Article
Computer Science, Artificial Intelligence
Yong Zhang, Xin-Fang Ji, Xiao-Zhi Gao, Dun-Wei Gong, Xiao-Yan Sun
Summary: This article introduces an objective-constraint mutual-guided surrogate-assisted particle swarm optimization algorithm for expensive constraint multimodal optimization problems. The algorithm utilizes a two-layer cooperative surrogate model framework and a partial evaluation strategy to reduce computational cost while obtaining multiple competitive feasible optimal solutions. It also proposes a hybrid update mechanism and a local search strategy to improve the algorithm's performance. Experimental results demonstrate the effectiveness of the proposed algorithm compared to existing methods.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Mechanics
Ricardo Fitas, Goncalo das Neves Carneiro, Carlos Conceicao Antonio
Summary: The inclusion of uncertainty in structural design optimization has led to more complex formulations, where uncertainty quantification significantly impacts solution methods and computing times. Designs that maintain steady levels of performance under uncertainty are referred to as robust, and the combination of both robustness and performance optimality is known as Robust Design Optimization (RDO). This study proposes a new approach to RDO for angle-ply composite laminate structures, utilizing a hybridization of Particle Swarm Optimization and Genetic Algorithms.
COMPOSITE STRUCTURES
(2023)
Article
Materials Science, Multidisciplinary
Limin Ma, Zhenghua Wang, Linghua Feng, Wende Dong, Wanlin Guo
Summary: The study focuses on the optimal sensing performance achieved by maximizing absorption at resonance frequencies using multi-band metamaterial absorbers. A constrained multi-objective optimization problem (CMOP) model is proposed to obtain the optimal geometric parameters for multi-band absorption. The simulation results show optimization of absorption for three narrow resonance peaks in the range of 0.5-2 THz, significantly improving the reflection loss compared to conventional methods. The reported design can be used as a refractive index sensor for liquid detection with high sensitivity.
OPTICAL MATERIALS EXPRESS
(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
Computer Science, Artificial Intelligence
Qinghua Gu, Qian Wang, Xuexian Li, Xinhong Li
Summary: A new algorithm, RFMOPSO, is proposed in this paper to optimize constrained combinatorial optimization problems by combining multi-objective particle swarm optimization with a random forest model. Adaptive ranking strategy and novel rule are employed to improve search speed and adaptively update particle states for better objective balance and feasible solutions. Experimental results show promising performance on benchmark problems with discrete variables varying from 10 to 100.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Hong Zhao, Zong-Gan Chen, Zhi-Hui Zhan, Sam Kwong, Jun Zhang
Summary: This paper investigates the multi-objective cardinality constrained portfolio optimization problem and proposes a multiple populations co-evolutionary particle swarm optimization algorithm to address this issue. The algorithm has advantages in dealing with cardinality constraints and multi-objective challenges through strategies such as hybrid encoding, heuristic method, local search, and elite competition.
Article
Computer Science, Artificial Intelligence
Deepika Shivani, Deepika Rani
Summary: The main objective of this study is to solve the non-linear fixed-charge transportation problem (NFCTP) using a non-linear particle swarm optimization (NPSO) algorithm with novel acceleration parameters and repair strategies. The efficiency of the algorithm is tested on small-scale and large-scale NFCTPs, and the results are compared with other genetic algorithms, showing that the proposed NPSO provides better feasible solutions in less computational time. The effectiveness of the NPSO algorithm is further demonstrated by comparing it with seven existing variants of PSO and outperforming them for both small and large scale NFCTPs.
APPLIED SOFT COMPUTING
(2023)
Article
Chemistry, Multidisciplinary
Milos Sedak, Maja Rosic
Summary: This paper proposes a modified hybrid algorithm, named HMOBPSO, to solve the challenging multi-objective optimization problem of a planetary gearbox. The proposed algorithm integrates PSO and BOA algorithms to improve performance. It can obtain non-convex Pareto optimal solutions, reduce gear weight and improve efficiency, and avoid early failure. Experimental results show significant improvements in gearbox size, efficiency, and spacing compared to conventional methods.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Information Systems
Hsuan-Yu Tseng, Pao-Hsien Chu, Hao-Chun Lu, Ming-Jyh Tsai
Summary: Particle swarm optimization (PSO) is a popular stochastic approach for solving practical optimal problems due to its effective performance and few hyperparameters. This study proposes easy particles inspired by lazy ant behavior to diversify moving directions and improve the exploration abilities of all referenced PSO-based algorithms in solving nonlinear constrained optimization (NCO) problems to reduce premature convergence.
Article
Automation & Control Systems
Guosen Li, Ting Zhou
Summary: This paper proposes a particle swarm optimizer based on reference point, termed RPPSO, which effectively handles global and local solutions in multimodal multi-objective optimization problems, achieving competitive performance on multiple benchmark test functions.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Yongkuan Yang, Pei-Qiu Huang, Xiangsong Kong, Jing Zhao
Summary: This paper proposes a novel constrained multi-objective evolutionary algorithm called CMAOO, which optimizes an (M+1)-objective optimization problem consisting of the original M objective functions and the degree of constraint violation. It constructs a main population and saves all feasible solutions in an external archive. The main population and the external archive are evolved to search the whole space and the feasible regions, respectively, and their offspring update the external archive and the main population separately. Experimental studies show that CMAOO is competitive in solving constrained multi-objective optimization problems compared to four state-of-the-art algorithms.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Information Systems
Zhenyu Meng, Yuxin Zhong, Guojun Mao, Yan Liang
Summary: In this paper, a new PSO variant is proposed to improve optimization performance by introducing a sorted particle swarm, novel adaptation schemes, and a fully-informed search scheme. Experimental results demonstrate the competitiveness of this algorithm with other state-of-the-art PSO variants on multiple test suites.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Interdisciplinary Applications
Xiaoli Shu, Yanmin Liu, Jun Liu, Meilan Yang, Qian Zhang
Summary: This paper proposes a multi-objective particle swarm optimization algorithm (D-MOPSO) to solve complex multi-objective optimization problems in the real world. It addresses the lack of convergence and diversity in traditional optimization methods and makes use of existing resources in the search process. D-MOPSO dynamically adjusts the population size based on the resources in the archive, improves particle exploration through local perturbations, and controls population size through non-dominated sorting and population density.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2023)
Article
Multidisciplinary Sciences
Heba Askr, M. A. Farag, Aboul Ella Hassanien, Vaclav Snasel, Tamer Ahmed Farrag
Summary: This paper proposes a novel many-objective African vulture optimization algorithm (MaAVOA) to solve many-objective optimization problems by simulating African vultures' foraging and navigation behaviors. The algorithm introduces a new social leader vulture for the selection process and adapts an environmental selection mechanism based on the alternative pool to maintain diversity. The best-nondominated solutions are saved in an external Archive based on the Fitness Assignment Method (FAM) and a Reproduction of Archive Solutions (RAS) procedure is developed to improve the quality of archiving solutions.
Article
Computer Science, Artificial Intelligence
Yun Hou, Guosheng Hao, Yong Zhang, Feng Gu, Wenyang Xu
Summary: This paper proposes a multi-objective discrete particle swarm optimization algorithm to solve the particle routing problem in distributed particle filters. Experimental results show that the algorithm is highly competitive and can provide multiple high-quality Pareto optimal solutions.
KNOWLEDGE-BASED SYSTEMS
(2022)
