Article
Automation & Control Systems
Chao Huang, Brian D. O. Anderson, Hao Zhang, Huaicheng Yan
Summary: For a group of networked agents, f-consensus means reaching a consensus on the value of a desired function, f, based on the initial state of the individual agents. This paper demonstrates how f-consensus problems can often be converted into distributed convex optimization (DCO) problems, which can be easily solved using existing DCO algorithms. This approach offers computational advantages and can solve specific classes of f-consensus problems, including weighted power mean consensus and kth smallest value or kth order statistic consensus.
Article
Computer Science, Artificial Intelligence
Jinhua Zheng, Yubing Zhou, Juan Zou, Shengxiang Yang, Junwei Ou, Yaru Hu
Summary: In this paper, a hybrid prediction strategy based on the classification of decision variables is proposed to track moving optima in dynamic multi-objective optimization problems. By analyzing the impact and using different prediction methods for decision variables in a new environment, the algorithm can significantly improve dynamic optimization performance according to experimental results.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Qingyong Yang, Shu-Chuan Chu, Jeng-Shyang Pan, Jyh-Horng Chou, Junzo Watada
Summary: This paper proposes a dynamic multi-strategy integrated differential evolution algorithm based on reinforcement learning (RLDMDE) to address the poor self-adaptation of subpopulations in existing differential evolution algorithms. By utilizing reinforcement learning, each subpopulation can autonomously select mutation strategies based on the current environmental state. Additionally, an individual dynamic migration strategy is proposed to avoid wastage of computing resources. Experimental results demonstrate that the RLDMDE algorithm performs well and has strong competitiveness in solving optimization problems.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Information Systems
S. Raghul, G. Jeyakumar
Summary: Conventional optimization problems assume stationary constraints, but practical optimization problems are dynamic and uncertain. Combining multi-population approaches with nature-inspired algorithms can efficiently handle dynamic optimization problems. This study proposes a hybrid multi-population reinitialization strategy to address key issues in dynamic optimization problems and evaluates its effectiveness on a moving peak benchmark problem.
Article
Chemistry, Analytical
Yanlei Yin, Lihua Wang, Litong Zhang
Summary: In this paper, a multipopulation dynamic adaptive coevolutionary strategy is proposed to enhance the algorithm's global and local searching capability and optimization accuracy for large-scale optimization problems. The proposed algorithm exhibited a high optimization accuracy and converging rate for high-dimensional and large-scale complex optimization problems.
Article
Automation & Control Systems
Hamidreza Tavafoghi, Yi Ouyang, Demosthenis Teneketzis
Summary: We study a class of dynamic multi-agent decision problems with asymmetric information and nonstrategic agents, including signaling phenomenon. By introducing the notion of sufficient information, we propose an information state for each agent that is sufficient for decision-making purposes. We generalize the policy-independence property of belief in partially observed Markov decision processes to dynamic multi-agent decision problems.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Computer Science, Information Systems
Lisang Liu, Hui Xu, Bin Wang, Rongsheng Zhang, Jionghui Chen
Summary: This paper proposes a new particle swarm algorithm based on restart strategy and adaptive dynamic adjustment mechanism to solve the problems of path planning for mobile robots. The algorithm prevents falling into local extremums, premature convergence, and improves optimization, speed, and effectiveness of the path.
Article
Engineering, Multidisciplinary
Noha Hamza, Ruhul Sarker, Daryl Essam, Saber Elsayed
Summary: The number of research works on dynamic constrained optimization problems has been increasing rapidly over the past two decades. However, no research on dynamic problems with changes in the coefficients of the constraint functions has been reported. In this paper, a new evolutionary framework with multiple novel mechanisms is proposed to deal with such problems, and the results demonstrate its significant contribution in achieving good quality solutions, high feasibility rates, and fast convergence in rapidly changing environments.
ALEXANDRIA ENGINEERING JOURNAL
(2023)
Article
Computer Science, Artificial Intelligence
Xiaotian Pan, Liping Wang, Menghui Zhang, Qicang Qiu
Summary: This paper investigates resource allocation in a constraint environment to efficiently utilize limited resources and improve performance. The concept of return on investment (ROI) is introduced to measure the contributions of two populations, and an evolutionary resource allocation strategy (AER) is proposed to increase their ROI.
APPLIED INTELLIGENCE
(2023)
Article
Engineering, Electrical & Electronic
Mingqiang Wang, Lei Zhang, Zhiqiang Zhang, Zhenpo Wang
Summary: Efficient trajectory planning for intelligent vehicles in dynamic environments is achieved through a hybrid approach combining sampling-based and numerical optimization-based methods. A risk field model is used to evaluate risks with static and moving obstacles. The sampling-based approach generates collision-free trajectory candidates, considering curve smoothness, collision risk, and travel time. The optimization-based method optimizes the behavior trajectory for safety, vehicle dynamics stability, and driving comfort. Simulation results demonstrate the competency of the proposed framework in generating high-quality trajectories in real-time.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Computer Science, Information Systems
Xinming Zhang, Qiuying Lin
Summary: This paper proposes an improved SL-PSO algorithm, called TLS-PSO, which enhances the optimization performance of PSO through the use of three learning strategies and a hybrid learning mechanism. Experimental results demonstrate that TLS-PSO outperforms state-of-the-art PSO variants and other algorithms on complex functions and engineering problems, indicating its superior performance and potential for practical problem-solving.
INFORMATION SCIENCES
(2022)
Article
Automation & Control Systems
Baohua Shen, Mohammad Khishe, Seyedali Mirjalili
Summary: The Marine Predators Algorithm (MPA) is a novel hunting-based optimizer that utilizes a transition model between Levy Flight (LF) and Brownian Motion (BM) strategies. However, the discrete transition of the canonical MPA limits its performance in real-world optimization problems. In this paper, a soft dynamic transition strategy (DFSMPA) is proposed to address this shortcoming and it achieves significant improvement in benchmark functions and engineering problems.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Physics, Multidisciplinary
Cheng Peng, Cai Dai, Xingsi Xue
Summary: This article proposes a many-objective evolutionary algorithm based on dual selection strategy (MaOEA/DS) to solve many-objective optimization problems in high-dimensional space. The algorithm introduces a new distance function and a point crowding-degree (PC) strategy to assess diversity and balance convergence. Experimental results demonstrate the superiority of the proposed algorithm in overall performance compared to other state-of-the-art algorithms.
Article
Computer Science, Interdisciplinary Applications
Qi Su, Alex McAvoy, Joshua B. Plotkin
Summary: This study provides an analytical treatment of cooperation on dynamic networks and finds that transitions among network structures can promote the spread of cooperation. It also reveals that spatial and temporal heterogeneity have different effects on cooperation.
NATURE COMPUTATIONAL SCIENCE
(2023)
Article
Automation & Control Systems
Zhiqiang Zhang, Jan Lunze, Yuangong Sun, Zehuan Lu
Summary: This article presents distributed continuous-time algorithms with dynamic event-triggered communication to solve a convex optimization problem in a multiagent network, and demonstrates their effectiveness through numerical simulations.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Computer Science, Artificial Intelligence
Marina Torres, David A. Pelta, Jose L. Verdegay, Carlos Cruz
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2019)
Article
Nuclear Science & Technology
Jose-Luis Montes-Tadeo, Raul Perusquia-del-Cueto, David A. Pelta, Juan-Luis Francois, Juan-Jose Ortiz-Servin, Cecilia Martin-del-Campo, Alejandro Castillo
PROGRESS IN NUCLEAR ENERGY
(2020)
Article
Computer Science, Artificial Intelligence
Marina Torres, David A. Pelta, Maria T. Lamata, Ronald R. Yager
Summary: The focus is on selecting a set of interesting solutions based on decision maker's preferences, rather than relying on geometrical features. The proposed a posteriori approach assigns potential score intervals to each solution based on decision maker's preferences, and filters solutions using a possibility degree formula. Three examples with different numbers of objectives showcase the benefits of the proposal.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Hanane El Raoui, Mustapha Oudani, David A. Pelta, Ahmed El Hilali Alaoui
Summary: The study introduces a many-objective Customer-centric Perishable Food Distribution Problem and proposes a GVNS-based approach to efficiently solve subproblems and obtain a set of solutions. These solutions are evaluated over non-optimized criteria and ranked using an a posteriori approach, allowing for easy generation of different decision maker profiles and rankings of solutions.
Article
Multidisciplinary Sciences
Hanane El Raoui, Marcelino Cabrera-Cuevas, David A. Pelta
Summary: Optimization problems are prevalent in today's society, often requiring consideration of various characteristics and features of the real world. This paper discusses the important role of metaheuristics as solution generators in a problem-solving framework, as well as how to obtain high-quality solutions.
Article
Computer Science, Information Systems
Cynthia Porras, Boris Perez-Canedo, David A. Pelta, Jose L. Verdegay
Summary: This paper investigates the tourism trip design problem with time-dependent recommendation factors. By solving 27 real-world instances, it is found that including waiting times has little impact on the quality of solutions, and it leads to longer solving times. This highlights the importance of properly evaluating the benefits of making the problem model more complex.
Article
Business
Jose Luis Verdegay, Ma Teresa Lamata, David Pelta, Carlos Cruz
Summary: Computers process information and make decisions, with AI systems achieving levels of decision-making comparable to or exceeding humans. While these Autonomous Decision Systems can enhance efficiency, the potential for replacing humans raises concerns, making avoiding system malfunctions a top priority.
Article
Computer Science, Artificial Intelligence
Boris Perez-Canedo, Cynthia Porras, David A. Pelta, Jose Luis Verdegay
Summary: Decisions made in various fields such as economics, engineering, industry, and medical sciences rely on finding and interpreting solutions to optimization problems. It is important to consider the decision-making context as a filter, along with the natural constraints of the problem, to avoid obtaining optimal but irrelevant solutions. This article proposes a method of modeling contexts using fuzzy propositions and introduces two approaches (a priori and a posteriori) for solving optimization problems under their influence. The results provide researchers and practitioners with a methodology for more effective optimization and decision making.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Marina Torres, David A. Pelta, Jose L. Verdegay
INFORMATION PROCESSING AND MANAGEMENT OF UNCERTAINTY IN KNOWLEDGE-BASED SYSTEMS: APPLICATIONS, IPMU 2018, PT III
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Maria T. Lamata, David A. Pelta, Alejandro Rosete, Jose L. Verdegay
INFORMATION PROCESSING AND MANAGEMENT OF UNCERTAINTY IN KNOWLEDGE-BASED SYSTEMS: APPLICATIONS, IPMU 2018, PT III
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Maria Teresa Lamata, David A. Pelta, Jose Luis Verdegay
ADVANCES IN ARTIFICIAL INTELLIGENCE, CAEPIA 2018
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Marina Torres, Shouyong Jiang, David Pelta, Marcus Kaiser, Natalio Krasnogor
ADVANCES IN ARTIFICIAL INTELLIGENCE, CAEPIA 2018
(2018)
Article
Mathematics, Applied
E. H. Cables Perez, M. T. Lamata, D. Pelta, J. L. Verdegay
FUZZY INFORMATION AND ENGINEERING
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Marina Torres, David A. Pelta, Maria Teresa Lamata
2018 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE)
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Pavel Novoa-Hernandez, David A. Pelta, Carlos Cruz Corona
2018 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)
(2018)
