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Structural inverse analysis by hybrid simplex artificial bee colony algorithms

COMPUTERS & STRUCTURES 2009年第13-14期87卷 861-870页

作者： Kang, Fei Li, Junjie Xu, Qing Dalian Univ Technol Sch Civil & Hydraul Engn Dalian 116024 Peoples R China

A hybrid simplex artificial bee colony algorithm (HSABCA) which combines Nelder-Mead simplex method with artificial bee colony algorithm (ABCA) is proposed for inverse analysis problems. The proposed algorithm is applied to parameter identification of concrete dam-foundation systems. To verify the performance of HSABCA, it is compared with the basic ABCA and a real coded genetic algorithm (RCGA) on two examples: a gravity dam and an arc dam. Results show that the proposed algorithm is an efficient tool for inverse analysis and it performs much better than ABCA and RCGA on such problems. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Inverse/back analysis artificial bee colony algorithms Nelder-Mead simplex search method Real coded genetic algorithms Parameter identification Concrete dams

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Digital Filter Design Using Improved artificial bee colony algorithms

Digital Filter Design Using Improved Artificial Bee Colony A...

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作者： Rija Raju University of Windsor

学位级别：博士

Digital filters are often used in digital signal processing applications. The design objective of a digital filter is to find the optimal set of filter coefficients, which satisfies the desired specifications of magnitude and group delay responses. Evolutionary algorithms are population-based meta-heuristic algorithms inspired by the biological behaviors of species. Compared to gradient-based optimization algorithms such as steepest descent and Newton's like methods, these bio-inspired algorithms have the advantages of not getting stuck at local optima and being independent of the starting point in the solution space. The limitations of evolutionary algorithms include the presence of control parameters, problem specific tuning procedure, premature convergence and slower convergence rate. The artificial bee colony (ABC) algorithm is a swarm-based search meta-heuristic algorithm inspired by the foraging behaviors of honey bee colonies, with the benefit of a relatively fewer control parameters. In its original form, the ABC algorithm has certain limitations such as low convergence rate, and insufficient balance between exploration and exploitation in the search equations. In this dissertation, an ABC-AMR algorithm is proposed by incorporating an adaptive modification rate (AMR) into the original ABC algorithm to increase convergence rate by adjusting the balance between exploration and exploitation in the search equations through an adaptive determination of the number of parameters to be updated in every iteration. A constrained ABC-AMR algorithm is also developed for solving constrained optimization problems. There are many real-world problems requiring simultaneous optimizations of more than one conflicting objectives. Multiobjective (MO) optimization produces a set of feasible solutions called the Pareto front instead of a single optimum solution. For multiobjective optimization, if a decision maker's preferences can be incorporated during the optimization process,

关键词： artificial bee colony algorithms Digital filters Evolutionary algorithms FIR design IIR filter design Linear phase filters Optimization

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Optimizing the Friction Stir Process Parameters of the AZ31 Mg Alloy/YSZ-Al2O3 Hybrid Surface Composite Using the Response Surface Methodology-Based artificial bee colony Algorithm

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ADVANCED ENGINEERING MATERIALS 2025年第5期27卷

作者： Mohankumar, Ashokkumar Thirugnanasambandam, Arunkumar Packkirisamy, Vignesh Chennai Inst Technol Ctr Sustainable Mat & Surface Metamorphosis Chennai 600069 Tamil Nadu India

The optimization of friction stir process (FSP) parameters plays a pivotal role in enhancing the mechanical properties of friction-stirred hybrid surface composites (FSHSC), which are crucial for lightweight and high-performance structural applications. This study introduces the use of the artificial bee colony algorithm (A(BC)A) for optimizing process parameters of yttria-stabilized zirconia (YSZ) and alumina (Al2O3)-reinforced FSHSC. A three-factor, five-level central composite design matrix based on response surface methodology is utilized to develop predictive models for ultimate tensile strength (UTS) and yield strength (YS). Analysis of variance confirms the robustness and reliability of the models, with high statistical significance (p < 0.0001) and R-2 values of 0.9940 for UTS and 0.9938 for YS. A(BC)A identifies optimal parameters-tool rotational speed of 1001.543 rpm, tool transverse speed of 71.896 mm min(-1), and tool axial force of 7.23 kN-achieving UTS of 198.345 MPa and YS of 163.534 MPa. The study demonstrates that A(BC)A is a powerful tool for optimizing FSP parameters and improving hybrid composite performance. This methodology offers a framework for advancing the mechanical properties of composites, with significant implications for lightweight engineering applications.

关键词： artificial bee colony algorithms AZ31 Mg alloys friction-stirred hybrid surface composites response surface methodologies tensile characteristics

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Seamless clustering multi-hop routing protocol based on improved artificial bee colony algorithm

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2020年第1期2020卷 1-20页

作者： Zhang, Tianyi Chen, Geng Zeng, Qingtian Song, Ge Li, Chao Duan, Hua Shandong Univ Sci & Technol Coll Elect & Informat Engn Qingdao 266590 Peoples R China Shandong Univ Sci & Technol Coll Math & Syst Sci Qingdao 266590 Peoples R China

An important issue in the mobile sink wireless sensor networks (MSWSNs) is sensor energy optimization. In order to alleviate the problem of unbalanced network load and high energy consumption in MSWSNs, we proposed a new data collection protocol in this paper: seamless clustering multi-hop routing protocol based on improved artificial bee colony algorithm (IABCP). Because of limited by the communication sensing range and intelligence of ordinary nodes, routing paths can only be constructed by crude methods. And the movement of the sink node will generate a large amount of energy consumption for locating the sink node. In order to solve this problem, we assign the task of routing table generation to the sink node, which will generate the routing table through the improved artificial bee colony algorithm. In addition, we adopt a new method to select cluster head (CH) nodes;node uses the average energy of the surrounding nodes and its own residual energy to calculate the claimed cluster head time. Moreover, we added a sub cluster head CH-beta node. When the CH node reaches the number of replacement rounds, the CH-beta node becomes CH directly. The simulation results show that our routing protocol is more robust compared with three other protocols.

关键词： artificial bee colony algorithms Load balancing Network life cycle Routing protocol

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A hybrid artificial bee colony algorithm for flexible job shop scheduling with worker flexibility

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2020年第14期58卷 4406-4420页

作者： Gong, Guiliang Chiong, Raymond Deng, Qianwang Gong, Xuran Hunan Univ State Key Lab Adv Design & Mfg Vehicle Body Changsha Hunan Peoples R China Univ Newcastle Sch Elect Engn & Comp Callaghan NSW Australia

The traditional flexible job shop scheduling problem (FJSP) considers machine flexibility but not worker flexibility. Given the influence and potential of human factors in improving production efficiency and decreasing the cost in practical production systems, we propose a mathematical model of an extended FJSP with worker flexibility (FJSPW). A hybrid artificial bee colony algorithm (HABCA) is presented to solve the proposed FJSPW. For the HABCA, effective encoding, decoding, crossover and mutation operators are designed, and a new effective local search method is developed to improve the speed and exploitation ability of the algorithm. The Taguchi method of Design of Experiments is used to obtain the best combination of key parameters of the HABCA. Extensive computational experiments carried out to compare the HABCA with some well-performing algorithms from the literature confirm that the proposed HABCA is more effective than these algorithms, especially on large-scale FJSPW instances.

关键词： flexible job shop scheduling worker flexibility human factors single-objective optimisation artificial bee colony algorithms

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Hybrid artificial bee colony algorithm based approaches for two ring loading problems

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APPLIED INTELLIGENCE 2017年第4期47卷 1157-1168页

作者： Singh, Alok Banda, Jayalakshmi Univ Hyderabad Sch Comp & Informat Sci Hyderabad 500046 Andhra Prades India

This paper presents hybrid artificial bee colony algorithm based approaches for two N P-hard problems arising in optical ring networks. These two problems falls under the category of ring loading problems. Given a set of data transfer demands between different pair of nodes, the first problem consists in routing the demands on the ring in either clockwise or counter-clockwise directions so that the maximum data transmitted through any link in either directions is minimized. The second problem, on the other hand, discriminates between the data transmitted in one direction from the other and consists in minimizing the maximum data transmitted in one particular direction through any link. The first problem is referred to as weighted ring edge-loading problem in the literature, whereas the latter as weighted ring arc-loading problem. Computational results on the standard benchmark instances show the effectiveness of our approaches on both the problems.

关键词： artificial bee colony algorithms Communication networks Ring loading Swarm intelligence Weighted ring arc-loading problem Weighted ring edge-loading problem

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Identification of an Airfoil-Store System with Cubic Nonlinearity via Enhanced Response Sensitivity Approach

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AIAA JOURNAL 2018年第12期56卷 4977-4987页

作者： Liu, G. Wang, L. Liu, J. K. Chen, Y. M. Lu, Z. R. Sun Yat Sen Univ Sch Aeronaut & Astronaut Dept Appl Mech & Engn 135 Xingang Rd Guangzhou 510275 Guangdong Peoples R China

This paper presents an investigation on parameters identification of an airfoil-store system with cubic nonlinearity via enhanced response sensitivity approach. The nonlinear aeroelastic behavior of the nonlinear airfoil system depends on the airfoil's parameters, and thus the structural parameters of the airfoil should be identified accurately before aeroelastic analysis. The identification problem of the airfoil-store system is modeled as a nonlinear least-squares optimization problem. The enhanced response sensitivity approach has been recognized for parameter identification with time domain response data and is to be followed up in the present work. To do so, the autonomous vibration response of the nonlinear motion equation is obtained by the numerical integration, and then the response sensitivity with respect to the unknown parameters is calculated likewise. Numerical tests are performed to show the effectiveness, the convergence, the sensitivity to initial parameter values, and measurement noises of the present approach in the airfoil-store system.

关键词： Airfoil Numerical Integration Aeroelastic Analysis Aerodynamic Moments Nonlinear Systems Second Order Differential Equations Nonlinear Vibrations Stiffness Matrices artificial bee colony algorithms Ailerons

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Robust possibilistic programming for joint order batching and picker routing problem in warehouse management

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2021年第14期59卷 4434-4452页

作者： Attari, Mahdi Yousefi Nejad Torkayesh, Ali Ebadi Malmir, Behnam Jami, Ensiyeh Neyshabouri Islamic Azad Univ Dept Ind Engn Bonab Branch Bonab Iran Sabanci Univ Fac Engn & Nat Sci Istanbul Turkey Univ Virginia Dept Syst & Informat Engn Charlottesville VA USA Org Ind Mine & Trade Expert Ind East Azarbatjan Iran Virginia Tech Dept Business Informat Technol Pamplin Coll Business Blacksburg VA USA

Decisions made for designing and operating a warehouse system are of great significance. These operational decisions are strongly affected by total logistics costs, including investment and direct operating costs. The number of orders made by customers in the logistics section of warehouse management is very high because the number, type of products and items ordered by different customers vary broadly. However, machines layout for picking up products at logistics centres is minimal, inflexible, and, in some cases, inconclusive. In this study, we address joint order batching procedures of orders considering picker routing problem as a mixed-integer programming model. Extensive numerical experiments were generated in small, medium, and large sizes. In order to consider the uncertainty of parameters, we applied robust possibilistic programming for this problem. Three different meta-heuristic algorithms;genetic algorithm, particle swarm optimisation algorithm, and honey artificial bee colony algorithms are used as solution approaches to solve the formulated model. The performance of solution approaches over the problem was analysed using several test indexes. In all three group examples, there was no significant difference among mean values of the objective function, while there was a remarkable difference among computing times.

关键词： joint order batching picker routing genetic algorithm particle swarm optimisation algorithm artificial bee colony algorithms warehouse design warehousing systems

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Buckling Analysis and Configuration Optimum Design of Grid-Stiffened Composite Panels

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AIAA JOURNAL 2020年第8期58卷 3653-3664页

作者： Xu, Yuanming Chen, Hao Wang, Xi Beihang Univ Sch Aeronaut Sci & Engn Beijing 100191 Peoples R China

To allow an efficient preliminary design, a grid pattern or configuration optimization strategy is proposed for grid-stiffened composite structures under the constraint of sustaining the buckling resistant capability. This is achieved by first introducing a configuration vector for representing stiffeners in a grid unit, which is applicable for the description of iso-grid, ortho-gridX-grid, and bi-grid. The parametric equivalent stiffener model of the structure is derived by the smearing method, and the global and local buckling loads are calculated for the different grid configurations. The artificial bee colony algorithm was used as an optimizer to address the discrete design variable space, including a grid-unit size, height, and thickness of stiffeners that could be specified by designers or manufacturing requirements. Two test examples validated the effectiveness of the approach. The results suggested that both optimal and suboptimal solutions can be considered effectively as design options in the preliminary stage of design for such types of structures.

关键词： Buckling Analysis artificial bee colony algorithms Composite Structures Finite Element Analysis Rayleigh Ritz Method Composite Laminates Bending Stiffness Elastic Modulus Potential Energy Principle Material Properties

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An Improved ABC Algorithm Based on Initial Population and Neighborhood Search

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IFAC-PapersOnLine 2018年第18期51卷 251-256页

作者： Pian, Jinxiang Wang, Guohui Li, Boming Shenyang Jianzhu University Shenyang110168 China School of Foreign Languages Shenyen Jianzhu University Liaoning Province China

The traditional artificial bee colony algorithm has the disadvantages of insufficient population diversity, strong equation-searching ability but weak developing capacity, which leads to poor quality of solution, local optimum and slow global convergence. This paper increases the population diversity by unlearning initialization, improves the quality of the solution, as well as avoids the local optimum. What's more, we introduce the cross-operation and the global optimal value into the search process so that it can generate candidate solution next to the global optimal. Thus, it accelerates global convergence speed. The simulation results show that the optimization performance of different optimal function algorithm is better when the cross-factor is about 0.5. An improved ABC algorithm based on initial population and neighborhood search results show that the optimization accuracy is improved by about 2 times, which avoids the local optimum generally. Meanwhile, the number of iteration decreases about 8% to 15%, accelerating the global convergence speed. © 2018

关键词： Iterative methods Optimization artificial bee colony algorithms Cross operations Global conver gence Number of iterations Population diversity Quality of solution Search Equation Unlearning initialization

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