Article
Automation & Control Systems
Wen-Hua Chen, Callum Rhodes, Cunjia Liu
Summary: This paper introduces an optimal autonomous search framework called DCEE for target localization in unknown environments. By using Bayesian inference to estimate target location and taking control actions to minimize error, DCEE can achieve optimal trade-off between exploitation and exploration while reducing estimation uncertainty.
Article
Computer Science, Interdisciplinary Applications
Jun-You Lin
Summary: Collaboration between universities and industries can enhance university innovation, but the type of collaboration and strategy selection play a crucial role in influencing the innovation outcomes. Active collaboration exploitation and exploration strategies contribute positively to university innovation, while combining ambidexterity with proactive search may result in more pronounced negative consequences.
Article
Computer Science, Information Systems
Nikhil Aditya, Siba Sankar Mahapatra
Summary: The study proposes an algorithm that enhances the exploration capability of GSA by using a disruption strategy with chaotic dynamics. It has been shown to outperform GSA, CGSA, and PSO on benchmark functions and can solve practical engineering problems effectively.
INFORMATION SCIENCES
(2023)
Article
Automation & Control Systems
Zhongguo Li, Wen-Hua Chen, Jun Yang
Summary: A concurrent learning framework is proposed for source search using autonomous platforms equipped with onboard sensors in an unknown environment. The proposed solution is computationally affordable and utilizes multiple parallel estimators to learn the operational environment and quantify estimation uncertainty.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Business
Stephan Billinger, Kannan Srikanth, Nils Stieglitz, Terry R. Schumacher
Summary: The study explores how decisions to continue or stop searching, as well as where to search, are interrelated in complex tasks. Results show that different feedback variables influence the decision to stop search rather than decisions about breadth of search. Thus, not accounting for the decision to continue (or stop) searching separately from breadth of search can lead to incorrect predictions about how feedback influences search behavior.
STRATEGIC MANAGEMENT JOURNAL
(2021)
Article
Chemistry, Multidisciplinary
Helong Yu, Shimeng Qiao, Ali Asghar Heidari, Lei Shi, Huiling Chen
Summary: The enhanced moth-flame optimization algorithm HMCMMFO proposed in this paper combines hybrid mutation and chemotaxis motion mechanisms to improve optimization accuracy and diversity, addressing the shortcomings of the original moth-flame optimization algorithm.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
Saeed Rafee Nekoo, Jose Angel Acosta, Anibal Ollero
Summary: This research investigates the application of an optimization algorithm in optimizing static and dynamic engineering problems. The algorithm generates random solutions, continuously refines them, and has been validated in structural optimization and dynamic problems such as vibration and control. The method's simplicity, along with its ability to avoid local minima/maxima, enables its application for a variety of problems.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Mathematics, Interdisciplinary Applications
Nathan Sandholtz, Yohsuke Miyamoto, Luke Bornn, Maurice A. Smith
Summary: This paper presents a probabilistic framework for estimating parameters of an acquisition function based on observed human behavior. By defining a likelihood function on the observed behavior, which is parameterized by a Bayesian optimization subroutine, it enables inference on an individual's acquisition function while accounting for deviations in behavior around the optimization solution.
Article
Computer Science, Artificial Intelligence
Xiaodong Zhao, Yiming Fang, Le Liu, Miao Xu, Qiang Li
Summary: This paper proposes a covariance-based Moth-Flame Optimization algorithm with Cauchy mutation (CCMFO) to solve numerical and real-world constrained optimization problems. CCMFO improves the information exchange ability and search performance by utilizing covariance to transform individuals to eigenspace and enhancing exploration with Cauchy mutation. Experimental results show that CCMFO performs well in parameter selection and tracking accuracy improvement.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Cybernetics
Cheyenne Dosso, Lynda Tamine, Pierre-Vincent Paubel, Aline Chevalier
Summary: This study aims to investigate the differences between complex lookup tasks and simple exploratory tasks, as well as the impact of users' prior domain knowledge on search strategies. The results showed that users in their knowledge domain were able to formulate more specific queries, achieve higher search scores, and obtain higher quality factual knowledge. Complex lookup tasks were more inclined towards thematic exploitation and navigational exploration than simple exploratory tasks. The prediction of search strategies on output performance varied depending on the search domain (in vs. out). Based on these findings, solutions to help users improve their search strategies are proposed.
BEHAVIOUR & INFORMATION TECHNOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Xiaodong Zhao, Yiming Fang, Shuidong Ma, Zhendong Liu
Summary: A multiswarm improved moth-flame algorithm (MIMFO) is proposed in this paper, which enhances the moth-flame optimization with chaotic grouping and dynamic regrouping mechanisms. The results show that MIMFO outperforms other swarm intelligence algorithms and MFO variants in terms of global optimum and convergence performance.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Mathematical & Computational Biology
Cesar A. Hernandez-Reyes, Shumpei Fukushima, Shunsuke Shigaki, Daisuke Kurabayashi, Takeshi Sakurai, Ryohei Kanzaki, Hideki Sezutsu
Summary: Insects exhibit basic behaviors such as food and mate searching by locating odor sources under turbulent odor plumes with a small number of neurons. Among insects, silk moths have been studied for their clear motor response to olfactory input. The study modeled silk moth olfactory search trajectories as a probabilistic learning agent with a belief of possible source locations, revealing that maneuvers mismatching the programmed behavior are related to larger entropy decrease, suggesting a balance between exploration and exploitation of olfactory information.
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
(2021)
Article
Computer Science, Artificial Intelligence
David Wittenberg, Franz Rothlauf, Christian Gagne
Summary: This study investigates the influence of corruption and sampling steps on a neural network-based genetic programming approach. The results show that both corruption strength and the number of sampling steps affect exploration and exploitation in search, and also impact performance.
GENETIC PROGRAMMING AND EVOLVABLE MACHINES
(2023)
Article
Computer Science, Artificial Intelligence
Sukanta Nama, Sushmita Sharma, Apu Kumar Saha, Amir H. Gandomi
Summary: The study introduces a modified BSA framework, gQR-BSA, which enhances search efficiency and addresses complex problems through methods like quasi-reflection-based initialization, quantum Gaussian mutations, adaptive parameter execution, and quasi-reflection-based jumping to alter the algorithm's coordinate structure.
ARTIFICIAL INTELLIGENCE REVIEW
(2022)
Article
Management
Roxana Barbulescu, Rocio Bonet
Summary: Research on the returns to specialist versus generalist careers has largely overlooked individuals' motivations to build different career profiles. This study introduces a novel mechanism, opportunity-enhancing generalism, where workers willingly give up the benefits of specialization to dissociate from past expertise with poor future prospects. The authors argue that negative feedback about advancement prospects in prior jobs increases motivation to search for jobs in new areas. Analyzing MBA job search data, they find support for their prediction.
ORGANIZATION SCIENCE
(2023)
Article
Chemistry, Physical
Xiaoxia Han, Jinzhou Yang, Hailong Guo, Zhifeng Qin, Shuyan Zhao, Yanxue Lu, Zhong Li, Jun Ren
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
(2016)
Article
Chemistry, Physical
Xiaoxia Han, Chaofan Zhao, Haixia Li, Shusen Liu, Yahong Han, Zhilei Zhang, Jun Ren
CATALYSIS SCIENCE & TECHNOLOGY
(2017)
Article
Chemistry, Physical
Xiaoxia Han, Wei Sun, Chaofan Zhao, Ruina Shi, Xuhui Wang, Shusen Liu, Zhong Li, Jun Ren
MOLECULAR CATALYSIS
(2017)
Article
Materials Science, Multidisciplinary
Xiaoxia Han, Chaofan Zhao, Wei Sun, Bingying Han, Yahong Han, Jun Ren
COMPUTATIONAL MATERIALS SCIENCE
(2018)
Article
Chemistry, Physical
Yuting Li, Xiaoxia Han, Chaofan Zhao, Lin Yue, Jinxian Zhao, Jun Ren
Article
Computer Science, Artificial Intelligence
Xiaoxia Han, Yingchao Dong, Lin Yue, Quanxi Xu, Gang Xie, Xinying Xu
Summary: In this article, a novel multi-objective optimization algorithm MOSTASA is proposed, which combines state-transition operators and the concept of Pareto dominance to generate and store Pareto optimal solutions, achieving a uniform distribution of solutions. Simulation experiments show that MOSTASA outperforms other algorithms in terms of efficiency and reliability.
APPLIED INTELLIGENCE
(2021)
Article
Computer Science, Hardware & Architecture
Yan Zhang, Xiaoxia Han, Yingchao Dong, Jun Xie, Gang Xie, Xinying Xu
Summary: This study introduces an intelligent algorithm for solving the multiple traveling salesman problem using metropolitan criteria and basic matrix operations to generate the solution space, while enhancing the ability to capture the global optimal solution by introducing the 2opt neighborhood search structure. Experimental results demonstrate the superior performance of this method in MTSP.
JOURNAL OF SUPERCOMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Xiaowei Qin, Xiaoxia Han, Junwen Chu, Yan Zhang, Xinying Xu, Jun Xie, Gang Xie
Summary: Cognitive computing involves discovering hidden rules and patterns in massive volumes of data. Density peaks clustering (DPC) is a powerful data mining tool that can efficiently detect clusters of arbitrary shapes. By utilizing the Jaccard coefficient to measure point similarity and implementing a two-step allocation strategy, accurate identification of density peaks can be achieved.
COGNITIVE COMPUTATION
(2021)
Article
Computer Science, Information Systems
Xiaoxia Han, Yingchao Dong, Lin Yue, Quanxi Xu