Swarm Intelligence最新文献

{"title":"On the evolution of adaptable and scalable mechanisms for collective decision-making in a swarm of robots","authors":"Ahmed Almansoori, Muhanad Alkilabi, Elio Tuci","doi":"10.1007/s11721-023-00233-4","DOIUrl":"https://doi.org/10.1007/s11721-023-00233-4","url":null,"abstract":"<p>A swarm of robots can collectively select an option among the available alternatives offered by the environment through a process known as collective decision-making. This process is characterised by the fact that once the group makes a decision, it can not be attributed to any of its group members. In swarm robotics, the individual mechanisms for collective decision-making are generally hand-designed and limited to a restricted set of solutions based on the voter or the majority model. In this paper, we demonstrate that it is possible to take an alternative approach in which the individual mechanisms are implemented using artificial neural network controllers automatically synthesised using evolutionary computation techniques. We qualitatively describe the group dynamics underlying the decision process on a collective perceptual discrimination task. We carry out extensive comparative tests that quantitatively evaluate the performance of the most commonly used decision-making mechanisms (voter model and majority model) with the proposed dynamic neural network model under various operating conditions and for swarms that differ in size. The results of our study clearly indicate that the performances of a swarm employing dynamical neural networks as the decision-making mechanism are more robust, more adaptable to a dynamic environment, and more scalable to a larger swarm size than the performances of the swarms employing the voter and the majority model. These results, generated in simulation, are ecologically validated on a swarm of physical e-puck2 robots.</p>","PeriodicalId":51284,"journal":{"name":"Swarm Intelligence","volume":"19 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139509403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Emergent communication enhances foraging behavior in evolved swarms controlled by spiking neural networks","authors":"Cristian Jimenez Romero, Alper Yegenoglu, Aarón Pérez Martín, Sandra Diaz-Pier, Abigail Morrison","doi":"10.1007/s11721-023-00231-6","DOIUrl":"https://doi.org/10.1007/s11721-023-00231-6","url":null,"abstract":"<p>Social insects such as ants and termites communicate via pheromones which allow them to coordinate their activity and solve complex tasks as a swarm, e.g. foraging for food or finding their way back to the nest. This behavior was shaped through evolutionary processes over millions of years. In computational models, self-coordination in swarms has been implemented using probabilistic or pre-defined simple action rules to shape the decision of each agent and the collective behavior. However, manual tuned decision rules may limit the emergent behavior of the swarm. In this work we investigate the emergence of self-coordination and communication in evolved swarms without defining any explicit rule. For this purpose, we evolve a swarm of agents representing an ant colony. We use an evolutionary algorithm to optimize a spiking neural network (SNN) which serves as an artificial brain to control the behavior of each agent. The goal of the evolved colony is to find optimal ways to forage for food and return it to the nest in the shortest amount of time. In the evolutionary phase, the ants are able to learn to collaborate by depositing pheromone near food piles and near the nest to guide other ants. The pheromone usage is not manually encoded into the network; instead, this behavior is established through the optimization procedure. We observe that pheromone-based communication enables the ants to perform better in comparison to colonies where communication via pheromone did not emerge. Furthermore, we assess the foraging performance of the ant colonies by comparing the SNN-based model to a multi-agent rule-based system. Our results show that the SNN-based model can efficiently complete the foraging task in a short amount of time. Our approach illustrates that even in the absence of pre-defined rules, self-coordination via pheromone emerges as a result of the network optimization. This work serves as a proof of concept for the possibility of creating complex applications utilizing SNNs as underlying architectures for multi-agent interactions where communication and self-coordination is desired.\u0000</p>","PeriodicalId":51284,"journal":{"name":"Swarm Intelligence","volume":"33 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138630920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved decentralized cooperative multi-agent path finding for robots with limited communication","authors":"Abderraouf Maoudj, Anders Lyhne Christensen","doi":"10.1007/s11721-023-00230-7","DOIUrl":"https://doi.org/10.1007/s11721-023-00230-7","url":null,"abstract":"","PeriodicalId":51284,"journal":{"name":"Swarm Intelligence","volume":"8 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139009780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decomposition and merging cooperative particle swarm optimization with random grouping for large-scale optimization problems","authors":"Alanna McNulty, Beatrice Ombuki-Berman, Andries Engelbrecht","doi":"10.1007/s11721-023-00229-0","DOIUrl":"https://doi.org/10.1007/s11721-023-00229-0","url":null,"abstract":"","PeriodicalId":51284,"journal":{"name":"Swarm Intelligence","volume":"67 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134957643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Elitist artificial bee colony with dynamic population size for multimodal optimization problems","authors":"Doğan Aydın, Yunus Özcan, Muhammad Sulaiman, Gürcan Yavuz, Zahid Halim","doi":"10.1007/s11721-023-00228-1","DOIUrl":"https://doi.org/10.1007/s11721-023-00228-1","url":null,"abstract":"","PeriodicalId":51284,"journal":{"name":"Swarm Intelligence","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135635421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the automatic design of multi-objective particle swarm optimizers: experimentation and analysis","authors":"Antonio J. Nebro, Manuel López-Ibáñez, José García-Nieto, Carlos A. Coello Coello","doi":"10.1007/s11721-023-00227-2","DOIUrl":"https://doi.org/10.1007/s11721-023-00227-2","url":null,"abstract":"Abstract Research in multi-objective particle swarm optimizers (MOPSOs) progresses by proposing one new MOPSO at a time. In spite of the commonalities among different MOPSOs, it is often unclear which algorithmic components are crucial for explaining the performance of a particular MOPSO design. Moreover, it is expected that different designs may perform best on different problem families and identifying a best overall MOPSO is a challenging task. We tackle this challenge here by: (1) proposing AutoMOPSO, a flexible algorithmic template for designing MOPSOs with a design space that can instantiate thousands of potential MOPSOs; and (2) searching for good-performing MOPSO designs given a family of training problems by means of an automatic configuration tool (irace). We apply this automatic design methodology to generate a MOPSO that significantly outperforms two state-of-the-art MOPSOs on four well-known bi-objective problem families. We also identify the key design choices and parameters of the winning MOPSO by means of ablation. AutoMOPSO is publicly available as part of the jMetal framework.","PeriodicalId":51284,"journal":{"name":"Swarm Intelligence","volume":"284 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135092929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Consensus decision-making in artificial swarms via entropy-based local negotiation and preference updating","authors":"Chuanqi Zheng, Kiju Lee","doi":"10.1007/s11721-023-00226-3","DOIUrl":"https://doi.org/10.1007/s11721-023-00226-3","url":null,"abstract":"","PeriodicalId":51284,"journal":{"name":"Swarm Intelligence","volume":"1 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48828059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of swarm density on collective tracking performance","authors":"Hian Lee Kwa, Julien Philippot, Roland Bouffanais","doi":"10.1007/s11721-023-00225-4","DOIUrl":"https://doi.org/10.1007/s11721-023-00225-4","url":null,"abstract":"How does the size of a swarm affect its collective action? Despite being arguably a key parameter, no systematic and satisfactory guiding principles exist to select the number of units required for a given task and environment. Even when limited by practical considerations, system designers should endeavor to identify what a reasonable swarm size should be. Here, we show that this fundamental question is closely linked to that of selecting an appropriate swarm density. Our analysis of the influence of density on the collective performance of a target tracking task reveals different ‘phases’ corresponding to markedly distinct group dynamics. We identify a ‘transition’ phase, in which a complex emergent collective response arises. Interestingly, the collective dynamics within this transition phase exhibit a clear trade-off between exploratory actions and exploitative ones. We show that at any density, the exploration–exploitation balance can be adjusted to maximize the system’s performance through various means, such as by changing the level of connectivity between agents. While the density is the primary factor to be considered, it should not be the sole one to be accounted for when sizing the system. Due to the inherent finite-size effects present in physical systems, we establish that the number of constituents primarily affects system-level properties such as exploitation in the transition phase. These results illustrate that instead of learning and optimizing a swarm’s behavior for a specific set of task parameters, further work should instead concentrate on learning to be adaptive, thereby endowing the swarm with the highly desirable feature of being able to operate effectively over a wide range of circumstances.","PeriodicalId":51284,"journal":{"name":"Swarm Intelligence","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136338878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-agent bandit with agent-dependent expected rewards","authors":"Fan Jiang, Huixin. Cheng","doi":"10.1007/s11721-023-00224-5","DOIUrl":"https://doi.org/10.1007/s11721-023-00224-5","url":null,"abstract":"","PeriodicalId":51284,"journal":{"name":"Swarm Intelligence","volume":"44 1","pages":"219-251"},"PeriodicalIF":2.6,"publicationDate":"2023-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80679086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-disciplinary approaches for designing intelligent swarms of drones","authors":"G. D. Croon, W. Hönig, G. Theraulaz, G. Vásárhelyi","doi":"10.1007/s11721-023-00223-6","DOIUrl":"https://doi.org/10.1007/s11721-023-00223-6","url":null,"abstract":"","PeriodicalId":51284,"journal":{"name":"Swarm Intelligence","volume":"17 1","pages":"1 - 4"},"PeriodicalIF":2.6,"publicationDate":"2023-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47347874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}