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IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)

作者： Cheng, Zhuoqi Foong, Shaohui Sun, Defeng Tan, U-Xuan Singapore Univ Technol & Design SUTDMIT Int Design Ctr Singapore Singapore Singapore Univ Technol & Design Pillar Engn Prod Dev Singapore Singapore Natl Univ Singapore Dept Math Fac Sci Singapore Singapore

ISBN: (纸本)9781479957361

Compliant mechanisms have been widely used in various mechatronics and robotics applications as they offer several significant advantages like repeatability and non-existence of backlash. The designs of these compliant mechanisms are often based on experience and intuition, and can be a frustrating experience during the design phase. Hence, this paper proposes an algorithm that aids users in designing compliant mechanisms for torsional applications in a systematic manner. With the proposed algorithm, users can input the desired torque-angular displacement stiffness property into the proposed algorithm, and the algorithm will output an appropriate compliant mechanism design accordingly. In this paper, two scenarios are explored, namely: 1) robotic arm with minimal impedance due to its own mass so as to reduce the required motor size, and 2) desired resonant frequency for better control or energy harvesting purposes. These two scenarios are used as motivation to input the required information to the algorithm, which generates the appropriate solution of compliant mechanism designs to address the need of the scenarios. Prototypes are developed for the above mentioned scenarios, and experiments are conducted to demonstrate the performance of the proposed algorithm.

关键词： algorithm design and analysis Energy harvesting Manufacturing processes Optimization Robots Shape Torque Manufacturing process algorithm design and analysis energy harvesting Robot Torque algorithms algorithm design

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Floating-point genetic algorithm for design of a reconfigurable antenna arrays by phase-only control

Floating-point genetic algorithm for design of a reconfigura...

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Asia-Pacific Microwave Conference

作者： Mahanti, G. K. Chakraborty, A. Das, S. Indian Inst Technol Dept Elect & Elect Commun Engn Kharagpur 721302 W Bengal India

ISBN: (纸本)078039433X

In this paper we propose a floating-point Genetic algorithm (GA) for optimal design of a reconfigurable antenna array by varying only the phase excitations of the radiators while fixing the common amplitude distribution constant. This common amplitude distribution is jointly optimized with phases to generate dual beam pattern antenna array. Results show a good agreement between the desired and GA synthesized pattern. Results are very encouraging even without minimizing the amplitude excitation dynamic range ratio (DRR).

关键词： antenna phased arrays antenna radiation patterns antenna theory floating point arithmetic genetic algorithms DRR GA antenna design common amplitude distribution dual beam pattern antenna array dynamic range ratio floating-point genetic algorithm phase-on Genetic algorithm (GA) antenna phased arrays floating point arithmetic Antenna arrays antenna theory antenna design antenna radiation patterns Dynamic range amplitude distribution FLOATING POINTS algorithm design configurability

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Teaching algorithm design: A Literature Review

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ACM Transactions on Computing Education 1000年

作者： Jonathan Liu Seth Poulsen Erica Goodwin Hongxuan Chen Grace Williams Yael Gertner Diana Franklin University of Chicago Utah State University University of Illinois Urbana-Champaign

algorithm design is a vital skill developed in most undergraduate Computer Science (CS) programs, but few research studies focus on pedagogy related to algorithms coursework. To understand the work that has been done in the area, we present a systematic survey and literature review of CS Education studies. We search for research that is both related to algorithm design (as described by the ACM Curricular Guidelines) and evaluated on post-secondary-level students. Across all venues we searched prior to July 2024, we only find 102 such *** first classify these papers by topic, evaluation metric, evaluation methods, and intervention target. Through our classification, we find a broad sparsity of papers which indicates that many open questions remain about teaching algorithm design. We also note the need for papers using rigorous research methods, as only 43 out of 92 papers presenting quantitative data use statistical tests, and only 16 out of 47 papers presenting qualitative data follow a coding scheme. Only 18 papers report controlled trials. In addition, almost all authors only contribute to one publication, an indication that few groups are specializing on these *** then synthesize the results of the existing literature to give insights into what the corpus reveals about how we should teach algorithms. Broadly, we find that much of the literature explores implementing well-established practices, such as active learning or automated assessment, in the algorithms classroom. However, there are algorithms-specific results as well: a number of papers find that students may under-utilize certain algorithmic design techniques, and studies describe a variety of ways to select algorithms problems that increase student engagement and *** results we present, along with the publicly available set of papers collected, provide a detailed representation of the current corpus of CS Education work related to algorithm design and can orient further research in t

关键词： algorithms algorithm design Literature Review

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Learning to design Greedy algorithm for NP-Complete Problems 42nd

Learning to Design Greedy Algorithm for NP-Complete Problems

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42nd National Conference on Theoretical Computer Science

作者： Ding, Zhenxin Huang, Zihao Sui, Jingyan Liu, Ruizhi Ding, Shizhe Xu, Liming Wang, Chao Zhang, Haicang Yu, Chungong Bu, Dongbo Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9789819614899;9789819614905

algorithm design is an art that heavily requires human designers' intuition, expertise, and insights into the combinatorial structure of the problems under consideration. In particular, while greedy algorithms for NP-complete problems are generally straightforward, finding an effective greedy-selection rule is consistently challenging. In the study, we introduce an approach called artificial intelligence algorithmist for set cover problem (AIA-SC), which leverages neural networks as greedy-selection rules to solve the minimum weighted set cover problem (WSCP), an NP-complete problem. Initially, we formulate a given WSCP as a 0-1 integer linear program (ILP), in which each variable xi is binary: x(i) = 0 indicating the exclusion of set s(i), and x(i) = 1 indicating the selection of the set. Subsequently, we design a generic search framework to identify the optimal solution to the ILP. At each search step, the value of a variable is determined with the aid of neural networks. The key ingredients of our neural network involve the design of its loss function and training process: the original ILP problem and the sub-problems generated by assigning a variable x(i) should satisfy the Bellman-Ford equation, which enables us to set the violation of Bellman-Ford equation as loss function of our neural network. Experimental results on representative instances indicate that the neural network-based greedy selection rule effectively identifies optimal solutions and surpasses the performance of the human-crafted Chvatal's greedy algorithm. Furthermore, the basic idea of our approach can be readily extended to design greedy algorithms for other NP-hard problems without significant modification. The source code of AIA-SC and data sets are available at https://***/zxding94/aia-sc.

关键词： algorithm design Greedy-selection Rules Neural Networks Minimum Weighted Set Cover Problem

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The solution of super-scale DEA models based on preference character

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ALEXANDRIA ENGINEERING JOURNAL 2025年 110卷 297-309页

作者： Ma, Zhanxin Li, Hao Yinjie Fan, Ziwei Inner Mongolia Univ Sch Econ & Management Hohhot 010021 Peoples R China Inner Mongolia Univ Technol Inner Mongolia Univ Technol Lib Hohhot 010051 Peoples R China

The basic relationships and intrinsic laws in the data envelopment analysis (DEA) are the basis for designing fast solutions for big data DEA models. By delving into these relationships, it becomes feasible to address super-scale DEA problems more swiftly, aligning with the need for timeliness in real-world efficiency evaluations and reducing time costs. This paper leverages the intrinsic characteristics of DEA to formulate a DEA model scale reduction approach, offering reliable theoretical support for solving super- scale DEA problems. Initially, the partially ordered set theory to prove the relationship between the efficient decision making units (DMUs) of DEA and the maximal element of the partially ordered set. Building on this, a DEA model based on maximal element set (DEA-PS) is constructed based on the theory of partially ordered set theory, and the equivalence between DEA-PS model and the original DEA model is proved. Furthermore, it is proved that the dominant theorem exists in the solution of multiple classical DEA models, and a method of super-scale DEA models based on dominant theorem is given. The conclusions of this paper theoretically reveal the relationship between efficient DMUs and maximal elements, from this relationship, two key insights emerge: (1) Regardless of how huge the number of DMU is, the proposed method in this paper can realize the scale reduction of the DEA model quickly, (2) The algorithm has good compatibility with all current big data DEA model algorithms.

关键词： Comprehensive assessment Super scale Data envelopment analysis algorithm design Dominant theorem

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Order acceptance and scheduling with weighted makespan

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4OR-A QUARTERLY JOURNAL OF OPERATIONS RESEARCH 2025年 1-21页

作者： Lu, Lingfa Zuo, Lili Zhang, Liqi Ou, Jinwen Zhengzhou Univ Sch Math & Stat Zhengzhou Henan Peoples R China Henan Agr Univ Coll Informat & Management Sci Zhengzhou Henan Peoples R China Jinan Univ Sch Management Guangzhou Guangdong Peoples R China

In this paper, we study eight order acceptance and scheduling (OAS) problems by considering job splitting, a fixed number of distinct processing times, weights or rejection costs, and position-dependent processing times, weights or rejection costs, respectively. In each OAS problem in consideration, each job is either accepted and processed on a single machine, or rejected by paying a rejection cost, and the objective is to minimize the weighted makespan (maximum weighted completion time) of the accepted jobs plus the total rejection cost of the rejected jobs. We show that three of the eight OAS problems can be solved in polynomial time, another three of the eight OAS problems are NP-hard, but the computational complexities of the remaining two OAS problems are open. For each of those three NP-hard problems, we propose a pseudo-polynomial-time dynamic programming algorithm and an efficient approximation algorithm. Based on the vector trimming technique, we also obtain a fully polynomial time approximation scheme (FPTAS) for each of those NP-hard problems.

关键词： Scheduling Rejection Weighted makespan NP-hardness algorithm design

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An adaptive helper and equivalent objective evolution strategy for constrained optimization

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INFORMATION SCIENCES 2025年 690卷

作者： Xu, Tao Chen, Hongyang He, Jun Zhejiang Lab Hangzhou 311121 Peoples R China Nottingham Trent Univ Dept Comp Sci Nottingham NG11 8NS England

The matrix adaptation evolution strategy is a simplified covariance matrix adaptation evolution strategy with reduced computational cost. Using it as a search engine, several algorithms have been recently proposed for constrained optimization and have shown excellent performance. However, these algorithms require the simultaneous application of multiple techniques to handle constraints, and also require gradient information. This makes them inappropriate for handling non-differentiable functions. This paper proposes a new matrix adaption evolutionary strategy for constrained optimization using helper and equivalent objectives. The method optimizes two objectives but with no need for special handling of infeasible solutions and without gradient information. A new mechanism is designed to adaptively adjust the weights of the two objectives according to the convergence rate. The efficacy of the proposed algorithm is evaluated using computational experiments on the IEEE CEC 2017 Constrained Optimization Competition benchmarks. Experimental results demonstrate that it outperforms current state-ofthe-art evolutionary algorithms. Furthermore, this paper provides sufficient conditions for the convergence of helper and equivalent objective evolutionary algorithms and proves that using helper objectives can reduce the likelihood of premature convergence.

关键词： Constrained optimization Evolutionary algorithms algorithm analysis algorithm design Multi-objective optimization

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A two-stage optimization approach to image-capture window positioning and path planning for automatic optical inspection

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SWARM AND EVOLUTIONARY COMPUTATION 2025年 92卷

作者： Yan, Xiaohui Zhang, Yukang Luo, Junwei Cheng, Shi Zhang, Zhicong Zhang, Liangwei Zhang, Zhengmin Dongguan Univ Technol Sch Mech Engn Dongguan 523808 Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian 710119 Peoples R China

Automatic optical inspection is an effective method to improve inspection efficiency in the manufacturing industry. This study has divided the automatic optical image acquisition problem into two subproblems and proposed a two-stage image acquisition method for incremental optimization to mitigate the time-consuming nature of traditional sequential image acquisition. An enhanced iterative self-organizing data algorithm is initially employed, integrating selection and crossover operators based on genetic algorithms to address the challenge of clustering image-capture windows. Regarding image acquisition path planning, a hybrid framework was introduced that dynamically combines swarm intelligence algorithms to identify optimal image-capture locations and a 2-opt algorithm to optimize the sequence of accessing these locations. Additionally, a boundary mapping strategy is introduced to reduce the search space effectively. Based on this hybrid framework, an improved brain storm optimization algorithm is proposed to accelerate convergence speed and enhance optimization precision. Furthermore, a neighborhood adjustment strategy is implemented to refine the inspection path further. Experimental results validate the efficacy of the proposed two-stage image acquisition method, demonstrating significant improvements in efficiency and performance.

关键词： Computational intelligence Brain storm optimization algorithm design Path planning Automatic optical inspection

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A Multi-Surrogate Assisted Multi-Tasking Optimization algorithm for High-Dimensional Expensive Problems

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algorithmS 2025年第1期18卷 4-4页

作者： Li, Hongyu Chen, Lei Zhang, Jian Li, Muxi Jiangsu Univ Fac Civil Engn & Mech Zhenjiang 212013 Peoples R China Northwestern Polytech Univ Ocean Inst Taicang 215400 Peoples R China Tianjin Univ Sch Mech Engn Tianjin 300354 Peoples R China

Surrogate-assisted evolutionary algorithms (SAEAs) are widely used in the field of high-dimensional expensive optimization. However, real-world problems are usually complex and characterized by a variety of features. Therefore, it is very challenging to choose the most appropriate surrogate. It has been shown that multiple surrogates can characterize the fitness landscape more accurately than a single surrogate. In this work, a multi-surrogate-assisted multi-tasking optimization algorithm (MSAMT) is proposed that solves high-dimensional problems by simultaneously optimizing multiple surrogates as related tasks using the generalized multi-factorial evolutionary algorithm. In the MSAMT, all exactly evaluated samples are initially grouped to form a collection of clusters. Subsequently, the search space can be divided into several areas based on the clusters, and surrogates are constructed in each region that are capable of completely describing the entire fitness landscape as a way to improve the exploration capability of the algorithm. Near the current optimal solution, a novel ensemble surrogate is adopted to achieve local search in speeding up the convergence process. In the framework of a multi-tasking optimization algorithm, several surrogates are optimized simultaneously as related tasks. As a result, several optimal solutions spread throughout disjoint regions can be found for real function evaluation. Fourteen 10- to 100-dimensional test functions and a spatial truss design problem were used to compare the proposed approach with several recently proposed SAEAs. The results show that the proposed MSAMT performs better than the comparison algorithms in most test functions and real engineering problems.

关键词： multi-tasking optimization algorithm algorithm design dynamic ensemble of surrogates decision space partition expensive optimization problem

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Towards a More Efficient Selection Monad 25th

Towards a More Efficient Selection Monad

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25th International Symposium on Trends in Functional Programming

作者： Hartmann, Johannes Schrijvers, Tom Gibbons, Jeremy Univ Oxford Dept Comp Sci Oxford England Katholieke Univ Leuven Dept Comp Sci Leuven Belgium

ISBN: (纸本)9783031745577;9783031745584

This paper explores a novel approach to selection functions through the introduction of a generalised selection monad. The foundation is laid with the conventional selection monad J, defined as (A -> R) -> A, together with various combinators for computing new selection functions from old. However, inefficiencies in these combinators are identified. To address these issues, a specialised type K is introduced, and its isomorphism to J is demonstrated. The paper further generalises the K type to G, where performance improvements and enhanced intuitive usability are observed. The embeddings between J and G are established, offering a more efficient and expressive alternative to the well established J type for selection functions. The findings emphasise the advantages of the generalised selection monad and its applicability in diverse scenarios, paving the way for further exploration and optimisation.

关键词： Selection monad Functional programming algorithm design Performance Optimisation Monads

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