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Improved binary artificial bee colony algorithm Research Articles

Rafet Durgut,rafetdurgut@karabuk.edu.tr

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 8, Pages 1080-1091 doi: 10.1631/FITEE.2000239

Abstract: The (ABC) algorithm is an evolutionary optimization algorithm based on swarm intelligence and inspired by the honey bees’ food search behavior. Since the ABC algorithm has been developed to achieve optimal solutions by searching in the continuous search space, modification is required to apply it to problems. In this study, we modify the ABC algorithm to solve problems and name it the improved binary ABC (IbinABC). The proposed method consists of an update mechanism based on fitness values and the selection of different decision variables. Therefore, we aim to prevent the ABC algorithm from getting stuck in a local minimum by increasing its exploration ability. We compare the IbinABC algorithm with three variants of the ABC and other meta-heuristic algorithms in the literature. For comparison, we use the well-known OR-Library dataset containing 15 problem instances prepared for the uncapacitated facility location problem. Computational results show that the proposed algorithm is superior to the others in terms of convergence speed and robustness. The source code of the algorithm is available at https://github.com/rafetdurgut/ibinABC.

Keywords：人工蜂群；二进制优化；无容量限制的设施选址位置问题（UFLP）

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Modified Binary Artificial Bee Colony Algorithm forMultidimensional Knapsack Problem

Wang Zhigang,Xia Huiming

Strategic Study of CAE 2014, Volume 16, Issue 8, Pages 106-112

Abstract:

The binary artificial bee colony algorithm has the shortcomings of slower convergence speed and falling into local optimum easily. According to the defects, a modified binary artificial bee colony algorithm is proposed. The algorithm redesign neighborhood search formula in artificial bee colony algorithm, the probability of the food position depends on the Bayes formula. The modified algorithm was used for solving multidimensional knapsack problem, during the evolution process, it uses the greedy algorithm repairs the infeasible solution and rectify knapsack resources with insufficient use. The simulation results show the feasibility and effectiveness of the proposed algorithm.

Keywords： artificial bee colony algorithm multidimensional knapsack problem greedy algorithm combinatorial optimization

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Reversible binary subtractor design using quantumdot-cellular automata Article

Jadav Chandra DAS, Debashis DE

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 9, Pages 1416-1429 doi: 10.1631/FITEE.1600999

Abstract: In the field of nanotechnology, quantum dot-cellular automata(QCA) is the promising archetype that can provide an alternative solutionto conventional complementary metal oxide semiconductor (CMOS) circuit.QCA has high device density, high operating speed, and extremely lowpower consumption. Reversible logic has widespread applications inQCA. Researchers have explored several designs of QCA-based reversiblelogic circuits, but still not much work has been reported on QCA-basedreversible binary subtractors. The low power dissipation and highcircuit density of QCA pledge the energy-efficient design of logiccircuit at a nano-scale level. However, the necessity of too manylogic gates and detrimental garbage outputs may limit the functionalityof a QCA-based logic circuit. In this paper we describethe design and implementation of a DG gate in QCA. The universal natureof the DG gate has been established. The QCA building block of theDG gate is used to achieve new reversible binary subtractors. Theproposed reversible subtractors have low quantum cost and garbageoutputs compared to the existing reversible subtractors. The proposedcircuits are designed and simulated using QCA Designer-2.0.3.

Keywords： Quantum dot-cellular automata (QCA) Reversible logic DG gate Binary subtractor Quantum cost

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Competitive binary multi-objective grey wolf optimizer for fast compact antenna topology optimization Research Article

Jian DONG, Xia YUAN, Meng WANG

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 9, Pages 1390-1406 doi: 10.1631/FITEE.2100420

Abstract: We propose a competitive binary (CBMOGWO) to reduce the heavy computational burden of conventional multi-objective problems. This method introduces a population competition mechanism to reduce the burden of electromagnetic (EM) simulation and achieve appropriate fitness values. Furthermore, we introduce a function of cosine oscillation to improve the linear convergence factor of the original binary (BMOGWO) to achieve a good balance between exploration and exploitation. Then, the optimization performance of CBMOGWO is verified on 12 standard multi-objective test problems (MOTPs) and four multi-objective knapsack problems (MOKPs) by comparison with the original BMOGWO and the traditional binary multi-objective particle swarm optimization (BMOPSO). Finally, the effectiveness of our method in reducing the computational cost is validated by an example of a compact high-isolation dual-band multiple-input multiple-output (MIMO) antenna with high-dimensional mixed design variables and multiple objectives. The experimental results show that CBMOGWO reduces nearly half of the computational cost compared with traditional methods, which indicates that our method is highly efficient for complex problems. It provides new ideas for exploring new and unexpected antenna structures based on multi-objective evolutionary algorithms (MOEAs) in a flexible and efficient manner.

Keywords： Antenna topology optimization Multi-objective grey wolf optimizer High-dimensional mixed variables Fast design

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Pushing the Data Capacity Limit with Lasers on Silicon

Peter Weiss

Engineering 2019, Volume 5, Issue 5, Pages 824-825 doi: 10.1016/j.eng.2019.08.011

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Deep learning compact binary codes for fingerprint indexing None

Chao-chao BAI, Wei-qiang WANG, Tong ZHAO, Ru-xin WANG, Ming-qiang LI

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 9, Pages 1112-1123 doi: 10.1631/FITEE.1700420

Abstract:

With the rapid growth in fingerprint databases, it has become necessary to develop excellent fingerprint indexing to achieve efficiency and accuracy. Fingerprint indexing has been widely studied with real-valued features, but few studies focus on binary feature representation, which is more suitable to identify fingerprints efficiently in large-scale fingerprint databases. In this study, we propose a deep compact binary minutia cylinder code (DCBMCC) as an effective and discriminative feature representation for fingerprint indexing. Specifically, the minutia cylinder code (MCC), as the state-of-the-art fingerprint representation, is analyzed and its shortcomings are revealed. Accordingly, we propose a novel fingerprint indexing method based on deep neural networks to learn DCBMCC. Our novel network restricts the penultimate layer to directly output binary codes. Moreover, we incorporate independence, balance, quantization-loss-minimum, and similarity-preservation properties in this learning process. Eventually, a multi-index hashing (MIH) based fingerprint indexing scheme further speeds up the exact search in the Hamming space by building multiple hash tables on binary code substrings. Furthermore, numerous experiments on public databases show that the proposed approach is an outstanding fingerprint indexing method since it has an extremely small error rate with a very low penetration rate.

Keywords： Fingerprint indexing Minutia cylinder code Deep neural network Multi-index hashing

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Zoning of Agricultural Resource and Environment in China

Erqi Xu

Strategic Study of CAE 2018, Volume 20, Issue 5, Pages 57-62 doi: 10.15302/J-SSCAE-2018.05.009

Abstract:

Problems in China’s agricultural resources and environment have become increasingly prominent, with distinct resource and environment constraints for regional agricultures. Based on regional differentiation in agricultural resource and environment characteristics, this paper divided the country into 10 first-level zones and 57 second-level zones at the county scale. The first-level zones were divided according to regional differentiation in climate and geotectonic. And the second-level zones differentiate in water resources, land resources, and environmental conditions. It analyzed the agricultural production conditions, types of resources and their different combinations, environmental production conditions, and existing problems in these zones. This study proposed the strategies of “optimizing the spatial layout of eastern, central, and western regions” and “improving Northeast China, regulating North China, and recovering South China”. Twenty-seven second-level zones, undertaking the supply of major agricultural products in China, were identified and labeled as “major agricultural developing regions in China”. The development direction and construction measures of the major agricultural developing regions in China were suggested to maintain and improve the health and sustainability of China’s agricultural production system.

Keywords：个二级区。一级区依据气候条件和大地构造的地域分异，二级区根据水资源、土地资源和环境条件问题差异。分析了全国各分区农业生产条件、资源类型及其组合的特点、环境生产条件和存在问题，提出中国农业资源环境问题日益突出，区域农业资源限制因素和环境制约问题各不相同。依据农业资源环境地域分异，以县域为制图单元，本文将全国划分为10 提高东北，整治华北，恢复南方战略，并划分了27 个承担主要农产品供给保障功能的，指明其建设方向措施，以维护和改善我国农业生产系统的健康和可持续性

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Jaya Learning-Based Optimization for Optimal Sizing of Stand-Alone Photovoltaic, Wind Turbine, and Battery Systems Article

Asif Khan, Nadeem Javaid

Engineering 2020, Volume 6, Issue 7, Pages 812-826 doi: 10.1016/j.eng.2020.06.004

Abstract:

Renewable energy sources (RESs) are considered to be reliable and green electric power generation sources. Photovoltaics (PVs) and wind turbines (WTs) are used to provide electricity in remote areas. Optimal sizing of hybrid RESs is a vital challenge in a stand-alone environment. The meta-heuristic algorithms proposed in the past are dependent on algorithm-specific parameters for achieving an optimal solution. This paper proposes a hybrid algorithm of Jaya and a teaching–learning-based optimization (TLBO) named the JLBO algorithm for the optimal unit sizing of a PV–WT–battery hybrid system to satisfy the consumer's load at minimal total annual cost (TAC). The reliability of the system is considered by a maximum allowable loss of power supply probability (LPSP_max) concept. The results obtained from the JLBO algorithm are compared with the original Jaya, TLBO, and genetic algorithms. The JLBO results show superior performance in terms of TAC, and the PV–WT–battery hybrid system is found to be the most economical scenario. This system provides a cost-effective solution for all proposed LPSP_max values as compared with PV–battery and WT–battery systems.

Keywords：单位尺寸独立系统可再生能源储能系统优化负荷缺电率

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What Are the Best Infrastructure Investments to Make? Is It Based on Economics, or Resilience, or Both?

David Singleton AM

Engineering 2018, Volume 4, Issue 2, Pages 180-181 doi: 10.1016/j.eng.2018.04.001

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Influence and Control Strategy for Local Settlement for High-Speed Railway Infrastructure

Gaoliang Kang

Engineering 2016, Volume 2, Issue 3, Pages 374-379 doi: 10.1016/J.ENG.2016.03.014

Abstract:

This paper discusses the main impact factors of the local settlement and differential settlement of high-speed railway lines. The analysis results show that groundwater exploitation is the direct cause of differential settlement. Based on the study of ballastless track additional load and of vehicle, track, and bridge dynamic responses under different differential settlements, a control standard of differential settlement during operation is proposed preliminarily.

Keywords： Local settlement Differential settlement Additional load of ballastless track Vehicle and track dynamics

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Understanding Infrastructure Resiliency in Chennai, India Using Twitter’s Geotags and Texts: A Preliminary Study Article

Wai K. Chong, Hariharan Naganathan, Huan Liu, Samuel Ariaratnam, Joonhoon Kim

Engineering 2018, Volume 4, Issue 2, Pages 218-223 doi: 10.1016/j.eng.2018.03.010

Abstract:

Geotagging is the process of labeling data and information with geographical identification metadata, and text mining refers to the process of deriving information from text through data analytics. Geotagging and text mining are used to mine rich sources of social media data, such as video, website, text, and Quick Response (QR) code. They have been frequently used to model consumer behaviors and market trends. This study uses both techniques to understand the resilience of infrastructure in Chennai, India using data mined from the 2015 flood. This paper presents a conceptual study on the potential use of social media (Twitter in this case) to better understand infrastructure resiliency. Using featureextraction techniques, the research team extracted Twitter data from tweets generated by the Chennai population during the flood. First, this study shows that these techniques are useful in identifying locations, defects, and failure intensities of infrastructure using the location metadata from geotags, words containing the locations, and the frequencies of tweets from each location. However, more efforts are needed to better utilize the texts generated from the tweets, including a better understanding of the cultural contexts of the words used in the tweets, the contexts of the words used to describe the incidents, and the least frequently used words.

Keywords： Social media Flooding Engineering design

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A surrogate-based optimization algorithm for network design problems Article

Meng LI, Xi LIN, Xi-qun CHEN

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 11, Pages 1693-1704 doi: 10.1631/FITEE.1601403

Abstract: Network design problems (NDPs) have long been regarded as one of the most challenging problems in the field of transportation planning due to the intrinsic non-convexity of their bi-level programming form. Furthermore, a mixture of continuous/discrete decision variables makes the mixed network design problem (MNDP) more complicated and difficult to solve. We adopt a surrogate-based optimization (SBO) framework to solve three featured categories of NDPs (continuous, discrete, and mixed-integer). We prove that the method is asymptotically completely convergent when solving continuous NDPs, guaranteeing a global optimum with probability one through an indefinitely long run. To demonstrate the practical performance of the proposed framework, numerical examples are provided to compare SBO with some existing solving algorithms and other heuristics in the literature for NDP. The results show that SBO is one of the best algorithms in terms of both accuracy and efficiency, and it is efficient for solving large-scale problems with more than 20 decision variables. The SBO approach presented in this paper is a general algorithm of solving other optimization problems in the transportation field.

Keywords： Network design problem Surrogate-based optimization Transportation planning Heuristics

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Solving Knapsack Problem by Hybrid Particle Swarm Optimization Algorithm

Gao Shang,Yang Jingyu

Strategic Study of CAE 2006, Volume 8, Issue 11, Pages 94-98

Abstract:

The classical particle swarm optimization is a powerful method to find the minimum of a numerical function, on a continuous definition domain. The particle swarm optimization algorithm combining with the idea of the genetic algorithm is recommended to solve knapsack problem. All the 6 hybrid particle swarm optimization algorithms are proved effective. Especially the hybrid particle swarm optimization algorithm derived from across strategy A and mutation strategy C is a simple yet effective algorithm and it has been applied successfully to investment problem. It can easily be modified for any combinatorial problem for which there has been no good specialized algorithm.

Keywords： particle swarm algorithm knapsack problem genetic algorithm mutation

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An improved fruit fly optimization algorithm for solving traveling salesman problem Article

Lan HUANG, Gui-chao WANG, Tian BAI, Zhe WANG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 10, Pages 1525-1533 doi: 10.1631/FITEE.1601364

Abstract: The traveling salesman problem (TSP), a typical non-deterministic polynomial (NP) hard problem, has been used in many engineering applications. As a new swarm-intelligence optimization algorithm, the fruit fly optimization algorithm (FOA) is used to solve TSP, since it has the advantages of being easy to understand and having a simple implementation. However, it has problems, including a slow convergence rate for the algorithm, easily falling into the local optimum, and an insufficient optimi-zation precision. To address TSP effectively, three improvements are proposed in this paper to improve FOA. First, the vision search process is reinforced in the foraging behavior of fruit flies to improve the convergence rate of FOA. Second, an elimination mechanism is added to FOA to increase the diversity. Third, a reverse operator and a multiplication operator are proposed. They are performed on the solution sequence in the fruit fly’s smell search and vision search processes, respectively. In the experiment, 10 benchmarks selected from TSPLIB are tested. The results show that the improved FOA outperforms other alternatives in terms of the convergence rate and precision.

Keywords： Traveling salesman problem Fruit fly optimization algorithm Elimination mechanism Vision search Operator

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应用完备集合固有时间尺度分解和混合差分进化和粒子群算法优化的最小二乘支持向量机对柴油机进行故障诊断 Article

俊红张,昱刘

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 2, Pages 272-286 doi: 10.1631/FITEE.1500337

Abstract: 针对固有时间尺度分解算法的模态混叠问题和最小二乘支持向量机的参数优化问题，本文提出了一种新的基于完备集合固有时间尺度分解和混合差分进化和粒子群算法优化最小二乘支持向量机的柴油机故障诊断方法。该方法主要包括以下几个步骤：首先，为解决固有时间尺度分解算法的模态混叠问题，提出了一种完备集合固有时间尺度分解算法。随后，利用完备集合固有时间尺度分解算法将非平稳的柴油机振动信号分解为一系列平稳的旋转分量和残差信号。最后，提出了混合差分进化和粒子群算法对最小二乘支持向量机的参数进行优化的方法，并通过将故障特征输入训练好的最小二乘支持向量机模型实现故障诊断。仿真和实验结果表明提出的故障诊断方法可以克服固有时间尺度分解的模态混叠问题，而且能够准确的识别柴油机故障。

Keywords：柴油机；故障诊断；完备集合固有时间尺度分解；最小二乘支持向量机；混合差分进化和粒子群优化算法

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Title Author Date Type Operation

Improved binary artificial bee colony algorithm

Rafet Durgut,rafetdurgut@karabuk.edu.tr

Journal Article

Modified Binary Artificial Bee Colony Algorithm forMultidimensional Knapsack Problem

Wang Zhigang,Xia Huiming

Journal Article

Reversible binary subtractor design using quantumdot-cellular automata

Jadav Chandra DAS, Debashis DE

Journal Article