14 papers · ranked by Valyu relevance
Eliézer Béczi, Noémi Gaskó, Lisu Yu
Determining the critical nodes in a complex network is an essential computation problem. Several variants of this problem have emerged due to its wide applicability in network analysis. In this article we study the bi-objective critical node detection problem (BOCNDP), which is a new variant of the well-known critical…
Jie Yang, Yu-Kai Wang, Xin Yao, Chin-Teng Lin
The K-means algorithm is a widely used clustering algorithm that offers simplicity and efficiency. However, the traditional K-means algorithm uses a random method to determine the initial cluster centers, which make clustering results prone to local optima and then result in worse clustering performance. In this…
Jie You, Zhaoxuan Li, Junli Du, Praveen Kumar Donta
The iterative initialization method of this paper is a stochastic strategy, and it depends on samples completely. If K = 1, the initial parameter value is obtained in step 1 by using MLE. The algorithm iterates from step 2, gradually increasing the number of partitions until X is divided into K components. The main…
João Fé, Sérgio D. Correia, Slavisa Tomic, Marko Beko + 1 more
'Paolo Bellavista'] In the last decades, several swarm-based optimization algorithms have emerged in the scientific literature, followed by a massive increase in terms of their fields of application. Most of the studies and comparisons are restricted to high-level languages (such as MATLAB®) and testing methods on…
Zhen Wang, Dong Zhao, Ali Asghar Heidari, Yi Chen + 2 more
'Guoxi Liang'] Image segmentation techniques play a vital role in aiding COVID-19 diagnosis. Multi-threshold image segmentation methods are favored for their computational simplicity and operational efficiency. Existing threshold selection techniques in multi-threshold image segmentation, such as Kapur based on…
Yanzhao Gu, Junhao Wei, Zikun Li, Baili Lu + 3 more
'Ngai Cheong' 'Seyedali Mirjalili'] This paper analyzes the shortcomings of the traditional Whale Optimization Algorithm (WOA), mainly including the tendency to fall into local optima, slow convergence speed, and insufficient global search ability for high-dimensional and complex optimization problems. An improved…
Kaiyuan Zheng, Huiyong Liu, Bopeng Li
In engineering applications, many complex problems can be formulated as mathematical optimization challenges, and efficiently solving these problems is critical. Metaheuristic algorithms have proven highly effective in addressing a wide range of engineering issues. The Snake Optimization Algorithm (SO) is a novel…
Verusca Severo, Felipe B. S. Ferreira, Rodrigo Spencer, Arthur Nascimento + 2 more
'Arthur Nascimento' 'Francisco Madeiro' 'Steve Vanlanduit'] Vector Quantization (VQ) is a technique with a wide range of applications. For example, it can be used for image compression. The codebook design for VQ has great significance in the quality of the quantized signals and can benefit from the use of swarm…
Mohamed Elhosseny, Mahmoud Abdel-Salam, Anand Nayyar, Emre Çelik + 3 more
The Dung Beetle Optimization (DBO) algorithm is a relatively recent metaheuristic known for its simplicity, versatility, and low parameter dependence, making it a valuable tool for solving complex optimization problems. Despite its potential, DBO suffers from limitations such as slow convergence and premature…
Yuyong Tan, Jianfeng Wang, Bin Wang, Yongquan Zhou
The intelligent optimization algorithm has become a key tool in complex and intertwined engineering and science fields. However, with the increasing complexity of the problem and the rapid expansion of the data scale, the performance of the algorithm has been challenged unprecedentedly. The artificial lemming algorithm…
Pankaj Sharma, Rohit Salgotra, Saravanakumar Raju, Mohamed Abouhawwash + 1 more
'Mohamed Abouhawwash' 'S. S. Askar'] This paper presents a study to enhance the performance of a recently introduced naked mole-rat algorithm (NMRA), by local optima avoidance, and better exploration as well as exploitation properties. A new set of algorithms, namely Prairie dog optimization algorithm, INFO, and…
Shuxin Wang, Yejun Zheng, Li Cao, Mengji Xiong + 2 more
'Xuewen Xia'] In this study, a brand-new algorithm called the Comprehensive Adaptive Enterprise Development Optimizer (CAED) is proposed to overcome the drawbacks of the Enterprise Development (ED) algorithm in complex optimization tasks. In particular, it aims to tackle the problems of slow convergence and low…
Abdul Kader Kassoumeh, Zühal Kartal, Ahmet Arslan, Dragan Pamucar
This article introduces methods for initializing a single-trajectory-based metaheuristic, specifically a simulated annealing (SA) algorithm, using constructive heuristics. These methods are designed to target promising regions within the search space of an nondeterministic polynomial time (NP)-hard problem, namely the…
Eva Trojovská, Mohammad Dehghani, Víctor Leiva, Qingjia Chi
Metaheuristic optimization algorithms play an essential role in optimizing problems. In this article, a new metaheuristic approach called the drawer algorithm (DA) is developed to provide quasi-optimal solutions to optimization problems. The main inspiration for the DA is to simulate the selection of objects from…