Article
Automation & Control Systems
Ying Hu, Yong Zhang, Dunwei Gong
Summary: The article explores a fuzzy multiobjective FS method called PSOMOFS, which utilizes particle swarm optimization and introduces fuzzy dominance relationship and fuzzy crowding distance measure to solve the feature selection problem with fuzzy cost. Experimental results demonstrate that the proposed method can achieve feature sets with superior performances in approximation, diversity, and feature cost.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Chemistry, Multidisciplinary
Beatriz C. Silva, Carine Menezes Rebello, Aliirio E. Rodrigues, Ana M. Ribeiro, Alexandre F. P. Ferreira, Idelfonso B. R. Nogueira
Summary: This study proposes a novel strategy for the design of adsorption heat pumps, which involves simultaneous optimization and material screening using the particle swarm optimization (PSO) approach. The proposed framework effectively evaluates different adsorbents and temperature intervals to find the optimal solution in terms of maximum performance and minimum heat supply cost. This approach provides a fast and intuitive evaluation of multiple design and operation variables.
Article
Engineering, Electrical & Electronic
Yu Zhou, Lin Gao, Dong Wang, Wenhui Wu, Zhiqiang Zhou, Tingqun Ye
Summary: In this study, an improved localized feature selection method based on multiobjective binary particle swarm optimization was proposed to address fault diagnosis by utilizing the local distribution of data without the need for balancing strategies.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Computer Science, Artificial Intelligence
Diana Cristina Valencia-Rodriguez, Carlos A. Coello Coello
Summary: Particle Swarm Optimization (PSO) is a bio-inspired metaheuristic algorithm that utilizes information exchange between particles to explore the search space. This study focuses on the influence of the number of connections among particles in Multi-Objective Particle Swarm Optimizers (MOPSOs) using random regular graphs as the swarm topology. Experimental results indicate that a higher connection degree can lead to algorithm instability in various problems, and MOPSOs with the same connection degree exhibit similar behavior.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Physics, Multidisciplinary
Huidong Ling, Xinmu Zhu, Tao Zhu, Mingxing Nie, Zhenghai Liu, Zhenyu Liu
Summary: This paper proposes a parallel multiobjective PSO weighted average clustering algorithm based on Apache Spark. The algorithm divides the entire dataset into multiple partitions and caches the data in memory using distributed parallel and memory-based computing of Apache Spark. The local fitness value of each particle is calculated in parallel according to the data in each partition, reducing the communication of data in the network. Additionally, a weighted average calculation of the local fitness values is performed to improve the problem of unbalanced data distribution affecting the results.
Article
Multidisciplinary Sciences
Pham Vu Hong Son, Nghiep Trinh Nguyen Dang
Summary: The study introduces a hybrid multi-verse optimizer model (hDMVO) that combines the multi-verse optimizer (MVO) and the sine cosine algorithm (SCA) to solve the discrete time-cost trade-off problem (DTCTP). The optimality of the algorithm is evaluated using 23 benchmark test functions, demonstrating its competitiveness with other algorithms. The performance of hDMVO is further evaluated using four benchmark test problems, showing its superiority in time-cost optimization for large-scale and complex projects compared to previous algorithms.
SCIENTIFIC REPORTS
(2023)
Article
Multidisciplinary Sciences
Shihong Yin, Qifang Luo, Guo Zhou, Yongquan Zhou, Binwen Zhu
Summary: This paper proposes a hybrid equilibrium optimizer slime mould algorithm (EOSMA) to efficiently solve the inverse kinematics problem of complex manipulators. A multi-objective version of EOSMA (MOEOSMA) is also introduced. Experimental results comparing with other algorithms reveal that this method performs well in terms of accuracy and computation time.
SCIENTIFIC REPORTS
(2022)
Article
Computer Science, Artificial Intelligence
Yanmin Liu, Shihua Wang, Xi Song, Jie Yang
Summary: In this paper, a novel multiobjective particle swarm optimization algorithm (RCDMOPSO) is proposed, which comprehensively considers spatial target and congestion information of particles. RCDMOPSO introduces a method called global proportional ranking (GPR) and combines it with cyclic distance to design novel external archive maintenance and global selection strategies. Experimental results show that RCDMOPSO outperforms other popular algorithms and is effective in tackling multiobjective optimization problems.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Multidisciplinary Sciences
Jianxin Tang, Hongyu Zhu, Jimao Lan, Li Zhang, Shihui Song
Summary: This paper proposes a learning-automata-driven discrete butterfly optimization algorithm (LA-DBOA) to efficiently solve the influence maximization problem. By exploiting the asymmetry of social connections and considering the topological features of discrete networks, the proposed algorithm achieves comparable influence spread to that of CELF and outperforms other classical methods, demonstrating the effectiveness of swarm intelligence-based meta-heuristics in solving the influence maximization problem.
Article
Chemistry, Multidisciplinary
Lingren Kong, Jianzhong Wang, Peng Zhao
Summary: Dynamic weapon target assignment (DWTA) is an effective method for solving the multi-stage battlefield fire optimization problem, with a meaningful and effective model established in this paper. The model includes conflicting objectives of maximizing combat benefits and minimizing weapon costs, as well as various constraints. An improved multiobjective particle swarm optimization algorithm (IMOPSO) is proposed to solve the complex DWTA problem, showing better convergence and distribution compared to other state-of-the-art algorithms in experimental results.
APPLIED SCIENCES-BASEL
(2021)
Article
Mathematics
Carine M. Rebello, Marcio A. F. Martins, Daniel D. Santana, Alirio E. Rodrigues, Jose M. Loureiro, Ana M. Ribeiro, Idelfonso B. R. Nogueira
Summary: This work presents a novel approach for multiobjective optimization problems, introducing the concept of the Pareto region to efficiently portray optimal conditions. By applying a clustering strategy, a balanced approach between objectives can be achieved, providing valuable insights for decision-making in process optimization. Benchmark results have shown the effectiveness of the proposed method in illustrating Pareto regions, demonstrating its potential impact on processes optimization and operation decision-making.
Article
Computer Science, Artificial Intelligence
Zeneng She, Wenjian Luo, Xin Lin, Yatong Chang, Yuhui Shi
Summary: This paper focuses on the study of multiparty multiobjective optimization problems (MPMOPs) and proposes a new algorithm OptMPNDS3 to solve these problems. Comparisons with other algorithms on a problem suite show that OptMPNDS3 performs strongly and similarly.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Interdisciplinary Applications
Gurwinder Singh, Amarinder Singh
Summary: This paper addresses a two-level time minimization transportation problem and proposes a solution procedure that hybridizes new algorithms within the Particle Swarm Optimization to efficiently utilize resources. The procedure eliminates the rigid constraints imposed by traditional techniques and provides a systematic approach.
MATHEMATICS AND COMPUTERS IN SIMULATION
(2023)
Article
Computer Science, Artificial Intelligence
Kanchan Jha, Sriparna Saha
Summary: The work introduces a new feature selection technique that combines multimodal multiobjective optimization and filter-based feature selection, aiming to generate diverse feature subsets and evaluate their quality using different measurements. By employing multiobjective PSO and non-dominated sorting with special crowding distance, the approach achieves the objectives of identifying a large number of Pareto-optimal solutions and selecting feature subsets with minimal redundancy and high correlation. Experimental results demonstrate that the multimodal PSO based feature selection approach outperforms its simple PSO counterpart in finding more feature subsets in multiobjective environment.
APPLIED SOFT COMPUTING
(2021)
Article
Automation & Control Systems
Li Li, Liang Chang, Tianlong Gu, Weiguo Sheng, Wanliang Wang
Summary: This paper introduces a novel multiobjective PSO algorithm named MOPSO/DD, which utilizes the dominant difference norm to tackle MaOPs. Experimental results demonstrate that the algorithm is competitive with state-of-the-art approaches on benchmark problems.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)