelevant results is specifically...
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ISBN:
(纸本)9781479928996
Many conventional search engines satisfy the need of information retrieval from WWW, but the results obtained still hold a chance for refinement and accuracy. This problem of getting irrelevant results is specifically observed for complex queries i.e. queries with many key words. We propose an intelligent method for web mining based on Genetic Algorithm (GA). The results produced by conventional search engine i.e. snippets, are further processed and refined further to extract only the most relevant snippets. A significant improvement is observed in the search results by using a modified GA with additional local searching technique of Memetic Algorithm (MA).
In this paper, we present a cosimulation environment that seamlessly integrates design and simulation tools used for mechanical parts design and optimization. The proposed environment enables use of tools from differe...
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ISBN:
(纸本)9781479928064
In this paper, we present a cosimulation environment that seamlessly integrates design and simulation tools used for mechanical parts design and optimization. The proposed environment enables use of tools from different vendors in addition to the developer's own developed tools. This way, the individual parts or components of the mechanical assembly no longer need to be designed, simulated, and optimized apart from each other. Furthermore, integrated cosimulation facilitates developing the integration methodology for the design, test and parameter optimization of these components. Within the presented environment, an evolutionary optimization method is applied to a novel four bar steering mechanism. It is used to evolve its morphology that requires minimal steering forces. A precise cosimulation is used to dynamically model the assembly, all constraints, and to evaluate each morphology by analyzing the required forces for accomplishing a desired steering maneuver.
Coevolutionary multi-objective heuristics solve multi-objective optimization problems by evolving two different heuristics, simultaneously while exchanging information to produce diverse solutions and faster convergen...
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Coevolutionary multi-objective heuristics solve multi-objective optimization problems by evolving two different heuristics, simultaneously while exchanging information to produce diverse solutions and faster convergence. However, evolving two algorithms concurrently is computationally intensive and slow. In this research article, we study the parallelization of Cooperative, Concurrent, Coevolutionary for Multi-objective Optimization (COMO3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{CO}}_{{{\text{MO}}}}<^>{{\textevolutionary}}$$\end{document}) algorithm, designed for dynamic problems. The two evolutionary algorithms, Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-objective evolutionary Algorithm based on Decomposition (MOEA/D) are parallelized on GPU and CPU architectures, respectively. The populations in MOEA/D are further partitioned forming an island topology to preserve diversity. Using the bi-objective traveling salesperson benchmark dataset, we analyze the performance of the individual algorithms and coevolutionary algorithm with respect to time and accuracy of the results.
This paper puts forward a proposal for combining multi-operator evolutionary algorithms (EAs), in which three EAs, each with multiple search operators, are used. During the evolution process, the algorithm gradually e...
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ISBN:
(纸本)9781479914883
This paper puts forward a proposal for combining multi-operator evolutionary algorithms (EAs), in which three EAs, each with multiple search operators, are used. During the evolution process, the algorithm gradually emphasizes on the best performing multi-operator EA, as well as the search operator. The proposed algorithm is tested on the CEC2014 single objective real-parameter competition. The results show that the proposed algorithm has the ability to reach good solutions.
Genetic algorithms (GAs) are meta-heuristic algorithms that are used for solving constrained and unconstrained optimization problems, mimicking the process of natural selection in biological evolution. Due to the fact...
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Genetic algorithms (GAs) are meta-heuristic algorithms that are used for solving constrained and unconstrained optimization problems, mimicking the process of natural selection in biological evolution. Due to the fact that GAs do not require the optimization function to be differentiable, they are suitable for application in cases where the derivative of the objective function is either unavailable or impractical to obtain numerically. This paper proposes a general purpose genetic algorithm toolkit, implemented in Python3 programming language, having only minimum dependencies in NumPy and Joblib, that handle some of the numerical and parallel execution details.
The increasing global demand for sustainable and cleaner transportation has led to extensive research on alternative fuels for Internal Combustion (IC) engines. One promising option is the utilization of methane/hydro...
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The increasing global demand for sustainable and cleaner transportation has led to extensive research on alternative fuels for Internal Combustion (IC) engines. One promising option is the utilization of methane/hydrogen blends in Spark-Ignition (SI) engines due to their potential to reduce Green House Gas (GHG) emissions and improve engine performance. However, the optimal operation of such an engine is challenging due to the interdependence of multiple conflicting objectives, including Brake Mean Effective Pressure (BMEP), Brake Specific Fuel Consumption (BSFC), and nitrogen oxide (NOx) emissions. This paper proposes an evolutionary optimization algorithm that employs a surrogate model as a fitness function to optimize methane/hydrogen SI engine performance and emissions. To create the surrogate model, we propose a novel ensemble learning algorithm that consists of several base learners. This paper employs ten different learning algorithms diversified via the Wagging method to create a pool of base-learner algorithms. This paper proposes a combinatorial evolutionary pruning algorithm to select an optimal subset of learning algorithms from a pool of base learners for the final ensemble algorithm. Once the base learners are designed, they are incorporated into an ensemble, where their outputs are aggregated using a weighted voting scheme. The weights of these base learners are optimized through a gradient descent algorithm. However, when optimizing a problem using surrogate models, the fitness function is subject to approximation uncertainty. To address this issue, this paper introduces an uncertainty reduction algorithm that performs averaging within a sphere around each solution. Experiments are performed to compare the proposed ensemble learning algorithm to the classical learning algorithms and state-of-the-art ensemble algorithms. Also, the proposed smoothing algorithm is compared with the state-ofthe-art evolutionary algorithms. Experimental studies suggest th
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