Article
Mechanics
P. Areias, H. C. Rodrigues, T. Rabczuk
Summary: The article demonstrates the application of conventional finite element technology to the topology optimization of 2D and 3D continua. By using nodal design variables and classical methods, smooth results are generated, side-constraints are addressed, and a single Lagrange multiplier is used to enforce volume constraint. Experimental results support the effectiveness of this density-based compliance minimization approach.
EUROPEAN JOURNAL OF MECHANICS A-SOLIDS
(2021)
Article
Engineering, Multidisciplinary
Mostafa Borhani
Summary: The study analyzes the air transport network structure using a multi-objective genetic algorithm to reduce air routes, improve passenger travel length, and minimize the number of stops per passenger. By combining point-to-point and hub-and-spoke topologies, the model successfully decreased air routes while increasing average travel length and route changes. The optimization model proved effective in improving airline topologies and reducing operational costs in the Iran airline industry.
AIN SHAMS ENGINEERING JOURNAL
(2021)
Article
Automation & Control Systems
Yousef Abdi, Mohammad Asadpour, Yousef Seyfari
Summary: In this study, a hybrid micro multi-objective evolutionary algorithm called mu MOSM is proposed to effectively address diversity loss and accelerate the convergence rate in approximating Pareto front solutions.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Mingjing Wang, Xiaoping Li, Yong Dai, Long Chen, Huiling Chen, Ruben Ruiz
Summary: Researchers have developed a method called Copula Incremental Learning (CIL) to improve the performance of the MOEA/D algorithm in problems with irregular Pareto Fronts (PFs) by generating non-uniform direction vectors. They also employ the Niche Hierarchical Selection (NHS) method to construct the neighborhood structure and prevent duplicate solutions. The use of convergence-guided direction (CGD) ensures efficiency by approximating irregular PFs. Statistical analysis shows that this method outperforms other competitive algorithms, particularly in handling multi-objective optimization problems with irregular PFs.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Zhixia Zhang, Hui Wang, Wensheng Zhang, Zhihua Cui
Summary: A cooperative-competitive two-stage game mechanism assisted many-objective evolutionary algorithm (MaOEA-GM) is proposed to address the conflicts between convergence and diversity and the lack of Pareto selection pressure in many-objective optimization problems (MaOPs). The algorithm includes a competition stage with a strategy pool and a new game utility function to balance convergence and diversity, and a cooperative stage where individuals choose their preferred environmental selection mechanism through voting. Experimental results show that the MaOEA-GM algorithm outperforms five advanced MaOEAs in terms of convergence, diversity, and competitiveness in solving MaOPs.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Maoqing Zhang, Lei Wang, Weian Guo, Wuzhao Li, Junwei Pang, Jun Min, Hanwei Liu, Qidi Wu
Summary: This paper introduces a dominance degree metric to enhance the comparability of non-dominated solutions in many-objective optimization problems. Based on this metric, a novel Many-Objective Evolutionary Algorithm is proposed, showing superior performance in terms of convergence, diversity, and spread compared to other state-of-the-art optimizers.
APPLIED SOFT COMPUTING
(2021)
Article
Engineering, Mechanical
Wenke Qiu, Qifu Wang, Liang Gao, Zhaohui Xia
Summary: This paper presents a novel approach to stress-based topology optimization using NURBS representations of geometric boundaries for stress minimization and stress-constrained problems. The methodology is compatible with CAD systems and uses Extended IsoGeometric Analysis (XIGA) for accurate stress field representation. A p-norm aggregation scheme and a Lagrange multiplier are employed to measure and enforce stress constraints. The proposed approach reduces computational costs and improves stability through a Lagrange multiplier trail strategy, normalization scheme, and a partial differential equation filter. Validation studies demonstrate its effectiveness and advantages over other FEA-based optimization methods in terms of computational efficiency. Overall, this paper makes a significant contribution to stress-based topology optimization in terms of accuracy, flexibility, and degree of freedom (DOF) cost.
INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES
(2023)
Article
Computer Science, Theory & Methods
Shouyong Jiang, Juan Zou, Shengxiang Yang, Xin Yao
Summary: Evolutionary dynamic multi-objective optimisation (EDMO) is a rapidly growing area that uses evolutionary approaches to solve multi-objective optimisation problems with time-varying changes. After nearly two decades, significant advancements have been made in theoretic research and applications. This article provides a comprehensive survey and taxonomy of existing research on EDMO, as well as highlighting multiple research opportunities for further development.
ACM COMPUTING SURVEYS
(2023)
Article
Computer Science, Artificial Intelligence
Tingrui Liu, Liguo Tan, Xin Li, Shenmin Song
Summary: This paper proposes an incremental learning-inspired mating restriction strategy (ILMR) to efficiently solve multiobjective optimization problems with complicated Pareto fronts. By establishing a mating pool and updating it as the population evolves, ILMR can learn new knowledge from high-quality offspring solutions and remove information from relatively poor solutions.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Information Systems
Maoqing Zhang, Lei Wang, Weian Guo, Wuzhao Li, Dongyang Li, Bo Hu, Qidi Wu
Summary: This paper proposes a relative non-dominance matrix and fitness formula to address the issue of dominance resistance in multi-objective optimization. Empirical analyses show that solutions with smaller fitness values are more likely to dominate other solutions in the evolutionary process and play a critical role in converging towards the true Pareto fronts. Additionally, the combination of k-means clustering strategy and the relative non-dominance matrix ensures diversity and adaptively adjusts the parameter k for environmental selection design.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Yongkuan Yang, Jianchang Liu, Shubin Tan
Summary: Many MOEAs are developed to solve CMOPs, but they encounter low efficiency for steady-state CMOPs. This paper proposes a multi-objective evolutionary algorithm named FACE, which maintains the known feasible solution in the second population and evolves together with the main population. Performance comparisons show the efficiency and scalability of FACE for steady-state CMOPs.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Information Systems
Hao Wang, Chaoli Sun, Gang Xie, Xiao-Zhi Gao, Farooq Akhtar
Summary: Surrogate-assisted multi-objective evolutionary algorithms have been focused on to solve expensive multi-objective problems. Gaussian process models are proposed for performance indicators instead of objective functions. The efficiency of the approach is validated on test suites and a real-world optimization problem, and it is found to be competitive compared to peer algorithms for expensive many-objective problems.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Ruochen Liu, Ping Yang, Haoyuan Lv, Weibin Li
Summary: This article proposes a multi-factorial evolutionary algorithm (MFEA) for solving the container placement problem in heterogeneous cluster environments. The MFEA algorithm, embedded with a local search strategy, significantly reduces optimization time and provides competitive solutions for container placement.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2023)
Article
Automation & Control Systems
Yulong Ye, Qiuzhen Lin, Ka-Chun Wong, Jianqiang Li, Zhong Ming, Carlos A. Coello Coello
Summary: This paper proposes a localized decomposition evolutionary algorithm (LDEA) to tackle imbalanced multi-objective optimization problems (MOPs). LDEA assigns a local region for each subproblem using a localized decomposition method and restricts the solution update within the region to maintain diversity. It also speeds up convergence by evolving only the best-associated solution in each subproblem while balancing the population's diversity.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Computer Science, Artificial Intelligence
Qinghua Gu, Shaopeng Zhang, Qian Wang, Neal N. Xiong
Summary: This paper proposes a two-stage multi-objective evolutionary algorithm based on direction vector guidance (DSMOEA) to solve the difficulty of generating high-quality solutions for variable linkages problems using conventional recombination operators. The algorithm transforms a portion of the population using the eigenmatrix of the covariance matrix to increase the probability of generating high-quality offspring, and creates direction vectors by selecting representative solutions in the transformed population. Under the guidance of the direction vectors, the population rapidly approaches the Pareto optimal set and generates promising offspring solutions. Differential Evolution (DE) is then performed for global searching to increase the diversity of the population. The algorithm is tested on three classes of variable linkages problems with different dimensions, and the results show its promising performance.
APPLIED SOFT COMPUTING
(2023)
