Article
Mathematics
Wenchao Yi, Zhilei Lin, Youbin Lin, Shusheng Xiong, Zitao Yu, Yong Chen
Summary: This paper proposes an epsilon-constrained method based on adaptive differential evolution to solve the optimal power flow problems. The effectiveness of the proposed algorithm is verified through single and multi-objective tests on the IEEE 30-bus test system. The comparison with state-of-the-art algorithms illustrates significant improvements.
Article
Engineering, Civil
Huy Tang, Jaehong Lee
Summary: In this paper, a discrete chaotic enhanced hybrid teaching-based differential evolution algorithm (CETDE) is proposed for efficiently solving realistic and challenging constrained discrete truss design problems. CETDE utilizes multiple mutation operators and embeds the chaotic logistic rule to prevent premature convergence. Furthermore, it introduces an improved logical strategy, an enhanced chaotic local search strategy, and a one-component change technique to enhance efficiency. CETDE is able to preserve the stochastic nature by maintaining both continuous and discrete population throughout the evolution, resulting in higher efficiency and solution optimality compared to existing discrete optimizers in literature.
Article
Computer Science, Information Systems
Hui Chen, Xiaobo Li, Shaolang Li, Yuxin Zhao, Junwei Dong
Summary: The Slime Mould Algorithm (SMA) is a meta-heuristics algorithm inspired by the behaviors of slime mould. Despite its effective performance, SMA tends to fall into local optima and lacks population diversity. This paper proposes an improved SMA algorithm named CHDESMA, which uses chaotic maps for better diversity and incorporates differential evolution for enhanced searching ability. Experimental results and statistical analysis show that CHDESMA performs competitively compared to advanced algorithms.
Article
Chemistry, Analytical
Wei Li, Wenyin Gong
Summary: This paper proposes an improved differential evolution algorithm (IMO-CADE) based on ant colony optimization for optimal power allocation in wireless sensor networks. By adaptively selecting operators, using constrained reward assignment, and parameter adaptation, IMO-CADE performs well under different conditions, especially with a large number of sensor nodes.
Article
Mathematics
Akram Belazi, Hector Migallon, Daniel Gonzalez-Sanchez, Jorge Gonzalez-Garcia, Antonio Jimeno-Morenilla, Jose-Luis Sanchez-Romero
Summary: This paper introduces an enhanced version of the sine cosine algorithm (ESCA algorithm) and designs several parallel algorithms to improve solution accuracy and convergence speed. Experimental results demonstrate the superiority of the proposed algorithm and its outstanding performance in engineering design problems. Additionally, the overall performance of the algorithm is statistically validated using non-parametric statistical tests.
Article
Computer Science, Artificial Intelligence
Jing Liang, Xuanxuan Ban, Kunjie Yu, Boyang Qu, Kangjia Qiao
Summary: In this paper, a rankings-based fitness function method is designed for efficiently selecting and utilizing promising infeasible solutions in solving constrained optimization problems using evolutionary algorithms. The method dynamically adjusts weights to balance constraints and objectives, and generates promising offspring using three differential evolution strategies. Experimental results show the proposed method's superior performance compared to other state-of-the-art methods, especially in solving real-world problems.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Information Systems
Qinghua Gu, Siping Huang, Qian Wang, Xuexian Li, Di Liu
Summary: This article proposes a Chaotic Differential Evolution and Symmetric Direction Sampling (CDE-SDS) method for large-scale multiobjective optimization. The CDE-SDS method utilizes chaotic differential evolution strategy to accelerate convergence and employs symmetric direction sampling strategy to explore the high-dimensional decision space. Experimental results show that CDE-SDS outperforms seven compared algorithms in terms of diversity and convergence under limited function evaluations.
INFORMATION SCIENCES
(2023)
Article
Engineering, Electrical & Electronic
Ying Cui, Yangchen Li, Chencheng Ye
Summary: This paper investigates sample-based and feature-based federated optimization, proposing FL algorithms using stochastic successive convex approximation (SSCA) and mini-batch techniques. The proposed algorithms adequately exploit the structures of the objective and constraint functions, and incrementally utilize samples. Numerical experiments demonstrate the inherent advantages of the proposed algorithms in convergence speeds, communication and computation costs, and model specifications.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2022)
Article
Computer Science, Artificial Intelligence
Jing Liang, Xuanxuan Ban, Kunjie Yu, Kangjia Qiao, Boyang Qu
Summary: This paper presents a constrained multiobjective differential evolution algorithm with an infeasible-proportion control mechanism, which addresses the handling of conflicting objectives and constraints through cooperative strategies and infeasible-proportion control. Experimental results demonstrate that the proposed algorithm outperforms or is at least comparable to existing constrained multiobjective optimization methods on various benchmark test functions.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Engineering, Multidisciplinary
Mohamad Faiz Ahmad, Nor Ashidi Mat Isa, Wei Hong Lim, Koon Meng Ang
Summary: Differential evolution (DE) is a popular optimization algorithm known for its simplicity and fast convergence rate. This study introduces a new DE variant called chaotic oppositional DE (CODE), which combines the strengths of chaotic maps and oppositional-based learning strategy to improve the quality of the initial population. The performance of CODE variants using seven different chaotic maps is evaluated, and chaotic circle oppositional DE (CCODE) is found to be the best performing variant. The optimization performance of CCODE is compared with other existing algorithms, demonstrating its superiority in terms of solution accuracy and convergence speed.
ALEXANDRIA ENGINEERING JOURNAL
(2022)
Article
Automation & Control Systems
Jing Liang, Kangjia Qiao, Kunjie Yu, Boyang Qu, Caitong Yue, Weifeng Guo, Ling Wang
Summary: This article explores and utilizes the relationship between constrained Pareto front (CPF) and unconstrained Pareto front (UPF) to solve constrained multiobjective optimization problems (CMOPs). A new constrained multiobjective evolutionary algorithm (CMOEA) is presented by dividing the evolutionary process into learning stage and evolving stage. Experimental results show that the proposed method has better performance compared to state-of-the-art CMOEAs, indicating the promising use of the relationship between CPF and UPF in solving CMOPs.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Yin-Yin Bao, Cheng Xing, Jie-Sheng Wang, Xiao-Rui Zhao, Xing-Yue Zhang, Yue Zheng
Summary: Teaching-Learning-Based Optimization (TLBO) is an intelligent optimization algorithm that simulates the teaching and learning processes. To overcome the issues of slow speed, low accuracy, and local optimization, an improved TLBO algorithm with Cauchy mutation and chaos operators is proposed. Dynamic teacher selection in the teaching phase leads to higher class average grades, while learning from the best students in the class during the learning phase improves class results. Cauchy mutation is performed after each teaching to increase population diversity and prevent local optima. Moreover, chaos theory is introduced to the TLBO algorithm to enhance convergence speed and accuracy. Experimental results on benchmark functions and engineering design problems demonstrate the significantly improved performance of the proposed TLBO algorithm.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Zhenzhen Hu, Wenyin Gong, Witold Pedrycz, Yanchi Li
Summary: This paper proposes a DRL-assisted co-evolutionary differential evolution algorithm (CEDE-DRL) that effectively utilizes DRL to help EAs solve constrained optimization problems. The method incorporates co-evolution into the extraction of training data, improves the accuracy of the neural network model through information exchange between multiple populations, and uses multiple constraint handling techniques for offspring selection. DRL is used to evaluate the population state, taking into account feasibility, convergence, and diversity. Additionally, an adaptive operator selection and individual archive elimination mechanism is added to avoid premature convergence and local optima.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Information Systems
Zhiqiang Zeng, Xiangyu Zhang, Zhiyong Hong
Summary: A novel constraint handling technique (CHT) that fuses two rankings is proposed in this paper, addressing the tradeoff between objective functions and constraints in constrained multiobjective optimization algorithms. Based on this CHT, a constrained multiobject differential evolution algorithm is proposed which combines four mutation operations to generate high-quality offspring. Experimental results demonstrate that the proposed algorithm outperforms eight state-of-the-art algorithms in five test suites.
INFORMATION SCIENCES
(2023)
Article
Engineering, Aerospace
Skylar A. Cox, Nathan B. Stastny, Greg N. Droge, David K. Geller
Summary: This paper addresses the problem of rendezvous and proximity operations constellation assignment by developing a responsive utility function for task allocation. It demonstrates that this methodology provides a robust technique for scheduling within resource-constrained agent constellations.
JOURNAL OF GUIDANCE CONTROL AND DYNAMICS
(2022)
