Brain storm optimization algorithm for solving knowledge spillover problems

Cheng, Shi; Zhang, Mingming; Ma, Lianbo; Lu, Hui; Wang, Rui; Shi, Yuhui

doi:10.1007/s00521-020-05674-0

Cited by 11 publications

(5 citation statements)

References 33 publications

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“…Xu et al [35] introduced an improved BSO to handle a real-parameter numerical optimization problem. Cheng et al [36] proposed a modified BSO for solving a knowledge spillover problem. Hao et al [37] designed a hybrid BSO to tackle a distributed hybrid flow shop scheduling problem.…”

Section: Relevant Literature On Bsomentioning

confidence: 99%

“…The original BSO is straightforward in its design and can be easily implemented. Over the past years, BSO and its derivatives have achieved impressive results in tackling a range of complex problems, including real-parameter numerical optimization, distributed flow shops, and knowledge spillover problems [35][36][37][38][39][40]. Comprehensive experiments have verified that BSO possesses powerful performance to provide an outstanding compromise between exploration and exploitation abilities.…”

Section: Bsomentioning

confidence: 99%

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Multi-Objective Q-Learning-Based Brain Storm Optimization for Integrated Distributed Flow Shop and Distribution Scheduling Problems

Zhang,

Xu,

Qiao

2023

Mathematics

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In recent years, integrated production and distribution scheduling (IPDS) has become an important subject in supply chain management. However, IPDS considering distributed manufacturing environments is rarely researched. Moreover, reinforcement learning is seldom combined with metaheuristics to deal with IPDS problems. In this work, an integrated distributed flow shop and distribution scheduling problem is studied, and a mathematical model is provided. Owing to the problem’s NP-hard nature, a multi-objective Q-learning-based brain storm optimization is designed to minimize makespan and total weighted earliness and tardiness. In the presented approach, a double-string representation method is utilized, and a dynamic clustering method is developed in the clustering phase. In the generating phase, a global search strategy, a local search strategy, and a simulated annealing strategy are introduced. A Q-learning process is performed to dynamically choose the generation strategy. It consists of four actions defined as the combinations of these strategies, four states described by convergence and uniformity metrics, a reward function, and an improved ε-greedy method. In the selecting phase, a newly defined selection method is adopted. To assess the effectiveness of the proposed approach, a comparison pool consisting of four prevalent metaheuristics and a CPLEX optimizer is applied to conduct numerical experiments and statistical tests. The results suggest that the designed approach outperforms its competitors in acquiring promising solutions when handling the considered problem.

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Section: Relevant Literature On Bsomentioning

confidence: 99%

Section: Bsomentioning

confidence: 99%

Multi-Objective Q-Learning-Based Brain Storm Optimization for Integrated Distributed Flow Shop and Distribution Scheduling Problems

Zhang,

Xu,

Qiao

2023

Mathematics

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“…Inspired by the human brainstorming conference, BSO is guided by the cluster centers and other individuals according to a certain probability, which can balance convergence and diversity greatly. The main process of the BSO includes three important operations [25]: clustering, disruption, and creation, which is shown in Fig. 2.…”

Section: A Bso Algorithmmentioning

confidence: 99%

An Integrated Decision-Making Framework Based on Many-Objective Brain Storming Optimization for Urban Drainage System Design

Zheng

Wang

et al. 2022

IEEE Access

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Many cities around the world face the flooding management problems as extreme rainfall events have become more frequent. It's of great practical significance to design an effective urban drainage system (UDS) to improve the stormwater runoff quality. An integrated decision-making analysis framework based on many-objective brain storm optimization is proposed to make the optimal design of UDS. Firstly, a generic mathematical model is presented considering five objectives simultaneously. Secondly, an effective many-objective brain storm optimization based on local region dominance (DRP) is designed as the solver of the model and a set of Pareto-optimal solutions are obtained. Thirdly, two kinds of multi-attribute decision analysis methods are introduced to rank the Pareto-optimal solutions. Fourthly, the comparison experiments of a case study with three multi-objective evolutionary algorithms show the promising performance of the integrated decision-making framework and the corresponding optimization algorithm.

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“…(5) While t < T (6) Grouping all the N ideas into M clusters by the method in [42]; (7) Select the best idea from each cluster as the cluster center; % the phase of updating ideas % (8) For i � 1: N % from the first idea to the last one (9) If a random number rand() < P gen , then (10) Randomly select a cluster and determine its cluster center; (11) If a random number rand() < P cluster , (12) Select the cluster center; (13) Generate a new idea by the equation ( 7); (14) Implement the proposed disturbance operator; (15) Else (16) Randomly select a normal idea from this cluster (17) Generate a new idea by the equation ( 7); (18) Implement the proposed disturbance operator; (19) End if (20) Else (21) Randomly select two clusters; (22) If a random number rand() < P cluster , then (23) Select two cluster centers; (24) Generate a new idea by the equation ( 7); (25) Implement the proposed disturbance operator; (26) Else (27) Randomly select two normal ideas from the two clusters respectively; (28) Generate a new idea by the equation ( 7); (29) Implement the proposed disturbance operator; (30) End if (31) End if % the phase of selecting elite ideas % (32) Evaluate the new idea and update correspond old idea by Section 3.…”

Section: Analysis On the Time-varying Integer Update Strategymentioning

confidence: 99%

“…Brain storm optimization (BSO) is a new swarm intelligence algorithm simulating collective behavior of human being [23,24]. And it has been applied to a lot of real problems including system identification and electromagnetic antenna design [25][26][27][28][29][30][31][32][33]. Recently BSO-based FS algorithms have received much attention.…”

Section: Introductionmentioning

confidence: 99%

A Clustering-Guided Integer Brain Storm Optimizer for Feature Selection in High-Dimensional Data

Jia

Zhang

2021

Discrete Dynamics in Nature and Society

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For high-dimensional data with a large number of redundant features, existing feature selection algorithms still have the problem of “curse of dimensionality.” In view of this, the paper studies a new two-phase evolutionary feature selection algorithm, called clustering-guided integer brain storm optimization algorithm (IBSO-C). In the first phase, an importance-guided feature clustering method is proposed to group similar features, so that the search space in the second phase can be reduced obviously. The second phase applies oneself to finding optimal feature subset by using an improved integer brain storm optimization. Moreover, a new encoding strategy and a time-varying integer update method for individuals are proposed to improve the search performance of brain storm optimization in the second phase. Since the number of feature clusters is far smaller than the size of original features, IBSO-C can find an optimal feature subset fast. Compared with several existing algorithms on some real-world datasets, experimental results show that IBSO-C can find feature subset with high classification accuracy at less computation cost.

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Brain storm optimization algorithm for solving knowledge spillover problems

Cited by 11 publications

References 33 publications

Multi-Objective Q-Learning-Based Brain Storm Optimization for Integrated Distributed Flow Shop and Distribution Scheduling Problems

Multi-Objective Q-Learning-Based Brain Storm Optimization for Integrated Distributed Flow Shop and Distribution Scheduling Problems

An Integrated Decision-Making Framework Based on Many-Objective Brain Storming Optimization for Urban Drainage System Design

A Clustering-Guided Integer Brain Storm Optimizer for Feature Selection in High-Dimensional Data

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