Article
Computer Science, Information Systems
Zhihua Cui, Yaqing Jin, Zhixia Zhang, Liping Xie, Jinjun Chen
Summary: This paper proposes an interval multi-objective optimization algorithm based on elite genetic strategy (EG-IMOEA) to solve practical multi-objective optimization problems with interval parameter (IMOPs). The algorithm considers a conditional-based interval confidence dominance relation and interval crowding distance (ICD) to evaluate the solutions more effectively. Experimental results demonstrate that the proposed algorithm outperforms other algorithms in terms of convergence, diversity, imprecision, and uniform distribution.
INFORMATION SCIENCES
(2023)
Review
Computer Science, Interdisciplinary Applications
Pinki Gulia, Rakesh Kumar, Wattana Viriyasitavat, Arwa N. Aledaily, Kusum Yadav, Amandeep Kaur, Gaurav Dhiman
Summary: This systematic review aims to evaluate the efficacy of fuzzy multi-objective optimization techniques in supporting decision-making in financial portfolio management under conditions of uncertainty. The review will critically appraise and compare different approaches to identify the most suitable technique for addressing the challenges posed by uncertain environments. Real-life decision-making scenarios involving the selection of investment options in the face of economic instability, political unrest, or natural disasters will be examined to provide practical insights.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2023)
Article
Computer Science, Information Systems
Zhixia Zhang, Mengkai Zhao, Hui Wang, Zhihua Cui, Wensheng Zhang
Summary: This paper explores task scheduling in cloud computing and presents an interval many-objective optimization model and evolutionary algorithm, which consider uncertain factors while improving scheduling efficiency and performance.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Dong Han, Wenli Du, Yaochu Jin, Wei Du, Guo Yu
Summary: This work proposes a fuzzy constraint handling technique to address the challenge of solving constrained multi-objective optimization problems (CMOPs). A fuzzy advantage concept is introduced to quantify the superiority of one solution over others, allowing infeasible solutions with promising fitness to survive. The proposed method is shown to be highly competitive in solving various CMOPs through comparisons with other algorithms.
INFORMATION SCIENCES
(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
Chemistry, Multidisciplinary
Jiehui Zheng, Mingming Tao, Zhigang Li, Qinghua Wu
Summary: This paper proposes a method called SetGSO to solve the stochastic many-objective optimal power flow (MaOPF) problem in power systems. The proposed SetGSO utilizes set-based individuals to represent the original stochastic variables and introduces hyper-volume and average imprecision metrics to transform the problem into a deterministic bi-objective OPF. Experimental results show that the SetGSO method outperforms sampling-based approaches in terms of computation time and metric improvement.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Artificial Intelligence
Fawei Ge, Kun Li, Ying Han
Summary: This paper proposes an improved algorithm LFOA-NSGA-III for solving interval many-objective optimization problems (IMaOPS), which effectively enhances optimization performance and population diversity by introducing matter-element extension model, K-mean algorithm, and local fruit fly optimization algorithm. Empirical evaluation on interval benchmark test problems and unmanned aerial vehicles path planning problem shows superior results compared to other algorithms, indicating the effectiveness and applicability of LFOA-NSGA-III in IMaOPS.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Zhenyu Shi, Tianhao Zhao, Qi Li, Zhixia Zhang, Zhihua Cui
Summary: This paper proposes a model for multi-objective optimized workflow migration in uncertain environments, considering the task dependencies and changing characteristics of edge server load. It also designs a migration-based interval many-objective evolutionary algorithm to solve this problem. Simulation results show that the algorithm achieves significant optimization effects in solving the objective values.
EGYPTIAN INFORMATICS JOURNAL
(2023)
Article
Computer Science, Artificial Intelligence
Juan Zou, Jing Liu, Jinhua Zheng, Shengxiang Yang
Summary: This paper proposes a multi-objective optimization algorithm based on staged coordination selection, consisting of convergence and diversity stages. The algorithm aims to balance convergence and diversity in evolutionary algorithms, showing improved performance compared to existing algorithms on various benchmark instances.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Automation & Control Systems
Kai Zhang, Chaonan Shen, Gary G. Yen
Summary: This article proposes a multipopulation-based differential evolution algorithm, called LSMaODE, to efficiently and effectively solve large-scale many-objective optimization problems. The algorithm divides the population into two groups of subpopulations and applies different optimization strategies. The performance is evaluated in both decision and objective dimensions.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Sanyan Chen, Xuewu Wang, Jin Gao, Wei Du, Xingsheng Gu
Summary: The paper introduces an adaptive switching strategy-based evolutionary algorithm to address the selection pressure and diversity issues in many-objective optimization. The algorithm dynamically switches between two deletion criteria in each generation to effectively remove poor solutions, demonstrating its effectiveness and advantages through comparisons with state-of-the-art algorithms on benchmark problems.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Information Systems
Qingzhu Wang, Lingling Zhang, Shuang Wei, Bin Li
Summary: A novel method called TASE is proposed, which is based on tensor decomposition for alternate evolution of sub-populations to solve multi- and many-objective optimization problems with large-scale decision variables. By introducing tensor canonical polyadic (CP) decomposition to divide heterogeneous variables into lower-dimensional sub-components, optimizing these sub-populations alternately in lower-dimensional decision subspaces, and designing a cross-population matching scheme, the algorithm achieves superior solution quality and convergence rate compared to other state-of-the-art algorithms on large-scale problems.
INFORMATION SCIENCES
(2021)
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
Automation & Control Systems
Songbai Liu, Qiuzhen Lin, Kay Chen Tan, Maoguo Gong, Carlos A. Coello Coello
Summary: This article proposes a fuzzy decomposition-based MOEA that estimates the population's shape using fuzzy prediction and selects weight vectors to fit the Pareto front shapes of different multi-objective optimization problems.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Mathematics
Chengxin Wen, Hongbin Ma
Summary: Many-objective optimization is an important research topic in evolutionary computing, and a two-stage hypervolume-based evolutionary algorithm is proposed to achieve convergence and diversity through global and local searches. Experimental results show that the algorithm is competitive in most cases.
