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检索条件"主题词=fireworks algorithm"

共 265 条记录，以下是81-90 订阅

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Enhancing fireworks algorithm in Local Adaptation and Global Collaboration for Solving ICSI 2022 Benchmark Problems 13th

Enhancing Fireworks Algorithm in Local Adaptation and Global...

引用

13th International Conference on Swarm Intelligence (ICSI)

作者： Liu, Yifan Li, Yifeng Tan, Ying Peking Univ Sch Artificial Intelligence Inst Artificial Intelligence Key Lab Machine Percept MOE Beijing Peoples R China

ISBN: (纸本)9783031097263;9783031097263

fireworks algorithm is a swarm intelligence optimization algorithm with superior performance, which can be used to solve various practical optimization problems. To enhance the performance of fireworks algorithm, we introduce a powerful local search mechanism and add multiple cooperative strategies. These strategies improve the local exploitation capability and global exploration capability of fireworks algorithm. The experimental results on the ICSI'2022 test set demonstrate that the performance of the algorithm is satisfactory.

关键词： fireworks algorithm Swarm intelligence Optimization Collaboration optimization

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An Improved fireworks algorithm for Integrated Flight Timetable and Crew Schedule Recovery Problem 13th

An Improved Fireworks Algorithm for Integrated Flight Timeta...

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13th International Conference on Swarm Intelligence (ICSI)

作者： Gan, Xiaobing Zhou, Tianwei Mai, Yuhan Zhong, Huifen Zhang, Xiuyun Xiao, Qinge Shenzhen Univ Coll Management Shenzhen 518060 Peoples R China Univ Macau Fac Business & Adm Macau 999078 Peoples R China Tianjin Univ Technol & Educ Sch Automat & Elect Engn Tianjin 300222 Peoples R China

ISBN: (纸本)9783031096778;9783031096761

Abnormal flights, which deviate from their scheduled plans, incurred huge costs for airlines and serious inconvenience for passengers. This phenomenon occurs frequently, especially under the influence of COVID-19 and requires high-quality solution within short time limits. To mitigate these negative effects, first, an integrated flight timetable and crew schedule recovery model with the aim of minimizing total cost is constructed in this paper. Second, an improved fireworks algorithm is proposed to effectively solve the model. Finally, an unscheduled temporary aircraft maintenance scenario is obtained to illustrate the superiority of the proposed algorithm in terms of computing time and solution quality.

关键词： Integrated flight timetable and crew Abnormal flight Recovery fireworks algorithm

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Route Planning in a New Tourist Recommender System: a fireworks algorithm Based Approach

Route Planning in a New Tourist Recommender System: a Firewo...

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IEEE Congress on Evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Ding, Hui Ke, Liangjun Geng, Zihao Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing Peoples R China

ISBN: (纸本)9781509006229

With the development of online tourist, Tourist Recommender System (TRS) has become a hot research topic. This paper presents a TRS and considers a new tourist trip design problem (TTDP), taking into account the compactness of the trip. To solve this problem, fireworks algorithm (FWA) is adopted. As a meta-heuristic method, FWA has been widely used in continuous problems, while TTDP is a discrete optimization problem with various constraints, the key difference between them lies in the definition of distance. In this paper, operators of the conventional FWA are redesigned to handle with TTDP. Experimental results demonstrate the effectiveness of the proposed FWA.

关键词： fireworks algorithm tourism recommendation TTDP

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Multi-Constrained Routing Based on Particle Swarm Optimization and fireworks algorithm 44

Multi-Constrained Routing Based on Particle Swarm Optimizati...

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44th Annual Conference of the IEEE Industrial-Electronics-Society (IECON)

作者： Hu, Youbing Wang, Kun Wan, Jinjiang Wang, Kaidong Hu, Xia Xidian Univ Sci & Technol Commun Networks Lab Shijiazhuang Sch Comp Sci & Technol Xian Peoples R China Xidian Univ Sch Comp Sci & Technol Xian Peoples R China Elect Technol Grp Corp Res Inst China 54 Shijiazhuang Hebei Peoples R China

ISBN: (纸本)9781509066841

This paper sets up a mathematical model that satisfies the multiconstrained routing optimization problem. By adding a penalty, multiple constraints are mapped to a fitness that satisfies multiple constraints. Then, it uses a heuristic routing algorithm based on particle swarm optimization (PSO) to perform heuristic routing search. Introducing the fireworks algorithm (FWA) based on the PSO search algorithm, our algorithm searches the optimal solution more quickly. Besides, it reduces the defect of PSO falling into the local optimum. Simulation shows the algorithm can effectively solve the multiconstrained routing problem in large-scale networks. While searching for optimal solutions, the success rate of the algorithm is about 5.21% higher than that of the standard PSO algorithm. That is improved by using the ant colony algorithm. The PSO-ACO algorithm is about 2.57% higher than the problem. The average cost of the final search is about 4.36% higher than that of the standard PSO algorithm. It is about 1.34% higher than the PSO-ACO algorithm improved by the ant colony algorithm.

关键词： multiple constraints routing Quality of Service penalty function particle swarm optimization algorithm fireworks algorithm

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Multi-Scale Collaborative fireworks algorithm

Multi-Scale Collaborative Fireworks Algorithm

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IEEE Congress on Evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Li, Yifeng Tan, Ying Peking Univ Sch Elect Engn & Comp Sci Key Lab Machine Percept MOE Dept Machine Intelligence Beijing Peoples R China

ISBN: (纸本)9781728169293

fireworks algorithm (FWA) is a special swarm intelligent optimization algorithm, which controls multiple subgroups of the population to search collaboratively. Instead of assigning fireworks to different local areas, we propose the multi scale collaborative firework algorithm (MSCFWA) which helps fireworks to search at coordinated scales. Since the collaboration of search scales is accomplished by restarting or adjusting fireworks whose local search are not making meaningful progress, fireworks in MSCFWA are able to exploit different local areas independently or cooperate in the same local area with different search scales. Experimental results show that the proposed strategy stably improved the overall optimization performance of fireworks algorithm on the benchmark functions of the CEC'13 competition significantly. It also shows outstanding efficiency compared with typical swarm intelligence optimization algorithms and evolutionary algorithms.

关键词： fireworks algorithm Multi-modal Optimization Swarm Intelligence Evolutionary algorithm Multi-Scale Optimization

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Multilevel Image Thresholding by fireworks algorithm 25

Multilevel Image Thresholding by Fireworks Algorithm

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25th International Conference RADIOELEKTRONIKA

作者： Tuba, Milan Bacanin, Nebojsa Alihodzic, Adis Megatrend Univ Belgrade Grad Sch Comp Sci Bulevar Umetnosti 29 Belgrade 11070 Serbia Univ Sarajevo Fac Math Sarajevo Bosnia & Herceg

ISBN: (纸本)9781479981175

This paper presents implementation of the recent fireworks algorithm adjusted for solving multilevel image thresholding problem. This is an important problem since it is often used in image processing for the purpose of image segmentation. Since the number of possible threshold combinations grows exponentially with the number of desirable thresholds, standard deterministic methods could not generate satisfying results when tackling this problem. To test the performance of our proposed approach, we employed Kapur's maximum entropy thresholding function on standard benchmark images where the optimal solutions are known (up to five thresholding points) from the exhaustive search. Results show that our approach has great potential in this field.

关键词： Mutilevel image thresholding fireworks algorithm swarm intelligence nature inspired algorithms constrained optimization

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Accelerating fireworks algorithm with Weight-Based Guiding Sparks 10th

Accelerating Fireworks Algorithm with Weight-Based Guiding S...

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10th International Conference on Swarm Intelligence (ICSI)

作者： Li, Yuhao Yu, Jun Takagi, Hideyuki Tan, Ying Kyushu Univ Grad Sch Design Fukuoka 8158540 Japan Japan Soc Promot Sci Tokyo Japan Kyushu Univ Fac Design Fukuoka 8158540 Japan Peking Univ Sch Elect Engn & Comp Sci Beijing 100871 Peoples R China

ISBN: (纸本)9783030263683;9783030263690

We introduce two strategies into the guided fireworks algorithm (GFWA) to further improve its performance by generating one or more weight-based guiding spark individual(s) for each firework individual. The first strategy assigns different weights to spark individuals under each firework individual according to their fitness and then calculates one or more guiding vector(s) to guide the firework individual to evolve into potential directions. The second strategy decides the number of weight-based guiding spark individuals dynamically based on the evolution of a firework individual, i.e. if a firework individual does not evolve and survive in the next generation, then the second strategy reduces the number of spark individuals generated around the firework individual and generates the same reduced number of weight-based guiding spark individuals additionally. We design a controlled experiment to evaluate the performance of our proposal using CEC 2013 benchmark functions with five different dimensions. The experiment results confirm that the proposed strategies can provide effective guidance information to improve the GFWA performance significantly, and its acceleration effect for higher dimensional tasks is more obvious.

关键词： fireworks algorithm Meta-heuristic algorithm Weight-based guiding sparks Acceleration

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An Improved Patch Matching algorithm Based on fireworks algorithm 11

An Improved Patch Matching Algorithm Based on Fireworks Algo...

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11th International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR) - Pattern Recognition and Computer Vision

作者： Shi, Huili Zhong, Sheng Yan, Luxin Huazhong Univ Sci & Technol Wuhan Hubei Peoples R China

ISBN: (纸本)9781510636385

With the development of technology, especially the rapid development of hand-held devices, it is more convenient to obtain video sequences, but the video quality still suffers from some issues, such as unwanted camera shakes and jitter. To address the issues, video stabilization techniques have been developed to obtain high quality and stable videos. Considering computational complexity and real-time requirements, patch matching, has become an important method for motion estimation and video stabilization. It transforms the video stabilization task into a minimum optimization problem. In this paper, we propose a novel patch matching method integrated with fireworks algorithm([1]) for motion search, which is a novel swarm intelligence optimization algorithm. Inspired by the fireworks explode in the air, the established mathematical model can be formulated as a parallel explosive search method by introducing random factors and selection strategies, and thus developed into a global probability search method for solving the optimal solution of complex optimization problems. It has excellent performance and high efficiency in solving complex optimization problems. Experimental results show that the improved patch matching method based on fireworks algorithm has achieved better results, compared with the ones with traditional motion search algorithms.

关键词： Patch matching motion estimation fireworks algorithm three-step search

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Image Segmentation using Clustering with fireworks algorithm 11

Image Segmentation using Clustering with Fireworks Algorithm

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11th International Conference on Intelligent Systems and Control (ISCO)

作者： Misra, Priya Ranjan Si, Tapas Bankura Unnayani Inst Engn Dept Comp Sci & Engn Bankura W Bengal India

ISBN: (纸本)9781509027170

This paper presents a hard clustering technique using fireworks algorithm with adaptive transfer function (FWA-ATF) for image segmentation. The fireworks algorithm (FWA) is a recently developed new Swarm Intelligence (SI) algorithm for function optimization. This algorithm simulates the process of fireworks explosion in the night sky. The main characteristic of FWA is the good balance between exploration and exploitation during the search process. The exploitation is done using good fireworks whereas the bad fireworks are responsible for exploration. FWA shows its efficiency and effectiveness in numerical function optimization over other SI algorithm like particle swarm optimization (PSO). FWA-ATF is a modified version of basic FWA and in this work, it is used in hard clustering technique to segment the image. FWA-ATF is used to find the optimal cluster centroids corresponding to different regions in the image. The proposed clustering technique is applied to segment four benchmark images and the well-known cluster validity indexDunn's Index is used to measure the performance of the proposed clustering technique quantitatively. The performance of the proposed method is compared with clustering using K-means, PSO and basic FWA. The experimental results demonstrates that the proposed clustering technique with FWA-ATF performs better than other methods in segmentation for most of the images.

关键词： fireworks algorithm adaptive transfer function optimization clustering segmentation Dunn's Index

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Wireless Sensor Network Coverage Problem Using Modified fireworks algorithm 12

Wireless Sensor Network Coverage Problem Using Modified Fire...

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12th International Wireless Communications & Mobile Computing Conference (IWCMC)

作者： Tuba, Eva Tuba, Milan Simian, Dana Univ Belgrade Fac Math Belgrade Serbia John Naisbitt Univ Fac Comp Sci Belgrade Serbia Lucian Blaga Univ Fac Sci Sibiu Romania

ISBN: (纸本)9781509003044

Wireless sensor networks are emerging technology with increasing number of applications, and consequently an active research area. One of the problems pertinent to wireless sensor networks is the coverage problem with number of definitions, depending on the assumed conditions. In this paper we consider hard optimization area coverage problem with the goal of finding optimal sensor nodes positions that maximize probabilistic coverage of the area of interest. For such type of optimization problem swarm intelligence stochastic metaheuristics have been successfully used. In this paper we propose a modified enhanced fireworks algorithm for wireless sensor network coverage problem and compare it with other approaches from literature, where our algorithm proved to be very robust and better, considering all conducted tests.

关键词： Wireless sensor networks Coverage problem fireworks algorithm Swarm intelligence Metaheuristic optimization algorithms

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