Triple competitive differential evolution for global numerical optimization

Yang, Qiang; Qiao, Zhuo-Yin; Xu, Peilan; Lin, Xin; Gao, Xu-Dong; Wang, Zi-Jia; Lu, Zhen-Yu; Jeon, Sang-Woon; Zhang, Jun

doi:10.1016/j.swevo.2023.101450

Cited by 17 publications

(3 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The future work of this study mainly lies in two aspects. One is to combine the hierarchical idea of triplets [30] to further optimize and find higher precision solutions. Another is to combine federated learning and resource allocation to better fit real-world scenarios.…”

Section: Discussionmentioning

confidence: 99%

An Inference Similarity-based Federated Learning Framework for Enhancing Collaborative Perception in Autonomous Driving

2024

KSII TIIS

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

An Inference Similarity-based Federated Learning Framework for Enhancing Collaborative Perception in Autonomous Driving

2024

KSII TIIS

View full text Add to dashboard Cite

“…Some other modifications of DE include population regeneration in case of premature convergence [29], modifications for binary search space [30], Gaussian-Cauchy mutation [31] and using an ensemble of mutation and crossover operators [32]. Some works focus on proposing new mutation strategies, such as the triple competitive approach recently proposed in [33]. In [34], the Unbounded DE (UDE) was proposed, where all the individuals generated throughout the whole search space are saved and used in the search.…”

Section: Parameter Adaptation In Differential Evolutionmentioning

confidence: 99%

Adaptation of the Scaling Factor Based on the Success Rate in Differential Evolution

Stanovov,

Semenkin

2024

Mathematics

View full text Add to dashboard Cite

Differential evolution is a popular heuristic black-box numerical optimization algorithm which is often used due to its simplicity and efficiency. Parameter adaptation is one of the main directions of study regarding the differential evolution algorithm. The main reason for this is that differential evolution is highly sensitive to the scaling factor and crossover rate parameters. In this study, a novel adaptation technique is proposed which uses the success rate to replace the popular success history-based adaptation for scaling factor tuning. In particular, the scaling factor is sampled with a Cauchy distribution, whose location parameter is set as an nth order root of the current success rate, i.e., the ratio of improved solutions to the current population size. The proposed technique is universal and can be applied to any differential evolution variant. Here it is tested with several state-of-the-art variants of differential evolution, and on two benchmark sets, CEC 2017 and CEC 2022. The performed experiments, which include modifications of algorithms developed by other authors, show that in many cases using the success rate to determine the scaling factor can be beneficial, especially with relatively small computational resource.

show abstract

“…DE has gained recognition for its efficiency, success, robustness, and global search capabilities. Nevertheless, it shows limited local search capability and slightly sluggish convergence [30]. To tackle these difficulties, recent research has concentrated on promoting variety among populations, expanding exploration and exploitation capacities through parameter management, and preventing early convergence.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Whale Optimization Algorithm with Differential Evolution and Lévy Flight for Robot Path Planning

TANG,

ZHAO

2024

ijacsa

View full text Add to dashboard Cite

Path planning is a prominent and essential part of mobile robot navigation in robotics. It allows robots to determine the optimal path from a given beginning point to a desired end goal. Additionally, it enables robots to navigate around obstacles, recognize secure pathways, and select the optimal route to follow, considering multiple aspects. The Whale Optimization Algorithm (WOA) is a frequently adopted approach to planning mobile robot paths. However, conventional WOA suffers from drawbacks such as a sluggish convergence rate, inefficiency, and local optimization traps. This study presents a novel methodology integrating WOA with Lévy flight and Differential Evolution (DE) to plan robot paths. As WOA evolves, the Levy flight promotes worldwide search capabilities. On the other hand, DE enhances WOA's ability to perform local searches and exploitation while also maintaining a variety of solutions to avoid getting stuck in local optima. The simulation results demonstrate that the proposed approach offers greater planning efficiency and enhanced route quality.

show abstract

Triple competitive differential evolution for global numerical optimization

Cited by 17 publications

References 66 publications

An Inference Similarity-based Federated Learning Framework for Enhancing Collaborative Perception in Autonomous Driving

An Inference Similarity-based Federated Learning Framework for Enhancing Collaborative Perception in Autonomous Driving

Adaptation of the Scaling Factor Based on the Success Rate in Differential Evolution

Enhancing Whale Optimization Algorithm with Differential Evolution and Lévy Flight for Robot Path Planning

Contact Info

Product

Resources

About