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biogeography-based optimization algorithm for large-scale multistage batch plant scheduling

EXPERT SYSTEMS WITH APPLICATIONS 2020年 162卷

作者： Zhang, Yaya Gu, Xingsheng East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China

The batch process is characterized by many varieties, small batches, redundant production equipment, flexible production process, and high-added-value products. This process is widely used in chemical, plastic, rubber, pharmaceutical, fine chemical, metallurgical, steel, food, and other industries. The optimized scheduling scheme of the batch process can effectively enhance enterprise competitiveness and improve economic benefits. Multistage Multiproduct Scheduling Problem (MMSP) is an important branch of batch scheduling problems. It is difficult to solve MMSP within a reasonable time by traditional mathematical programming, because once the scale of scheduling problems increases, the solution space expands exponentially. This study proposes a metaheuristic approach based on a time key biogeography-based optimization algorithm to solve MMSP. This new time key representation contains two vectors, which represent the processing sequence and equipment allocation of orders respectively. In accordance with the time information in the new representation, we add the preference of equipment processing time to migration and calculate the probability of every mutation value. In addition, the elite solution is combined with the active scheduling technique and modified Nawaz-Enscore-Ham (NEH) algorithm to improve the search accuracy of the proposed algorithm. To test the performance of Improved Time Key biogeography-based optimization (Improved-TKBBO) algorithm, its results are compared with computational results of mathematical programming, Genetic algorithm (GA), and Line-up Competition algorithm (LCA). Simulation results show that the proposed Improved-TKBBO can solve the large-scale MMSP with non-identical parallel units effectively. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Multistage multiproduct Batch plant biogeography-based optimization algorithm Heuristic rules

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biogeography-based optimization algorithm for Solving the Set Covering Problem 5th

Biogeography-Based Optimization Algorithm for Solving the Se...

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5th Computer Science On-line Conference (CSOC)

作者： Crawford, Broderick Soto, Ricardo Riquelme, Luis Olguin, Eduardo Pontificia Univ Catolica Valparaiso Valparaiso Chile Univ San Sebastian Santiago Santiago Metrop Chile Univ Cent Chile Santiago Santiago Metrop Chile Univ Autunoma Chile Temuco Chile Univ Cient Sur Lima Peru

ISBN: (纸本)9783319336251;9783319336237

biogeography-based optimization algorithm (BBOA) is a kind of new global optimization algorithm inspired by biogeography. It mimics the migration behavior of animals in nature to solve optimization and engineering problems. In this paper, BBOA for the Set Covering Problem (SCP) is proposed. SCP is a classic combinatorial problem from NP-hard list problems. It consist to find a set of solutions that cover a range of needs at the lowest possible cost following certain constraints. In addition, we provide a new feature for improve performance of BBOA, improving stagnation in local optimum. With this, the experiment results show that BBOA is very good at solving such problems.

关键词： biogeography-based optimization algorithm Set Covering Problem

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An Effective biogeography-based optimization algorithm for Flow Shop Scheduling with Intermediate Buffers

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Chinese Journal of Electronics 2018年第6期27卷 1141-1150页

作者： LIU Shufen WANG Pengfei YAO Zhilin College of Computer Science and Technology Jilin University

This paper proposes an Effective biogeography-based optimization(EBBO) algorithm for solving the flow shop scheduling problem with intermediate buffers to minimize the Total flow time(TFT). Discrete job permutations are used to represent individuals in the EBBO so the discrete problem can be solved directly. The NEH heuristic and NEH-WPT heuristic are used for population initialization to guarantee the diversity of the solution. Migration and mutation rates are improved to accelerate the search process. An improved migration operation using a two-points method and mutation operation using inverse rules are developed to prevent illegal solutions. A new local search algorithm is proposed for embedding into the EBBO algorithm to enhance local search *** simulations and comparisons demonstrated the superiority of the proposed EBBO algorithm in solving the flow shop scheduling problem with intermediate buffers with the TFT criterion.

关键词： Flow shop scheduling Intermediate buffer Total flow time(TFT) biogeography-based optimization algorithm Local search algorithm

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Improved biogeography-based optimization algorithm for lean production scheduling of prefabricated components

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ENGINEERING CONSTRUCTION AND ARCHITECTURAL MANAGEMENT 2023年第4期30卷 1601-1635页

作者： Du, Juan Xue, Yan Sugumaran, Vijayan Hu, Min Dong, Peng Shanghai Univ SILC Business Sch Shanghai Peoples R China Univ Technol Sydney Fac Engn & IT Sydney NSW Australia Shanghai Engn Res Ctr Urban Infrastruct Renewal Shanghai Peoples R China Oakland Univ Rochester MI 48063 USA

Purpose For prefabricated building construction, improper handling of the production scheduling for prefabricated components is one of the main reasons that affect project performance, which causes overspending, schedule overdue and quality issues. Prior research on prefabricated components production schedule has shown that optimizing the flow shop scheduling problem (FSSP) is the basis for solving this issue. However, some key resources and the behavior of the participants in the context of actual prefabricated components production are not considered comprehensively. Design/methodology/approach This paper characterizes the production scheduling of the prefabricated components problem into a permutation flow shop scheduling problem (PFSSP) with multi-optimization objectives, and limitation on mold and buffers size. The lean construction principles of value-based management (VBM) and just-in-time (JIT) are incorporated into the production process of precast components. Furthermore, this paper applies biogeography-based optimization (BBO) to the production scheduling problem of prefabricated components combined with some improvement measures. Findings This paper focuses on two specific scenarios: production planning and production rescheduling. In the production planning stage, based on the production factor, this study establishes a multi-constrained and multi-objective prefabricated component production scheduling mathematical model and uses the improved BBO for prefabricated component production scheduling. In the production rescheduling stage, the proposed model allows real-time production plan adjustments based on uncertain events. An actual case has been used to verify the effectiveness of the proposed model and the improved BBO. Research limitations/implications With respect to limitations, only linear weighted transformations are used for objective optimization. In regards to research implications, this paper considers the production of prefabricated compone

关键词： Prefabricated component Production scheduling biogeography-based optimization algorithm Lean construction

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Novel biogeography-based optimization algorithm with hybrid migration and global-best Gaussian mutation

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APPLIED MATHEMATICAL MODELLING 2020年 86卷 74-91页

作者： Zhang, Xinming Wang, Doudou Fu, Zihao Liu, Shangwang Mao, Wentao Liu, Guoqi Jiang, Yun Li, Shuangqian Henan Normal Univ Coll Comp & Informat Engn Xinxiang 453007 Henan Peoples R China Engn Lab Intelligence Business & Internet Things Xinxiang 453007 Henan Peoples R China

The biogeography-based optimization algorithm and its variants have been used widely for optimization problems. To get better performance, a novel biogeography-based optimization algorithm with Hybrid migration and global-best Gaussian mutation is proposed in this paper. Firstly, a linearly dynamic random heuristic crossover strategy and an exponentially dynamic random differential mutation one are presented to form a hybrid migration operator, and the former is used to get stronger local search ability and the latter strengthen the global search ability. Secondly, a new global-best Gaussian mutation operator is put forward to balance exploration and exploitation better. Finally, a random opposition learning strategy is merged to avoid getting stuck in local optima. The experiments on the classical benchmark functions and the complexity functions from CEC-2013 and CEC-2017 test sets, and the Wilcoxon, Bonferroni-Holm and Friedman statistical tests are used to evaluate our algorithm. The results show that our algorithm obtains better performance and faster running speed compared with quite a few state-of-the-art competitive algorithms. In addition, experimental results on Minimum Spanning Tree and K-means clustering optimization show that our algorithm can cope with these two problems better than the comparison algorithms. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Evolutionary algorithm biogeography-based optimization algorithm Migration Gaussian mutation Minimum spanning tree K-menas clustering

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Control and synchronization of chaotic systems by an improved biogeography-based optimization algorithm

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APPLIED INTELLIGENCE 2013年第1期39卷 132-143页

作者： Yang, Guo-Ping Liu, San-Yang Zhang, Jian-Ke Feng, Quan-Xi Xidian Univ Dept Math Xian 710071 Shannxi Peoples R China Xian Univ Posts & Telecommun Sch Sci Xian 710121 Peoples R China Guilin Univ Technol Coll Sci Guilin 541004 Guangxi Peoples R China

biogeography-based optimization algorithm (BBO) is a relatively new optimization technique which has been shown to be competitive to other biology-based algorithms. However, there is still an insufficiency in BBO regarding its migration operator, which is good at exploitation but poor at exploration. To address this concerning issue, we propose an improved BBO (IBBO) by using a modified search strategy to generate a new mutation operator so that the exploration and exploitation can be well balanced and then satisfactory optimization performances can be achieved. In addition, to enhance the global convergence, both opposition-based learning methods and chaotic maps are employed, when producing the initial population. In this paper, the proposed algorithm is applied to control and synchronization of discrete chaotic systems which can be formulated as high-dimension numerical optimization problems with multiple local optima. Numerical simulations and comparisons with some typical existing algorithms demonstrate the effectiveness and efficiency of the proposed approach.

关键词： Chaotic systems Control and synchronization biogeography-based optimization algorithm

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A biogeography-based optimization algorithm with local search for large-scale heterogeneous distributed scheduling with multiple process plans

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NEUROCOMPUTING 2024年 595卷

作者： Zhang, Yaya Gu, Xingsheng East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai 200237 Peoples R China

Since modern production mode has shifted from a single factory to a multi-factory production network, distributed scheduling has been derived. Distributed scheduling problem (DSP) is characterized by many varieties, large scale, redundant production factories, flexible production processes, and high-value products. Each factory in the heterogeneous DSP can be considered an individual entity, and there may be several production process plans. To address the heterogeneous DSP with multiple process plans, we consider minimizing the global makespan over all factories containing the transportation time delivering tasks from factories to their destinations and propose a biogeography-based optimization algorithm combined with local search based on heuristic rules (BBO-LH) to find the optimal production plan and enhance productivity. First, a new encoding scheme with three-segment representation has been developed to avoid illegal solutions and realize the information sharing between solutions selecting different process plans. Then two efficient local search approaches have been proposed based on different heuristic rules on sequence and equipment allocation respectively, to improve the search efficiency. Besides, BBO-LH has adopted a cosine migration rate model to replace the linear one to strengthen the ability to jump out of local optima. BBO-LH is compared with a genetic algorithm (GA_X), using generated examples to test the performance of the proposed strategies, and simulation results show the effectiveness of the proposed BBO-LH on large-scale heterogeneous DSP with multiple process plans.

关键词： Heterogeneous distributed scheduling Process plan biogeography-based optimization algorithm Local search Heuristic rule

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Game theory-based multi-task scheduling in cloud manufacturing using an extended biogeography-based optimization algorithm

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CONCURRENT ENGINEERING-RESEARCH AND APPLICATIONS 2019年第4期27卷 314-330页

作者： Xiao, Jiuhong Zhang, Wenyu Zhang, Shuai Zhuang, Xiaoyu Zhejiang Univ Finance & Econ Sch Informat Hangzhou 310018 Zhejiang Peoples R China

Cloud manufacturing is an emerging paradigm of global manufacturing networks. Through centralized management and operation of distributed manufacturing services, it can deal with different requirement tasks submitted by multiple customers in parallel. Therefore, the cloud manufacturing multi-task scheduling problem has attracted increasing attention from researchers. This article proposes a new cloud manufacturing multi-task scheduling model based on game theory from the customer perspective. The optimal result for a cloud manufacturing platform is derived from the Nash equilibrium point in the game. As the cloud manufacturing multi-task scheduling problem is known as an NP-hard combinatorial optimization problem, an extended biogeography-based optimization algorithm that embeds three improvements is presented to solve the corresponding model. Compared with the basic biogeography-based optimization algorithm, genetic algorithm, and particle swarm optimization, the experimental simulation results demonstrate that the extended biogeography-based optimization algorithm finds a better schedule for the proposed model. Its benefit is to provide each customer with reliable services that fulfill the demanded manufacturing tasks at reasonable cost and time.

关键词： cloud manufacturing multi-task scheduling game theory biogeography-based optimization algorithm

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Migration Ratio Model Analysis of biogeography-based optimization algorithm and Performance Comparison

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2016年第3期9卷 544-558页

作者： Wang, Jie-sheng Song, Jiang-di Univ Sci & Technol Liaoning Natl Financial Secur & Syst Equipment Engn Res Ct Anshan Liaoning Provin Peoples R China Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114044 Liaoning Provin Peoples R China

biogeography-based optimization (BBO) algorithm is based on species migration between habitats to complete information circulation and sharing, which achieves the global optimization by improving the adaptability of habitats. In this paper, the basic migration balance model of biogeography theory is elaborated. based on the population adaptive migration mechanism of BBO algorithm, the algorithm procedure is set up. Seven linear or nonlinear migration ratio models (including three new migration ratio models) are described. Simulation experiments are carried out on eight testing functions to verify the proposed migration ratio models. Simulation results show that different migration ratio model has different influence on the optimization performance of BBO algorithm, in which the sine migration ratio model has the best optimization performance. This also represents that the nonlinear migration ratio model close to the natural laws outperforms other simple linear migration ratio models.

关键词： biogeography-based optimization algorithm migration ratio model function optimization performance comparison

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BMDA: applying biogeography-based optimization algorithm and Mexican hat wavelet to improve dragonfly algorithm

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SOFT COMPUTING 2020年第21期24卷 15979-16004页

作者： Shirani, Mohammad Reza Safi-Esfahani, Faramarz Islamic Azad Univ Fac Comp Engn Najafabad Branch Najafabad Iran Islamic Azad Univ Big Data Res Ctr Najafabad Branch Najafabad Iran

One of the methods for solving optimization problems is applying metaheuristic algorithms that find near to optimal solutions. Dragonfly algorithm is one of the metaheuristic algorithms which search problem space by the inspiration of hunting and emigration behavior of dragonflies in nature. However, it suffers from the premature convergence of the population to an undesirable point in the detection ability (global search). In this research, an improved dragonfly algorithm called BMDA (applying biogeography-based algorithm, Mexican hat wavelet, and Dragonfly algorithm) is presented to resolve the premature convergence in high workloads by creating a mutation phase based on the combination of the biogeography-based optimization (BBO) migration process and the Mexican hat wavelet transform in dragonfly algorithm (DA). The algorithm was evaluated for the mean error in comparison with standard dragonfly algorithm (DA), Memory-based Hybrid Dragonfly algorithm (MHDA), chaotic dragonfly algorithm version 9 (CDA9), Adaptive_DA algorithm, bat algorithm (BAT), particle swarm optimization algorithm (PSO), raven roosting optimization (RRO) and whale optimization algorithm (WOA) using the CEC2017 benchmark functions. The implementation results of the proposed BMDA algorithm applying different benchmark functions outweighed the DA-based algorithm, MHDA algorithm, CDA9 algorithm, Adaptive_DA algorithm, BAT algorithm, PSO algorithm, RRO, and WOA algorithms in terms of mean error.

关键词： Dragonfly algorithm biogeography-based optimization algorithm Mexican hat wavelet

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