A Bi-Level Programming-Based Method for Service Composition Optimization of Collaborative Manufacturing of Sewing Machine Cases

Machines, Journal Year: 2025, Volume and Issue: 13(3), P. 195 - 195

Published: Feb. 28, 2025

Aiming at the problem of optimizing composition manufacturing resources in part-level outsourcing sewing machine case manufacturing, this paper proposes a service optimization method based on bi-level programming. We analyze structure and production process cases, determine required resources, establish evaluation indicator system line with interests multiple parties. also introduce idea programming, construct model cases planning, characteristics NSGA-Ⅱ (Non-dominated Sorting Genetic Algorithm II) algorithm improvement strategy, complete solution cases. The experimental results show that strategy can well solve programming complex feature information avoid falling into local optimal solution.

Language: Английский

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Modified crayfish optimization algorithm with adaptive spiral elite greedy opposition-based learning and search-hide strategy for global optimization

Journal of Computational Design and Engineering, Journal Year: 2024, Volume and Issue: 11(4), P. 249 - 305

Published: July 3, 2024

Abstract Crayfish optimization algorithm (COA) is a novel bionic metaheuristic with high convergence speed and solution accuracy. However, in some complex problems real application scenarios, the performance of COA not satisfactory. In order to overcome challenges encountered by COA, such as being stuck local optimal insufficient search range, this paper proposes four improvement strategies: search-hide, adaptive spiral elite greedy opposition-based learning, competition-elimination, chaos mutation. To evaluate accuracy, speed, robustness modified crayfish (MCOA), simulation comparison experiments 10 algorithms are conducted. Experimental results show that MCOA achieved minor Friedman test value 23 functions, CEC2014 CEC2020, average superiority rates 80.97%, 72.59%, 71.11% WT, respectively. addition, shows applicability progressiveness five engineering actual industrial field. Moreover, 80% 100% rate against on CEC2020 fixed-dimension function benchmark functions. Finally, owns better population diversity.

Language: Английский

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Wild Gibbon Optimization Algorithm

Computers, materials & continua/Computers, materials & continua (Print), Journal Year: 2024, Volume and Issue: 80(1), P. 1203 - 1233

Published: Jan. 1, 2024

Complex optimization problems hold broad significance across numerous fields and applications. However, as the dimensionality of such increases, issues like curse local optima trapping also arise. To address these challenges, this paper proposes a novel Wild Gibbon Optimization Algorithm (WGOA) based on an analysis wild gibbon population behavior. WGOA comprises two strategies: community search competition. The strategy facilitates information exchange between families, generating multiple candidate solutions to enhance algorithm diversity. Meanwhile, competition reselects leaders for after each iteration, thus enhancing precision. assess algorithm's performance, CEC2017 CEC2022 are chosen test functions. In suite, secures first place in 10 benchmark functions, obtained rank 5 ultimate experimental findings demonstrate that outperforms others tested This underscores strong robustness stability tackling complex single-objective problems.

Language: Английский

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An advanced RIME Optimizer with Random Reselection and Powell Mechanism for Engineering Design

Journal of Computational Design and Engineering, Journal Year: 2024, Volume and Issue: 11(6), P. 139 - 179

Published: Oct. 18, 2024

Abstract RIME is a recently introduced optimization algorithm that draws inspiration from natural phenomena. However, has certain limitations. For example, it prone to falling into Local Optima, thus failing find the Global and problem of slow convergence. To solve these problems, this paper introduces an improved (PCRIME), which combines random reselection strategy Powell mechanism. The enhances population diversity helps escape while mechanism improve convergence accuracy optimal solution. verify superior performance PCRIME, we conducted series experiments at CEC 2017 2022, including qualitative analysis, ablation studies, parameter sensitivity comparison with various advanced algorithms. We used Wilcoxon signed-rank test Friedman confirm advantage PCRIME over its peers. experimental data show ability robustness. Finally, applies five real engineering problems proposes feasible solutions comprehensive index definitions for prove stability proposed algorithm. results can not only effectively practical but also excellent stability, making

Language: Английский

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A cosine adaptive particle swarm optimization based long-short term memory method for urban green area prediction

PeerJ Computer Science, Journal Year: 2024, Volume and Issue: 10, P. e2048 - e2048

Published: May 28, 2024

In the quest for sustainable urban development, precise quantification of green space is paramount. This research delineates implementation a Cosine Adaptive Particle Swarm Optimization Long Short-Term Memory (CAPSO-LSTM) model, utilizing comprehensive dataset from Beijing (1998-2021) to train and test model. The CAPSO-LSTM which integrates cosine adaptive mechanism into particle swarm optimization, advances optimization long short-term memory (LSTM) network hyperparameters. Comparative analyses are conducted against conventional LSTM Partical (PSO)-LSTM frameworks, employing mean absolute error (MAE), root square (RMSE), percentage (MAPE) as evaluative benchmarks. findings indicate that model exhibits substantial improvement in prediction accuracy over manifesting 66.33% decrease MAE, 73.78% RMSE, 57.14% MAPE. Similarly, when compared PSO-LSTM demonstrates 58.36% 65.39% 50% These results underscore efficacy enhancing area prediction, suggesting its significant potential aiding planning environmental policy formulation.

Language: Английский

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An Enhanced Slime Mould Algorithm with Triple Strategy for Engineering Design Optimization

Journal of Computational Design and Engineering, Journal Year: 2024, Volume and Issue: 11(6), P. 36 - 74

Published: Oct. 16, 2024

Abstract This paper introduces an enhanced slime mould algorithm (EESMA) designed to address critical challenges in engineering design optimization. The EESMA integrates three novel strategies: the Laplace logistic sine map technique, adaptive t-distribution elite mutation mechanism, and ranking-based dynamic learning strategy. These enhancements collectively improve algorithm’s search efficiency, mitigate convergence local optima, bolster robustness complex optimization tasks. proposed demonstrates significant advantages over many conventional algorithms performs on par with, or even surpasses, several advanced benchmark tests. Its superior performance is validated through extensive evaluations diverse test sets, including IEEE CEC2014, CEC2020, CEC2022, its successful application six distinct problems. Notably, excels solving economic load dispatch problems, highlighting capability tackle challenging scenarios. results affirm that a competitive effective tool for addressing issues, showcasing potential widespread beyond.

Language: Английский

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