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A hybrid evolutionary algorithm based on tissue membrane systems and CMA-ES for solving numerical optimization problems

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KNOWLEDGE-BASED SYSTEMS 2016年 105卷 38-47页

作者： Liu, Chuang Fan, Linan Shenyang Univ Sch Informat Engn Liaoning Peoples R China

In this paper, a new hybrid algorithm is proposed to solve the single objective real-parameter numerical optimization problems, named as CETMS. The proposed CETMS is based on tissue membrane systems(TMS), and the evolution strategy with covariance matrix adaptation (CMA-ES) algorithm is employed to find the optimal solution in each cell of TMS. Some features of Tissue Membrane Systems, such as membrane structure, evolution mechanism and communication mechanism among cells, are introduced into CETMS. hi addition, the optimal information of different cells can be shared by communication mechanism of TMS after the appointed cycle. The simulation experiments are conducted on thirty benchmark functions on the CEC14 test suite, which evaluate the performance of the proposed algorithm on solving single objective real-parameter numerical optimization problems. numerical results show that the proposed CETMS has a very good performance in comparison with some of the state-of-the-art algorithms. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Tissue membrane system CMA-ES numerical optimization problems Membrane computing optimization

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An ameliorated particle swarm optimizer for solving numerical optimization problems

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APPLIED SOFT COMPUTING 2018年 73卷 482-496页

作者： Chen, Ke Zhou, Fengyu Wang, Yugang Yin, Lei Shandong Univ Sch Control Sci & Engn Jinan 250061 Shandong Peoples R China

Although the particle swarm optimizer (PSO) has been widely used to address various complicated engineering problems, it is likely to suffer lack of diversity and ineffectiveness of balance between the global search ability and the local search ability in the search process. In this paper, we report an innovative and improved optimization method called ameliorated particle swarm optimizer (A-PSO), which is different from the original PSO algorithm and its variants in parameter update and the position generation of each particle. In A-PSO, the nonlinear dynamic acceleration coefficients, logistic map and a modified particle position update approach are introduced in PSO to improve the solution quality and accelerate the global convergence rate. Twenty well-known numerical optimization functions are adopted to evaluate the effectiveness of the proposed method and it is illustrated that, for most numerical optimization problems, the convergence performance and search accuracy of the A-PSO method are superior to the similar heuristic optimization algorithms and other well-known PSO variants. Namely, the proposed A-PSO technique has a faster convergence rate and is more stable than other PSO variants and similar population-based methods for almost all numerical optimization problems. Therefore, the A-PSO method is successfully used as a new optimization technique for solving numerical optimization problems. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Particle swarm optimizer Nonlinear dynamic acceleration coefficients Logistic map sequence numerical optimization problems optimization

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A Novel Hybrid Vortex Search and Artificial Bee Colony Algorithm for numerical optimization problems

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Wuhan University Journal of Natural Sciences 2017年第4期22卷 295-306页

作者： WANG Zhaowei WU Guomin WAN Zhongping School of Mathematics and Statistics Wuhan University Department of Mathematics and Physics Beijing Institute of Petrochemical Technology

Though vortex search（VS） algorithm has good performance in solving global numerical optimization problems, it cannot fully search the whole space occasionally. Combining the vortex search algorithm and the artificial bee colony algorithm（ABC） which has good performance in exploration, we present a HVS（hybrid vortex search） algorithm to solve the numerical optimization problems. We first use the employed bees and onlooker bees of ABC algorithm to find a solution, and then adopt the VS algorithm to find the best solution. In the meantime, we cannot treat the best solution so far as the center of the algorithm all the time. The algorithm is tested by 50 benchmark functions. The numerical results show the HVS algorithm has superior performance over the ABC and the VS algorithms.

关键词： numerical optimization problems vortex searchalgorithm artificial bee colony algorithm hybrid algorithm

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Evaluation of Performance of Adaptive and Hybrid ABC (aABC) Algorithm in Solution of numerical optimization problems

Evaluation of Performance of Adaptive and Hybrid ABC (aABC) ...

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Innovations in Intelligent Systems and Applications Conference (ASYU)

作者： Karaboga, Dervis Kaya, Ebubekir Erciyes Univ Dept Comp Engn Kayseri Turkey Nevsehir Haci Bektas Veli Univ Dept Comp Technol Nevsehir Turkey

ISBN: (纸本)9781538677865

Artificial bee colony (ABC) algorithm is a heuristic optimization algorithm that models food search behavior of the honey bees. It is used to solve many real-world problems and has been successful. In the literature, it is seen that different modifications of ABC algorithm are proposed to obtain more effective results. In this study, adaptive and hybrid ABC (aABC) algorithm which is one of the modifications of ABC algorithm is used. Its performance is evaluated in solving numerical test functions. Unlike standard ABC algorithm, aABC algorithm uses arithmetic crossover and adaptive neighborhood radius in the solution generation mechanism. The applications are performed on 6 numerical test functions. The results are evaluated in terms of solution quality and convergence speed. In addition, Wilcoxon signed-rank test is used to examine the significance of the results. The results show that aABC algorithm is more effective than ABC algorithm in solving numerical optimization problems.

关键词： artificial bee colony aABC algorithm numerical optimization problems optimization

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Best Guided Backtracking Search Algorithm for numerical optimization problems 9th

Best Guided Backtracking Search Algorithm for Numerical Opti...

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9th International Conference on Knowledge Science, Engineering, and Management (KSEM)

作者： Zhao, Wenting Wang, Lijin Wang, Bingqing Yin, Yilong Shandong Univ Sch Comp Sci & Technol Jinan 250101 Peoples R China Fujian Agr & Forestry Univ Coll Comp & Informat Sci Fuzhou 350002 Peoples R China Shandong Univ Finance & Econ Sch Comp Sci & Technol Jinan 250014 Peoples R China

ISBN: (纸本)9783319476506;9783319476490

Backtracking search algorithm is a promising stochastic search technique by using its historical information to guide the population evolution. Using historical population information improves the exploration capability, but slows the convergence, especially on the later stage of iteration. In this paper, a best guided backtracking search algorithm, termed as BGBSA, is proposed to enhance the convergence performance. BGBSA employs the historical information on the beginning stage of iteration, while using the best individual obtained so far on the later stage of iteration. Experiments are carried on the 28 benchmark functions to test BGBSA, and the results show the improvement in efficiency and effectiveness of BGBSA.

关键词： Backtracking search algorithm Best guided Historical information numerical optimization problems

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A Novel Artificial Bee Colony Algorithm with Integration of Extremal optimization for numerical optimization problems

A Novel Artificial Bee Colony Algorithm with Integration of ...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Chen, Min-Rong Zeng, Wei Zeng, Guo-Qiang Li, Xia Luo, Jian-Ping S China Normal Univ Sch Comp Sci Guangzhou Guangdong Peoples R China Shenzhen Univ Coll Informat Engn Shenzhen Peoples R China Wenzhou Univ Dept Elect & Elect Engn Wenzhou Peoples R China

ISBN: (纸本)9781479914883

Artificial Bee Colony (ABC) algorithm is an optimization algorithm based on a particular intelligent behaviour of honeybee swarms. The standard ABC is weak at the local-search capability and precision. Extremal optimization (EO) is a general-purpose heuristic method which has strong local-search capability and has been successfully applied to a wide variety of hard optimization problems. In order to strengthen the local-search capability of ABC, this work proposes a novel hybrid optimization method, called ABC-EO algorithm, through introducing EO to ABC. The simulation results show that the performance of the proposed method is as good as or superior to those of the state-of-the-art algorithms in complex numerical optimization problems.

关键词： Artificial Bee Colony Extremal optimization numerical optimization problems

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A vector-encirclement-model-based sparrow search algorithm for engineering optimization and numerical optimization problems

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APPLIED SOFT COMPUTING 2022年 131卷

作者： Hong, Jiale Shen, Bo Xue, Jiankai Pan, Anqi Donghua Univ Coll Informat Sci & Technol Shanghai Peoples R China Minist Educ Engn Res Ctr Digitalized Text & Fash Technol Shanghai 201620 Peoples R China

Sparrow search algorithm (SSA) is a new global optimization tool with high performance. In order to further improve its global and local search abilities, in this paper, an improved SSA (ISSA) is put forward where the main novelties lie in the producer centralization strategy, the vector encirclement model and the direction selection strategy. The producer centralization strategy is designed to update the position of producer with the hope of improving the global search ability of the producer, while the vector encirclement model and the direction selection strategy are proposed to update the position of scrounger for the purpose of improving the local search ability of scrounger. For the comparison purpose, the performances of the proposed ISSA and the existing excellent algorithms are tested in CEC2017 benchmark functions and 30 real-world constrained optimization problems. The experimental results demonstrate that the proposed ISSA substantially improves the convergence rate, optimization accuracy, stability of the SSA and can solve a wide range of real-world constrained optimization problems successfully with satisfactory performances. Finally, the proposed ISSA is successfully applied in the trajectory optimization of mechanical arms.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Engineering optimization problems numerical optimization problems Sparrow search algorithm Trajectory optimization Vector encirclement model

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A covariance-based Moth-flame optimization algorithm with Cauchy mutation for solving numerical optimization problems

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APPLIED SOFT COMPUTING 2022年第0期119卷 108538-108538页

作者： Zhao, Xiaodong Fang, Yiming Liu, Le Xu, Miao Li, Qiang Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao 066004 Peoples R China Yanshan Univ Engn Res Ctr Minist Educ Intelligent Control Syst & Intelligent Qinhuangdao 066004 Peoples R China Hebei Normal Univ Coll Engn Shijiazhuang 050024 Peoples R China

Moth-Flame optimization (MFO) algorithm, which is inspired by the navigation method of moths, is a nature-inspired optimization algorithm. The MFO is easy to implement and has been used to solve many real-world optimization problems. However, the MFO cannot balance exploration and exploitation well, and the information exchange between individuals is limited, especially in solving some complex numerical problems. To overcome these disadvantages of the MFO in solving the numerical optimization problems, a covariance-based Moth-Flame optimization algorithm with Cauchy mutation (CCMFO) is proposed in this paper. In the CCMFO, the concept of covariance is used to transform the individuals of the moths and flames from the original space to the eigenspace and update the positions of moths, which can better improve the information exchange ability of the flames and moths in the eigenspace. In addition, Cauchy mutation is utilized to improve the exploration. And the CCMFO is compared with the other 22 algorithms on CEC 2020 test suite. The test results show that the CCMFO is better than other population-based optimization algorithms and MFO variants in search performance, while its performance is statistically similar to CEC competition algorithms. Furthermore, the CCMFO is compared with the other 12 algorithms on CEC 2020 real -world constrained optimization problems, and the results show that the CCMFO can effectively solve real-world constrained optimization problems. Finally, the CCMFO is used to optimize the tracking controller parameters of continuous casting mold vibration displacement. The experimental results based on the experimental platform show that the CCMFO can effectively reduce the difficulty of parameter selection and improve the tracking accuracy. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Moth-flame optimization algorithm Covariance matrix Cauchy mutation numerical optimization problems Engineering optimization problems

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Improved immune algorithm for global numerical optimization and job-shop scheduling problems

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APPLIED MATHEMATICS AND COMPUTATION 2007年第2期194卷 406-424页

作者： Tsai, Jinn-Tsong Ho, Wen-Hsien Liu, Tung-Kuan Chou, Jyh-Horng Natl Pingteng Univ Educ Dept Comp Sci Pingtung Taiwan Kaohsiung Med Univ Dept Med Informat Management Kaohsiung 807 Taiwan Natl Kaohsiung First Univ Sci & Technol Inst Syst Informat & Control Kaohsiung 824 Taiwan

In this paper, by using the unified procedures, an improved immune algorithm named a modified Taguchi-immune algorithm (MTIA), based on both the features of an artificial immune system and the systematic reasoning ability of the Taguchi method, is proposed to solve both the global numerical optimization problems with continuous variables and the combinatorial optimization problems for the job-shop scheduling problems (JSP). The MTIA combines the artificial immune algorithm, which has a powerful global exploration capability, with the Taguchi method, which can exploit the optimal antibody. In the MTIA, the clonal proliferation within hypermutation for several antibody diversifications and the recombination by using the Taguchi method for the local search are integrated to improve the capabilities of exploration and exploitation. The systematic reasoning ability of the Taguchi method is executed in the recombination operations to select the better antibody genes to achieve the potential recombination, and consequently enhance the MTIA. The proposed MTIA is effectively applied to solve 15 benchmark problems of global optimization with 30 or 100 dimensions. The computational experiments show that the proposed MTIA can not only find optimal or close-to-optimal solutions but can also obtain both better and more robust results than the existing improved genetic algorithms reported recently in the literature. In addition, the MTIA is also applied to solve the famous Fisher-Thompson and Lawrence benchmarks of the JSP. The computational experiments show that the proposed MTIA approach can also obtain both better and more robust results than those evolutionary methods reported recently. (c) 2007 Elsevier Inc. All rights reserved.

关键词： immune algorithm numerical optimization problems job-shop scheduling problems Taguchi method

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A novel hybrid combination optimization algorithm based on search area segmentation and fast Fourier transform

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ENGINEERING optimization 2019年第5期51卷 846-861页

作者： Zhao, Fuqing Yang, Guoqiang Zhang, Yi Lei, Wenchang Ma, Weimin Zhang, Chuck Lanzhou Univ Technol Sch Comp & Commun Technol Lanzhou Gansu Peoples R China Xijin Univ Sch Mech Engn Xian Shaanxi Peoples R China Tongji Univ Sch Econ & Management Shanghai Peoples R China Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA

A novel hybrid optimization algorithm combining search area segmentation technique and the fast Fourier transform (HSAS/FFT) is presented to solve the numerical optimization problems. Firstly, the spectrum of each dimension of the objective function can be acquired by the FFT. The search space is segmented by using the spectrum to ensure that each subspace is unimodal. Secondly, the population of subspaces is produced and the optimal individual can be obtained by gradient descent algorithm. Finally, the local optimal solution in the optimal subspace is generated by the binary search algorithm. Make the optimal individual the new search space and repeat the process until meeting the termination condition. The proposed HSAS/FFT was tested on the CEC2017 benchmark, which evaluates the performance of the proposed algorithm on solving global optimization problems. Results obtained show that HSAS/FFT has an excellent performance and better convergence speed in comparison with some of the state-of-the-art algorithms.

关键词： Fast Fourier transform search area segmentation gradient descent binary search numerical optimization problems

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