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共 102 条记录，以下是1-10 订阅

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A New Coati Optimization algorithm for Binary Optimization 15th

A New Coati Optimization Algorithm for Binary Optimization

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15th International Conference on Genetic and Evolutionary Computing, ICGEC 2023

作者： Wang, Gui-Ling Chu, Shu-Chuan Pan, Jeng-Shyang Yang, Dongqiang Shieh, Chin-Shiuh College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China School of Computer Science and Technology Shandong Jianzhu University Jinan China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan Department of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung City Taiwan

ISBN: (纸本)9789819700677

Coati Optimization algorithm (COA) is a recently proposed heuristic optimization algorithm. the COA establishes phrases of exploitation and exploration by mimicking coati behavior. A comparison with twelve well-known optimization algorithms demonstrates the robustness of the COA. However, it can only be directly applied to continuous problems and its application in binary problems has not been well implemented. therefore, a binary version (BCOA) is proposed in this paper. In BCOA, the positions of individuals in discrete space are mapped using sigmoid and V-shaped transfer functions. In addition, two experiments are underway to test the performance of BCOA. the comparison with the five optimization algorithms of the CEC2013 suite proved that BCOA has better optimization ability. Finally, this paper performed a Wilcoxon rank-sum test at a 5% significance level. the test shows that the proposed BCOA significantly outperforms the other algorithms. © 2024, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Optimization

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Barnacle Growth algorithm (BGA): A New Bio-Inspired Metaheuristic algorithm for Solving Optimization Problems 15th

Barnacle Growth Algorithm (BGA): A New Bio-Inspired Metaheur...

引用

15th International Conference on Genetic and Evolutionary Computing, ICGEC 2023

作者： Shao, Ankang Chu, Shu-Chuan Chen, Yeh-Cheng Wu, Tsu-Yang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Department of Computer Science University of California DavisCA001313 United States Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (纸本)9789819994113

Metaheuristic algorithms are an important area of artificial intelligence research and a popular method for solving complex optimization problems. In this paper, we propose a new metaheuristic algorithm called the Barnacle Growth algorithm (BGA). BGA simulates three stages of barnacle growth, namely planktonic stage, exploratory stage, and mature stage. In three stages, particles will have different behaviors and parameters. In the first process, BGA will find as many local optimal solutions as possible in the solution space. In the second process, it finds the global optimal solution around. In the third stage, BGA will re-explore the surrounding area for some search individuals to ensure that they will not fall into local optimum. the convergence speed performance of BGA is tested using the benchmark function of CEC 2013 by experiments. Experiment results are shown that BGA has better convergence speed and search ability on unimodal functions and multimodal functions, compared with the same type optimization algorithms. Applying the BGA algorithm to the optimization problem of city power transmission network, it is verified that the BGA algorithm can be applied to real problems. At the same time, the experimental results are shown that transmission route generated by the BGA algorithm can achieve the lowest power loss. © 2024, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Power transmission

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Fick’s Law algorithm with Gaussian Mutation: Design and Analysis 15th

Fick’s Law Algorithm with Gaussian Mutation: Design and A...

引用

15th International Conference on Genetic and Evolutionary Computing, ICGEC 2023

作者： Li, Haonan Chu, Shu-Chuan Kumari, Saru Wu, Tsu-Yang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Department of Mathematics Chaudhary Charan Singh University Uttar Pradesh Meerut250004 India Nanjing University of Information Science & Technology Nanjing210044 China

ISBN: (纸本)9789819994113

In recent years, an increasing number of meta-heuristic algorithms have been employed to tackle real-life problems, leading to the successive emergence of numerous meta-heuristic algorithms. Fick’s Law algorithm (FLA), introduced in 2022, is one such newly proposed meta-heuristic algorithm. FLA has exhibited strong performance in several optimization scenarios;nonetheless, similar to the majority of meta-heuristic algorithms, it is prone to falling into local optima. To mitigate this issue and enhance the algorithm’s capability to escape local optima, we improve FLA using Gaussian mutation, giving rise to the Fick’s law algorithm with Gaussian mutation (GM-FLA). In order to validate the performance of the proposed GM-FLA, we conduct comparative experiments between GM-FLA, SCA, WOA, and FLA using the benchmark functions from CEC2013 in dimensions 10D and 50D. Convergence curves were also plotted. the experimental results are demonstrated that GM-FLA exhibits promising performance across all dimensions and possesses favorable convergence characteristics. therefore, the proposed GM-FLA effectively avoids local optima and demonstrates the capability to escape from local optima. © 2024, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Gaussian distribution

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Manhattan Nonnegative matrix factorization using the alternating direction method of multipliers

Manhattan Nonnegative matrix factorization using the alterna...

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International workshop on Advanced Computational Intelligence (IWACI)

作者： Chan Cao Shuyu Tang Nian Zhang Xiangguang Dai Wei Zhang Yuming Feng Jiang Xiong Jinkui Liu Lara thompson Chongqing Three Gorges University Wanzhou China Department of Electrical and Computer Engineering University of the District of Columbia Washington D.C. USA Department of Mechanical Engineering Biomedical Engineering Program University of the District of Columbia Washington D.C. USA

Nonnegative matrix factorization (NMF) was a classic model for dimensional reduction. Manhattan NMF is a variant version of NMF that uses a L 1 -norm cost function as the objective function instead of the L 2 -norm cost function. Manhattan NMF can be formulated as a nonconvex nonsmooth optimization problem. An algorithm framework for solving the Manhattan NMF problem based on the alternating direction method of multiplication is presented to us. Compared with the existed algorithm, our proposed algorithm is more effective by experiments on synthetic and real data sets.

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Fuzzy-Adaptive Matrix-Based PSO with Group Learning

Fuzzy-Adaptive Matrix-Based PSO with Group Learning

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International workshop on Advanced Computational Intelligence (IWACI)

作者： Mingyu Li Peifa Sun Zhanyang Gao Xiaoyan Zhao Hu Jin College of Computer and Information Engineering Henan Normal University Xinxiang China Department of Electrical and Electronic Engineering Hanyang University Ansan South Korea

the particle swarm optimization (PSO) algorithm is a random search algorithm that simulates biological activities in nature and swarm intelligence. Traditional PSO algorithms still suffer from their inherent problems such as long computing time and premature convergence when dealing with a large number of decision variables. How to reduce the computing time while ensuring its accuracy is one of the main research issues in the literature. this paper proposes a PSO algorithm with matrix-based fuzzy adaptation and group learning. Firstly, to reduce the computing time, we propose a strategy to balance the global search ability and the local search ability by controlling the number of iterations adaptively. Second, to accelerate the convergence speed of the algorithm, we further propose a group learning mechanism. Along with the above two considerations, the proposed algorithm is able to achieve a promising balance between exploration and exploitation and, consequently, improves the performance. through extensive comparison experiments on six classic functions, we show that our proposed PSO algorithm can achieve competitive or even better performance than several state-of-the-art methods.

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A Constrained Sampling Assisted Differential Evolution for Expensive Optimization

A Constrained Sampling Assisted Differential Evolution for E...

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International workshop on Advanced Computational Intelligence (IWACI)

作者： Feng-Feng Wei Tai-You Chen Xuan-Li Shi Wei-Neng Chen School of Computer Science and Engineering South China University of Technology Guangzhou China

Surrogate-assisted evolutionary algorithms (SAEAs) have become a widely used method to solve computationally expensive optimization problems, in which the evaluation of objective has high time cost. However, in many problems, the evaluation of constraints also has high time cost. these problems are known as expensive constrained optimization problems (ECOPs). To solve ECOPs, this paper proposes a constrained sampling assisted differential evolution (CSADE) algorithm. CSADE consists a global search process and a local search process. Global search process aims to explore the whole search space. During this process, constraints are transformed as a penalty to fitness and a radial basis function (RBF) model is built for the penalty fitness. DE/rand/1 algorithm is adopted for candidate generation to improve the population diversity. Local search process aims to exploit the promising areas. In this process, the objective and each constraint are approximated by an RBF model respectively. DE/best/1 algorithm is utilized for population convergence in promising areas. Extensive experiments with several SAEAs for ECOPs on benchmark test suites demonstrate the competitive performance of CSADE. Besides, comparison experiments on the optimization of bulk carrier design show the promising application in engineering optimization of CSADE.

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A Scoring Model Considering the Variability of Subjects' Characteristics in Subjective experiments

A Scoring Model Considering the Variability of Subjects' Cha...

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International workshop on Quality of Multimedia Experience, QoMEx

作者： Lohic Fotio Tiotsop Antonio Servetti Enrico Masala Control and Computer Engineering Department Politecnico di Torino Turin Italy

Many authors argued that the scoring behavior of a subject in a subjective quality evaluation experiment can be modeled by two main characteristics, i.e., the subject's bias and the subject's inconsistency. However, for simplicity's sake, they disregarded the fact that subjects are usually less inconsistent when evaluating stimuli with very low or very high quality. this work addresses this shortcoming by providing an analytical formulation about how to link subjects' bias and inconsistency to the ground truth subjective quality of the stimulus under evaluation. By integrating this formulation into a state-of-the-art subject scoring model we obtain a more realistic model to recover the ground truth subjective quality of each stimulus. An iterative algorithm able to estimate the model parameters is also provided. Computational experiments show that our proposed model yields more realistic confidence intervals for the recovered ground truth subjective quality values and exhibits more robustness to synthetically added noise in several testing conditions.

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A Comparative Study on Crossover Operators of Genetic algorithm for Traveling Salesman Problem

A Comparative Study on Crossover Operators of Genetic Algori...

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International workshop on Advanced Computational Intelligence (IWACI)

作者： Xin-Ai Dou Qiang Yang Xu-Dong Gao Zhen-Yu Lu Jun Zhang School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China Department of Electrical and Electronic Engineering Hanyang University Ansan Korea

Genetic algorithm (GA) has been successfully employed to solve the traveling salesman problem (TSP). In GA, the crossover operator makes crucial influence on its optimization effectiveness and efficiency in solving TSP. therefore, many kinds of crossover operators have been proposed successively in the literature, but a systematic investigation of these operators has not ever been conducted. To fill this gap, this paper systematically compares 10 widely used crossover operators. By conducting extensive experiments on different TSP instances of different sizes, we investigate the optimization effectiveness of the 10 crossover operators in helping GA solve TSP. Experimental results demonstrate that the sequential constructive crossover (SCX) and the zoning crossover (ZX) are the two best crossover operators for GA to solve TSP. Hopefully, this comparative study could provide a guideline for readers and facilitate them to choose a suitable crossover operator for GA to solve TSP effectively.

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Frequent Trajectory Mining with Local Differential Privacy

Frequent Trajectory Mining with Local Differential Privacy

引用

International workshop on Advanced Computational Intelligence (IWACI)

作者： Yaqian Huang Huiwei Wang College of Electronic and Information Engineering Southwest University Chongqing China Key Laboratory of Intelligent Information Processing Chongqing Three Gorges University Chongqing China Chongqing Innovation Center Beijing Institute of Technology Chongqing China

With the rapid development of mobile Internet, trajectory data has become an indispensable data type in the era of big data due to its potential information. Since trajectory data of real number type cannot be directly applied to existing encoding mechanisms of local differential privacy and existing frequent item mining algorithms, we propose an approach called Private Frequent Trajectory Mining (PrivFTM). the implementation process of the algorithm is divided into three phases. Specifically, in the first phase, an adaptive spatial decomposition based on local differential privacy (PrivASD) is designed to discretize user trajectory data, while in the subsequent phase, the data collector completes the mining of candidate set and frequent items respectively according to the grouped user data. Extensive experiments performed on the two real-world datasets indicate that the performance of our PrivFTM is better than the existing algorithms.

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GA=PSO: Realizing Equivalent Particle Swarm Optimization by Genetic algorithm

GA=PSO: Realizing Equivalent Particle Swarm Optimization by ...

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International workshop on Advanced Computational Intelligence (IWACI)

作者： Ruoyu Zhu Zhonglong Zheng Hua Wang Xiangmin Zhou Dong Liu Lin Sun College of Computer and Information Engineering Henan Normal University Xinxiang China College of Mathematics and Computer Science Zhejiang Normal University Jinhua China Institute for Sustainable Industries & Liveable Cities Victoria University Melbourne Australia School of Computing Technologies RMIT University Melbourne Australia

Evolutionary computation algorithms (EC) are a kind of powerful stochastic optimization approaches inspired by biological phenomena, such as natural selection, genetic information transmission, and intelligent swarm behaviors. After years of development, EC has many branches. Among them, the most classical one is the genetic algorithm (GA), which is also the earliest EC method. though a lot of novel EC methods have been developed in the literature, they actually are all on the foundation of GA. However, little attention is put on the research on the connection between GA and the other EC algorithms. To fill this gap, this paper takes an in-depth study to investigate the relation between GA and the particle swarm optimization algorithm (PSO). By analyzing the operators in GA and PSO, this paper designs a new GA by devising novel crossover and mutation operators to simulate the evolution of particles in PSO. In this way, this paper gives the first transformation of GA to PSO and demonstrates that PSO is actually a special version of GA. experiments have conducted on the CEC2017 single-objective benchmark set. Experimental results show that the designed new GA achieves very similar performance with the classical PSO.

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