Enhancing the adaptability of Unmanned Aerial Vehicle (UAV) swarm control models to cope with different complex working scenarios is an important issue in this research field. To achieve this goal, control model with tunable parameters is a widely adopted approach. In this article, an improved UAV swarm control model with tunable parameters namely Multi-Objective O-Flocking (MO O-Flocking) is proposed. The MO O-Flocking model is a combination of a multi rule control system and a virtual-physical-law based control model with tunable parameters. To achieve multi-objective parameter tuning, a multi-objective parameter tuning method namely Improved Strength Pareto Evolutionary Algorithm 2 (ISPEA2) is designed. Simulation experiment scenarios include six target orientation scenarios with different kinds of objectives. Experimental results show that both the ISPEA2 algorithm and MO O-Flocking control model have good performance in their experiment scenarios.
提高无人飞行器（UAV）蜂群控制模型的适应性，以应对不同的复杂工作场景，是该研究领域的一个重要问题。为实现这一目标，参数可调的控制模型被广泛采用。本文提出了一种参数可调的改进型无人机蜂群控制模型，即多目标 O-Flocking（MO O-Flocking）。MO O-Flocking模型是多规则控制系统与基于虚拟物理定律的可调参数控制模型的结合。为实现多目标参数调整，设计了一种多目标参数调整方法，即改进强度帕累托进化算法 2（ISPEA2）。仿真实验场景包括六种不同目标的目标定向场景。实验结果表明，ISPEA2 算法和 MO O-Flocking 控制模型在各自的实验场景中都有良好的表现。
{"title":"Multi-Objective Rule System Based Control Model with Tunable Parameters for Swarm Robotic Control in Confined Environment","authors":"Yuan Wang;Lining Xing;Junde Wang;Tao Xie;Lidong Chen","doi":"10.23919/CSMS.2023.0022","DOIUrl":"https://doi.org/10.23919/CSMS.2023.0022","url":null,"abstract":"Enhancing the adaptability of Unmanned Aerial Vehicle (UAV) swarm control models to cope with different complex working scenarios is an important issue in this research field. To achieve this goal, control model with tunable parameters is a widely adopted approach. In this article, an improved UAV swarm control model with tunable parameters namely Multi-Objective O-Flocking (MO O-Flocking) is proposed. The MO O-Flocking model is a combination of a multi rule control system and a virtual-physical-law based control model with tunable parameters. To achieve multi-objective parameter tuning, a multi-objective parameter tuning method namely Improved Strength Pareto Evolutionary Algorithm 2 (ISPEA2) is designed. Simulation experiment scenarios include six target orientation scenarios with different kinds of objectives. Experimental results show that both the ISPEA2 algorithm and MO O-Flocking control model have good performance in their experiment scenarios.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"4 1","pages":"33-49"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10525674","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140895069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Power grids, due to their lack of network redundancy and structural interdependence, are particularly vulnerable to cascading failures, a phenomenon where a few failed nodes-having their loads exceeding their capacities—can trigger a widespread collapse of all nodes. Here, we extend the cascading failure (Motter-Lai) model to a more realistic perspective, where each node's load capacity is determined to be nonlinearly correlated with the node's centrality. Our analysis encompasses a range of synthetic networks featuring small-world or scale-free properties, as well as real-world network configurations like the IEEE bus systems and the US power grid. We find that fine-tuning this nonlinear relationship can significantly enhance a network's robustness against cascading failures when the network nodes are under attack. Additionally, the selection of initial nodes and the attack strategies also impact overall network robustness. Our findings offer valuable insights for improving the safety and resilience of power grids, bringing us closer to understanding cascading failures in a more realistic context.
{"title":"Cascading Failures in Power Grids: A Load Capacity Model with Node Centrality","authors":"Chaoyang Chen;Yao Hu;Xiangyi Meng;Jinzhu Yu","doi":"10.23919/CSMS.2023.0020","DOIUrl":"https://doi.org/10.23919/CSMS.2023.0020","url":null,"abstract":"Power grids, due to their lack of network redundancy and structural interdependence, are particularly vulnerable to cascading failures, a phenomenon where a few failed nodes-having their loads exceeding their capacities—can trigger a widespread collapse of all nodes. Here, we extend the cascading failure (Motter-Lai) model to a more realistic perspective, where each node's load capacity is determined to be nonlinearly correlated with the node's centrality. Our analysis encompasses a range of synthetic networks featuring small-world or scale-free properties, as well as real-world network configurations like the IEEE bus systems and the US power grid. We find that fine-tuning this nonlinear relationship can significantly enhance a network's robustness against cascading failures when the network nodes are under attack. Additionally, the selection of initial nodes and the attack strategies also impact overall network robustness. Our findings offer valuable insights for improving the safety and resilience of power grids, bringing us closer to understanding cascading failures in a more realistic context.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"4 1","pages":"1-14"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10525231","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140895070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
There are many studies about flexible job shop scheduling problem with fuzzy processing time and deteriorating scheduling, but most scholars neglect the connection between them, which means the purpose of both models is to simulate a more realistic factory environment. From this perspective, the solutions can be more precise and practical if both issues are considered simultaneously. Therefore, the deterioration effect is treated as a part of the fuzzy job shop scheduling problem in this paper, which means the linear increase of a certain processing time is transformed into an internal linear shift of a triangle fuzzy processing time. Apart from that, many other contributions can be stated as follows. A new algorithm called reinforcement learning based biased bi-population evolutionary algorithm (RB 2 EA) is proposed, which utilizes Q-learning algorithm to adjust the size of the two populations and the interaction frequency according to the quality of population. A local enhancement method which combimes multiple local search stratgies is presented. An interaction mechanism is designed to promote the convergence of the bi-population. Extensive experiments are designed to evaluate the efficacy of RB 2 EA, and the conclusion can be drew that RB 2 EA is able to solve energy-efficient fuzzy flexible job shop scheduling problem with deteriorating jobs (EFFJSPD) efficiently.
{"title":"Biased Bi-Population Evolutionary Algorithm for Energy-Efficient Fuzzy Flexible Job Shop Scheduling with Deteriorating Jobs","authors":"Libao Deng;Yingjian Zhu;Yuanzhu Di;Lili Zhang","doi":"10.23919/CSMS.2023.0021","DOIUrl":"https://doi.org/10.23919/CSMS.2023.0021","url":null,"abstract":"There are many studies about flexible job shop scheduling problem with fuzzy processing time and deteriorating scheduling, but most scholars neglect the connection between them, which means the purpose of both models is to simulate a more realistic factory environment. From this perspective, the solutions can be more precise and practical if both issues are considered simultaneously. Therefore, the deterioration effect is treated as a part of the fuzzy job shop scheduling problem in this paper, which means the linear increase of a certain processing time is transformed into an internal linear shift of a triangle fuzzy processing time. Apart from that, many other contributions can be stated as follows. A new algorithm called reinforcement learning based biased bi-population evolutionary algorithm (RB\u0000<sup>2</sup>\u0000EA) is proposed, which utilizes Q-learning algorithm to adjust the size of the two populations and the interaction frequency according to the quality of population. A local enhancement method which combimes multiple local search stratgies is presented. An interaction mechanism is designed to promote the convergence of the bi-population. Extensive experiments are designed to evaluate the efficacy of RB\u0000<sup>2</sup>\u0000EA, and the conclusion can be drew that RB\u0000<sup>2</sup>\u0000EA is able to solve energy-efficient fuzzy flexible job shop scheduling problem with deteriorating jobs (EFFJSPD) efficiently.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"4 1","pages":"15-32"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10525671","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140895034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper considers the impact of setup time in production scheduling and proposes energy-aware distributed hybrid flow shop scheduling problem with sequence-dependent setup time (EADHFSP-ST) that simultaneously optimizes the makespan and the energy consumption. We develop a mixed integer linear programming model to describe this problem and present a two-stage adaptive memetic algorithm (TAMA) with a surprisingly popular mechanism. First, a hybrid initialization strategy is designed based on the two optimization objectives to ensure the convergence and diversity of solutions. Second, multiple population co-evolutionary approaches are proposed for global search to escape from traditional cross-randomization and to balance exploration and exploitation. Third, considering that the memetic algorithm (MA) framework is less efficient due to the randomness in the selection of local search operators, TAMA is proposed to balance the local and global searches. The first stage accumulates more experience for updating the surprisingly popular algorithm (SPA) model to guide the second stage operator selection and ensures population convergence. The second stage gets rid of local optimization and designs an elite archive to ensure population diversity. Fourth, five problem-specific operators are designed, and non-critical path deceleration and right-shift strategies are designed for energy efficiency. Finally, to evaluate the performance of the proposed algorithm, multiple experiments are performed on a benchmark with 45 instances. The experimental results show that the proposed TAMA can solve the problem effectively.
{"title":"Two-Stage Adaptive Memetic Algorithm with Surprisingly Popular Mechanism for Energy-Aware Distributed Hybrid Flow Shop Scheduling Problem with Sequence-Dependent Setup Time","authors":"Feng Chen;Cong Luo;Wenyin Gong;Chao Lu","doi":"10.23919/CSMS.2024.0003","DOIUrl":"https://doi.org/10.23919/CSMS.2024.0003","url":null,"abstract":"This paper considers the impact of setup time in production scheduling and proposes energy-aware distributed hybrid flow shop scheduling problem with sequence-dependent setup time (EADHFSP-ST) that simultaneously optimizes the makespan and the energy consumption. We develop a mixed integer linear programming model to describe this problem and present a two-stage adaptive memetic algorithm (TAMA) with a surprisingly popular mechanism. First, a hybrid initialization strategy is designed based on the two optimization objectives to ensure the convergence and diversity of solutions. Second, multiple population co-evolutionary approaches are proposed for global search to escape from traditional cross-randomization and to balance exploration and exploitation. Third, considering that the memetic algorithm (MA) framework is less efficient due to the randomness in the selection of local search operators, TAMA is proposed to balance the local and global searches. The first stage accumulates more experience for updating the surprisingly popular algorithm (SPA) model to guide the second stage operator selection and ensures population convergence. The second stage gets rid of local optimization and designs an elite archive to ensure population diversity. Fourth, five problem-specific operators are designed, and non-critical path deceleration and right-shift strategies are designed for energy efficiency. Finally, to evaluate the performance of the proposed algorithm, multiple experiments are performed on a benchmark with 45 instances. The experimental results show that the proposed TAMA can solve the problem effectively.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"4 1","pages":"82-108"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10525672","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140895035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lin Wang;Cong Wang;Hongli Zhang;Ping Ma;Shaohua Zhang
A fractional-order memristor load Buck-Boost converter causes periodic system oscillation, electromagnetic noise, and other phenomena due to the frequent switching of the switch in actual operation, which is detrimental to the stable operation of the power electronic converter. It is of great significance to the study of the modeling method and chaos control strategy to suppress the nonlinear behavior of the Buck-Boost converter and expand the safe and stable operation range of the power system. An estimation-correction modeling method based on a fractional active voltage-controlled memristor load peak current Buck-Boost converter is proposed. The discrete numerical solution of the state variables in the continuous mode of the inductor current is derived. The bursting oscillation phenomenon when the system introduces external excitation is analyzed. Using bifurcation, Lyapunov exponent, and phase diagrams, a large number of numerical simulations are performed. The results show that the Buck-Boost converter is chaotic for certain selected parameters, which is the prerequisite for the introduction of the controller. Based on the idea of parameter perturbation and state association, a three-dimensional hybrid control strategy for a fractional memristor Buck-Boost converter is designed. The effectiveness of the control strategy is verified by simulations, and it is confirmed that the system is controlled in a stable periodic state when the external tunable parameter $s$ , which represents the coupling strength between the state variables in the system, gradually decreases in [−0.4, 0]. Compared with integer-order controlled systems, the stable operating range of fractional-order controlled systems is much larger.
{"title":"Estimation-Correction Modeling and Chaos Control of Fractional-Order Memristor Load Buck-Boost Converter","authors":"Lin Wang;Cong Wang;Hongli Zhang;Ping Ma;Shaohua Zhang","doi":"10.23919/CSMS.2024.0002","DOIUrl":"https://doi.org/10.23919/CSMS.2024.0002","url":null,"abstract":"A fractional-order memristor load Buck-Boost converter causes periodic system oscillation, electromagnetic noise, and other phenomena due to the frequent switching of the switch in actual operation, which is detrimental to the stable operation of the power electronic converter. It is of great significance to the study of the modeling method and chaos control strategy to suppress the nonlinear behavior of the Buck-Boost converter and expand the safe and stable operation range of the power system. An estimation-correction modeling method based on a fractional active voltage-controlled memristor load peak current Buck-Boost converter is proposed. The discrete numerical solution of the state variables in the continuous mode of the inductor current is derived. The bursting oscillation phenomenon when the system introduces external excitation is analyzed. Using bifurcation, Lyapunov exponent, and phase diagrams, a large number of numerical simulations are performed. The results show that the Buck-Boost converter is chaotic for certain selected parameters, which is the prerequisite for the introduction of the controller. Based on the idea of parameter perturbation and state association, a three-dimensional hybrid control strategy for a fractional memristor Buck-Boost converter is designed. The effectiveness of the control strategy is verified by simulations, and it is confirmed that the system is controlled in a stable periodic state when the external tunable parameter \u0000<tex>$s$</tex>\u0000, which represents the coupling strength between the state variables in the system, gradually decreases in [−0.4, 0]. Compared with integer-order controlled systems, the stable operating range of fractional-order controlled systems is much larger.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"4 1","pages":"67-81"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10525676","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140895054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Complexity science is an interdisciplinary scientific field that analyzes systems as holistic entities consisting of characteristics beyond the sum of a system's individual elements. This paper presents current research across the literature promoting cyber security as a complex adaptive system. We introduce complex systems concepts and fields of study, and deliver historical context, main themes, and current research relevant to cyber operations. Examples of cyber operations research leveraging agent-based modeling demonstrate the power of computational modeling grounded in complex systems principles. We discuss cyber operations as a scientific field, define current shortfalls for scientific rigor, and provide examples of how a complexity science foundation can further research and practice across a variety of cyber-based efforts. We propose standard definitions applicable to complex systems for cyber professionals and conclude with recommendations for future cyber operations research.
{"title":"Complexity Science and Cyber Operations: A Literature Survey","authors":"Briant Becote;Bhaskar Prasad Rimal","doi":"10.23919/CSMS.2023.0018","DOIUrl":"https://doi.org/10.23919/CSMS.2023.0018","url":null,"abstract":"Complexity science is an interdisciplinary scientific field that analyzes systems as holistic entities consisting of characteristics beyond the sum of a system's individual elements. This paper presents current research across the literature promoting cyber security as a complex adaptive system. We introduce complex systems concepts and fields of study, and deliver historical context, main themes, and current research relevant to cyber operations. Examples of cyber operations research leveraging agent-based modeling demonstrate the power of computational modeling grounded in complex systems principles. We discuss cyber operations as a scientific field, define current shortfalls for scientific rigor, and provide examples of how a complexity science foundation can further research and practice across a variety of cyber-based efforts. We propose standard definitions applicable to complex systems for cyber professionals and conclude with recommendations for future cyber operations research.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"3 4","pages":"327-342"},"PeriodicalIF":0.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10347383","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138550268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The distributed hybrid flow shop scheduling problem (DHFSP), which integrates distributed manufacturing models with parallel machines, has gained significant attention. However, in actual scheduling, some adjacent machines do not have buffers between them, resulting in blocking. This paper focuses on addressing the DHFSP with blocking constraints (DBHFSP) based on the actual production conditions. To solve DBHFSP, we construct a mixed integer linear programming (MILP) model for DBHFSP and validate its correctness using the Gurobi solver. Then, an advanced iterated greedy (AIG) algorithm is designed to minimize the makespan, in which we modify the Nawaz, Enscore, and Ham (NEH) heuristic to solve blocking constraints. To balance the global and local search capabilities of AIG, two effective inter-factory neighborhood search strategies and a swap-based local search strategy are designed. Additionally, each factory is mutually independent, and the movement within one factory does not affect the others. In view of this, we specifically designed a memory-based decoding method for insertion operations to reduce the computation time of the objective. Finally, two shaking strategies are incorporated into the algorithm to mitigate premature convergence. Five advanced algorithms are used to conduct comparative experiments with AIG on 80 test instances, and experimental results illustrate that the makespan and the relative percentage increase (RPI) obtained by AIG are 1.0% and 86.1% respectively, better than the comparative algorithms.
{"title":"Intelligent Optimization Under Multiple Factories: Hybrid Flow Shop Scheduling Problem with Blocking Constraints Using an Advanced Iterated Greedy Algorithm","authors":"Yong Wang;Yuting Wang;Yuyan Han;Junqing Li;Kaizhou Gao;Yusuke Nojima","doi":"10.23919/CSMS.2023.0016","DOIUrl":"https://doi.org/10.23919/CSMS.2023.0016","url":null,"abstract":"The distributed hybrid flow shop scheduling problem (DHFSP), which integrates distributed manufacturing models with parallel machines, has gained significant attention. However, in actual scheduling, some adjacent machines do not have buffers between them, resulting in blocking. This paper focuses on addressing the DHFSP with blocking constraints (DBHFSP) based on the actual production conditions. To solve DBHFSP, we construct a mixed integer linear programming (MILP) model for DBHFSP and validate its correctness using the Gurobi solver. Then, an advanced iterated greedy (AIG) algorithm is designed to minimize the makespan, in which we modify the Nawaz, Enscore, and Ham (NEH) heuristic to solve blocking constraints. To balance the global and local search capabilities of AIG, two effective inter-factory neighborhood search strategies and a swap-based local search strategy are designed. Additionally, each factory is mutually independent, and the movement within one factory does not affect the others. In view of this, we specifically designed a memory-based decoding method for insertion operations to reduce the computation time of the objective. Finally, two shaking strategies are incorporated into the algorithm to mitigate premature convergence. Five advanced algorithms are used to conduct comparative experiments with AIG on 80 test instances, and experimental results illustrate that the makespan and the relative percentage increase (RPI) obtained by AIG are 1.0% and 86.1% respectively, better than the comparative algorithms.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"3 4","pages":"282-306"},"PeriodicalIF":0.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10347379","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138550352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Harmony Search (HS) algorithm is highly effective in solving a wide range of real-world engineering optimization problems. However, it still has the problems such as being prone to local optima, low optimization accuracy, and low search efficiency. To address the limitations of the HS algorithm, a novel approach called the Dual-Memory Dynamic Search Harmony Search (DMDS-HS) algorithm is introduced. The main innovations of this algorithm are as follows: Firstly, a dual-memory structure is introduced to rank and hierarchically organize the harmonies in the harmony memory, creating an effective and selectable trust region to reduce approach blind searching. Furthermore, the trust region is dynamically adjusted to improve the convergence of the algorithm while maintaining its global search capability. Secondly, to boost the algorithm's convergence speed, a phased dynamic convergence domain concept is introduced to strategically devise a global random search strategy. Lastly, the algorithm constructs an adaptive parameter adjustment strategy to adjust the usage probability of the algorithm's search strategies, which aim to rationalize the abilities of exploration and exploitation of the algorithm. The results tested on the Computational Experiment Competition on 2017 (CEC2017) test function set show that DMDS-HS outperforms the other nine HS algorithms and the other four state-of-the-art algorithms in terms of diversity, freedom from local optima, and solution accuracy. In addition, applying DMDS-HS to data clustering problems, the results show that it exhibits clustering performance that exceeds the other seven classical clustering algorithms, which verifies the effectiveness and reliability of DMDS-HS in solving complex data clustering problems.
{"title":"Harmony Search Algorithm Based on Dual-Memory Dynamic Search and Its Application on Data Clustering","authors":"Jinglin Wang;Haibin Ouyang;Zhiyu Zhou;Steven Li","doi":"10.23919/CSMS.2023.0019","DOIUrl":"https://doi.org/10.23919/CSMS.2023.0019","url":null,"abstract":"Harmony Search (HS) algorithm is highly effective in solving a wide range of real-world engineering optimization problems. However, it still has the problems such as being prone to local optima, low optimization accuracy, and low search efficiency. To address the limitations of the HS algorithm, a novel approach called the Dual-Memory Dynamic Search Harmony Search (DMDS-HS) algorithm is introduced. The main innovations of this algorithm are as follows: Firstly, a dual-memory structure is introduced to rank and hierarchically organize the harmonies in the harmony memory, creating an effective and selectable trust region to reduce approach blind searching. Furthermore, the trust region is dynamically adjusted to improve the convergence of the algorithm while maintaining its global search capability. Secondly, to boost the algorithm's convergence speed, a phased dynamic convergence domain concept is introduced to strategically devise a global random search strategy. Lastly, the algorithm constructs an adaptive parameter adjustment strategy to adjust the usage probability of the algorithm's search strategies, which aim to rationalize the abilities of exploration and exploitation of the algorithm. The results tested on the Computational Experiment Competition on 2017 (CEC2017) test function set show that DMDS-HS outperforms the other nine HS algorithms and the other four state-of-the-art algorithms in terms of diversity, freedom from local optima, and solution accuracy. In addition, applying DMDS-HS to data clustering problems, the results show that it exhibits clustering performance that exceeds the other seven classical clustering algorithms, which verifies the effectiveness and reliability of DMDS-HS in solving complex data clustering problems.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"3 4","pages":"261-281"},"PeriodicalIF":0.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10347380","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138550223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cooperative spatial exploration in initially unknown surroundings is a common embodied task in various applications and requires satisfactory coordination among the agents. Unlike many other research questions, there is a lack of simulation platforms for the cooperative exploration problem to perform and statistically evaluate different methods before they are deployed in practical scenarios. To this end, this paper designs a simulation framework to run different models, which features efficient event scheduling and data sharing. On top of such a framework, we propose and implement two different cooperative exploration strategies, i.e., the synchronous and asynchronous ones. While the coordination in the former approach is conducted after gathering the perceptive information from all agents in each round, the latter enables an ad-hoc coordination. Accordingly, they exploit different principles for assigning target points for the agents. Extensive experiments on different types of environments and settings not only validate the scheduling efficiency of our simulation engine, but also demonstrate the respective advantages of the two strategies on different metrics.
{"title":"Modeling and Simulation of Cooperative Exploration Strategies in Unknown Environments","authors":"Yong Peng;Yue Hu;Chuan Ai","doi":"10.23919/CSMS.2023.0014","DOIUrl":"https://doi.org/10.23919/CSMS.2023.0014","url":null,"abstract":"Cooperative spatial exploration in initially unknown surroundings is a common embodied task in various applications and requires satisfactory coordination among the agents. Unlike many other research questions, there is a lack of simulation platforms for the cooperative exploration problem to perform and statistically evaluate different methods before they are deployed in practical scenarios. To this end, this paper designs a simulation framework to run different models, which features efficient event scheduling and data sharing. On top of such a framework, we propose and implement two different cooperative exploration strategies, i.e., the synchronous and asynchronous ones. While the coordination in the former approach is conducted after gathering the perceptive information from all agents in each round, the latter enables an ad-hoc coordination. Accordingly, they exploit different principles for assigning target points for the agents. Extensive experiments on different types of environments and settings not only validate the scheduling efficiency of our simulation engine, but also demonstrate the respective advantages of the two strategies on different metrics.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"3 4","pages":"343-356"},"PeriodicalIF":0.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10347385","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138550270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nowadays, autonomous robots are expected to accomplish more complex tasks and operate in an open-world environment with uncertainties. Developing software for such robots involves the design of task planning paradigms and the implementation of robotic software architectures, making software development rather tricky and time-consuming. In recent decades, component-based software development approaches have been increasingly adopted in robotics to improve software development efficiency by reusing data and controlling flows between components. However, few works have tackled the more critical issue of reusing complex high-level task planning paradigms and robotic software architectures. To make up for the limitation, this paper first identifies the mainstream task planning paradigms and proposes a set of novel patterns for interaction pipelines between the robotic functions of sensing, planning, and acting. Then this paper presents a novel Behavior Tree (BT) based development framework Structural-BT, which provides a set of reusable BT structures that implement abstract interaction pipelines while maintaining interfaces for task-specific customization. The Structural-BT framework supports the modular design of structure functionalities and allows easy extensibility of the inner planning flows between BT components. With the Structural-BT framework, software engineers can develop robotic software by flexibly composing BT structures to formulate the skeleton software architecture and implement task-specific algorithms when necessary. In the experiment, this paper develops robotic software for diverse task scenarios and selects the baseline approaches of Robot Operating System (ROS) and classical BT development frameworks for comparison. By quantitatively measuring the reuse frequencies and ratios of BT structures, the Structural-BT framework has been shown to be more efficient than the baseline approaches for robotic software development.
{"title":"Towards Efficient Robotic Software Development by Reusing Behavior Tree Structures for Task Planning Paradigms","authors":"Shuo Yang;Qi Zhang","doi":"10.23919/CSMS.2023.0017","DOIUrl":"https://doi.org/10.23919/CSMS.2023.0017","url":null,"abstract":"Nowadays, autonomous robots are expected to accomplish more complex tasks and operate in an open-world environment with uncertainties. Developing software for such robots involves the design of task planning paradigms and the implementation of robotic software architectures, making software development rather tricky and time-consuming. In recent decades, component-based software development approaches have been increasingly adopted in robotics to improve software development efficiency by reusing data and controlling flows between components. However, few works have tackled the more critical issue of reusing complex high-level task planning paradigms and robotic software architectures. To make up for the limitation, this paper first identifies the mainstream task planning paradigms and proposes a set of novel patterns for interaction pipelines between the robotic functions of sensing, planning, and acting. Then this paper presents a novel Behavior Tree (BT) based development framework Structural-BT, which provides a set of reusable BT structures that implement abstract interaction pipelines while maintaining interfaces for task-specific customization. The Structural-BT framework supports the modular design of structure functionalities and allows easy extensibility of the inner planning flows between BT components. With the Structural-BT framework, software engineers can develop robotic software by flexibly composing BT structures to formulate the skeleton software architecture and implement task-specific algorithms when necessary. In the experiment, this paper develops robotic software for diverse task scenarios and selects the baseline approaches of Robot Operating System (ROS) and classical BT development frameworks for comparison. By quantitatively measuring the reuse frequencies and ratios of BT structures, the Structural-BT framework has been shown to be more efficient than the baseline approaches for robotic software development.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"3 4","pages":"357-380"},"PeriodicalIF":0.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10347382","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138550269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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