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An effective discrete monarch butterfly optimization algorithm for distributed blocking flow shop scheduling with an assembly machine

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期225卷

作者： Du, Songlin Zhou, Wenju Wu, Dakui Fei, Minrui Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China

Distributed scheduling with assembly machines has been an attractive research field in sustainable supply chains and multi-factory manufacturing systems. This paper investigates a distributed blocking flow shop scheduling problem with an assembly machine (DABFSP) with the total assembly completion time criterion, and proposes an effective discrete monarch butterfly optimization algorithm (EDMBO). First, a constructive heuristic combining the largest processing time rule and the earliest start assembly rule is provided to find a promising sequence. On this basis, an efficient initialization method is introduced to generate an initial population with high quality and diversity. Afterward, a global search procedure is presented, which integrates four kinds of improved operators and expands the solution space in a good direction. Then, according to different problem-specific characteristics, we present four targeted and flexible variable neighborhood search methods based on the critical job and critical factory to exploit the solution space. Finally, statistically significant numerical experiments are carried out with state-of-the-art optimization methods based on 1710 benchmark instances. The experimental results and detailed analysis demonstrate that the EDMBO is superior to preferred algorithms for addressing the DABFSP.

关键词： Distributed assembly blocking flow shop scheduling problem Total assembly completion time monarch butterfly optimization algorithm Constructive heuristics Local search

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Modified added activation function based exponential robust random vector functional link network with expanded version for nonlinear system identification

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APPLIED INTELLIGENCE 2022年第5期52卷 5657-5683页

作者： Samal, Debashisa Dash, Pradipta Kishore Bisoi, Ranjeeta Siksha O Anusandhan Deemed Univ Dept Elect & Commn Engn Bhubaneswar 751030 Odisha India Siksha O Anusandhan Deemed Univ Multidisciplinary Res Cell Bhubaneswar 751030 Odisha India

This paper presents an improved non-iterative random vector functional link network hybrid model with better input-output representation, improved generalization for nonlinear dynamic system identification. The modified random vector functional link network model uses random weights between the input layer and hidden or enhancement layer neurons whose outputs are obtained by using two suitably weighted activation functions and additionally it provides a weighted direct link between the trigonometric functions based exponentially expanded the inputs and the output node. This novel architecture provides a direct link of inputs and its nonlinear expanded version in a higher dimensional space to the output node along with a randomized version of the hidden layer operating with an optimized added activation function to handle the chaotic nature of the non-linear dynamic systems. Also the weights and the parameters associated with the summed activation functions are optimized using an efficient modified sine cosine algorithm based monarch butterfly algorithm with levy distribution optimization algorithm with better exploitation and exploration capabilities in order to improve overall identification accuracy. To authenticate the efficiency of the proposed model, five benchmark dynamic plants are examined;the achieved outputs are compared with recognized methods like extreme learning machine, conventional random vector functional link network, and enhanced random vector functional link network with single activation function and least mean square. The method proposed here exhibits improved performance accuracy which is superior to the considered models. The proposed model is also compared with some iterative existing methods and found suitable by taking into consideration the merits of non-iterative approach,

关键词： Nonlinear system identification Random vector functional link network Added activation function based enhanced random vector functional link network Trigonometric exponential input enhancement monarch butterfly optimization algorithm

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Maximum likelihood DOA estimation method based on matrix completion in non-uniform noise environment 14

Maximum likelihood DOA estimation method based on matrix com...

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14th International Symposium on Antennas Propagation and EM Theory

作者： Zhao Xiaomei Wu Jiajing Peng Huaiyun Chinese Res Inst Radiowave Propagat Natl key Lab Electromagnet Environm Qingdao Peoples R China

ISBN: (纸本)9798350353129;9798350353136

For the estimation of the direction of reach in non-uniform noise environment, the method is proposed by applying Matrix Completion theory (Matrix Completion, MC) combined with the Improved monarch butterfly optimization algorithm based ML (ML-IMBO) method, In this paper, a matrix completion based ML-IMBO (MML-IMBO) estimation method for maximum likelihood DOA under heterogeneous noise environment is proposed. Experimental simulation results show that the proposed MML-IMBO algorithm can effectively suppress the influence of non-uniform noise and has good performance in DOA estimation.

关键词： Direction of Arrival estimation Non-uniform noise Matrix completion Maximum likelihood algorithm monarch butterfly optimization algorithm

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Stochastic Economic Dispatch of Hydro-Thermal-Wind-Photovoltaic Power System Considering Mixed Coal-Blending Combustion

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IEEE ACCESS 2020年 8卷 218542-218553页

作者： Huang, Qiuli Han, Song Rong, Na Luo, Jingxin Hu, Xiao Guizhou Univ Dept Elect Engn Guiyang 550025 Peoples R China

This paper proposes a stochastic economic dispatch (SED) model for hydro-thermal-wind-photovoltaic power system (HTWPPS) considering mixed coal-blending combustion (MCBC) to improve the operational efficiencies of thermal power units (TPUs) with deep peak-regulating operation (DPRO). Firstly, the rations for different types of coal can be acquired by meeting the operational requirements of boilers. Three functions including the cost of coal consumption, polluting gas emission, and the life loss cost for TPUs could be obtained by fitting coal quality. Besides, they would be strictly piecewise linearized, respectively. Consequently, the objective function involving the above three kinds of costs as well as the cost of oil injection can be presented. Secondly, there is a two-stage framework for reducing the coupling of HTWPPS and improving the solving efficiency. In the first stage, the monarch butterfly optimization (MBO) algorithm is employed for obtaining the power outputs of hydro-power units (HPUs) taking the minimum water consumption as the objective. In the second stage, the load curve can be updated according to the results from the first stage. Besides, the Latin Hypercube Sampling method and the backward scene reduction technique are utilized to acquire a set of scenarios of wind power. In this way, the SED model considering MCBC can be achieved. The case studies have been carried out on a simplified provincial power system in China with the help of MATPOWER and CPLEX. The results show that the proposed model can effectively increase the economic and environmental benefits for TPUs.

关键词： Coal Power generation optimization Economics Combustion Stochastic processes Oils Stochastic economic dispatch hydro-thermal-wind-photovoltaic power system cascading hydro-power plants deep peak-regulating operation two-stage framework mixed coal-blending combustion monarch butterfly optimization algorithm

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Hybridizing artificial bee colony with monarch butterfly optimization for numerical optimization problems

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NEURAL COMPUTING & APPLICATIONS 2018年第1期30卷 163-181页

作者： Ghanem, Waheed A. H. M. Jantan, Aman Univ Sains Malaysia Sch Comp Sci George Town Malaysia Univ Aden Fac Engn Aden Yemen Univ Aden Fac Educ Saber Aden Yemen

The aim of the study was to propose a new metaheuristic algorithm that combines parts of the well-known artificial bee colony (ABC) optimization with elements from the recent monarch butterfly optimization (MBO) algorithm. The idea is to improve the balance between the characteristics of exploration and exploitation in those algorithms in order to address the issues of trapping in local optimal solution, slow convergence, and low accuracy in numerical optimization problems. This article introduces a new hybrid approach by modifying the butterfly adjusting operator in MBO algorithm and uses that as a mutation operator to replace employee phase of the ABC algorithm. The new algorithm is called Hybrid ABC/MBO (HAM). The HAM algorithm is basically employed to boost the exploration versus exploitation balance of the original algorithms, by increasing the diversity of the ABC search process using a modified operator from MBO algorithm. The resultant design contains three components: The first and third component implements global search, while the second one performs local search. The proposed algorithm was evaluated using 13 benchmark functions and compared with the performance of nine metaheuristic methods from swarm intelligence and evolutionary computing: ABC, MBO, ACO, PSO, GA, DE, ES, PBIL, and STUDGA. The experimental results show that the HAM algorithm is clearly superior to the standard ABC and MBO algorithms, as well as to other well-known algorithms, in terms of achieving the best optimal value and convergence speed. The proposed HAM algorithm is a promising metaheuristic technique to be added to the repertory of optimization techniques at the disposal of researchers. The next step is to look into application fields for HAM.

关键词： Artificial bee colony algorithm monarch butterfly optimization algorithm Global optimization problem Computation intelligence

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A Novel Hybrid Artificial Bee Colony with monarch butterfly optimization for Global optimization Problems 1

A Novel Hybrid Artificial Bee Colony with Monarch Butterfly ...

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1st International Conference on Computer Science and Engineering (COMPSE)

作者： Ghanem, Waheed Ali H. M. Jantan, Aman Univ Sains Malaysia Sch Comp Sci Gelugor Penang Malaysia Univ Aden Fac Educ Saber Aden Yemen Univ Aden Fac Engn Aden Yemen Univ Sains Malaysia Sch Comp Sci Gelugor Penang Malaysia

ISBN: (纸本)9783319705422;9783319705415

This article introduces a novel hybrid approach between two of the meta-heuristic algorithms to solve global optimization problems. The proposed hybrid algorithm uses the butterfly adjusting operator in monarch butterfly Op-timization (MBO) algorithm as a mutation operator to replace the employee phase of the Artificial Bee Colony (ABC) algorithm. The novel Hybrid ABC/MBO (HAM) algorithm addresses the issues of trapping in local optimal solutions, slow convergence, and low precision by improving the balance be-tween the characteristics of exploration and exploitation. The proposed HAM al-gorithm is validated on eight benchmark functions, and is compared with ABC and MBO algorithms. The experimental results show that the HAM algorithm is clearly superior to both the standard ABC and MBO algorithms. © Springer-Verlag Berlin Heidelberg 2017.

关键词： Artificial bee colony algorithm Computation intelligence Global optimization problem monarch butterfly optimization algorithm

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