检索结果-内蒙古大学图书馆

A novel nature-inspired algorithm for optimization: Squirrel search algorithm

SWARM AND EVOLUTIONARY COMPUTATION 2019年 44卷 148-175页

作者： Jain, Mohit Singh, Vijander Rani, Asha Univ Delhi Netaji Subhas Inst Technol Instrumentat & Control Engn Div Delhi 110078 India

This paper presents a novel nature-inspired optimization paradigm, named as squirrel search algorithm (SSA). This optimizer imitates the dynamic foraging behaviour of southern flying squirrels and their efficient way of locomotion known as gliding. Gliding is an effective mechanism used by small mammals for travelling long distances. The present work mathematically models this behaviour to realize the process of optimization. The efficiency of the proposed SSA is evaluated using statistical analysis, convergence rate analysis, Wilcoxon's test and ANOVA on classical as well as modern CEC 2014 benchmark functions. An extensive comparative study is carried out to exhibit the effectiveness of SSA over other well-known optimizers in terms of optimization accuracy and convergence rate. The proposed algorithm is implemented on a real-time Heat Flow Experiment to check its applicability and robustness. The results demonstrate that SSA provides more accurate solutions with high convergence rate as compared to other existing optimizers.

关键词： nature-inspired algorithm Unconstrained optimization Squirrel search algorithm

来源：评论

学校读者我要写书评

暂无评论

μ&x1d703;&xdf03;-EGF: A New Multi-Thread and nature-inspired algorithm for the Packing Problem

引用

MOBILE NETWORKS & APPLICATIONS 2020年第6期25卷 2105-2117页

作者： Martinez-Rios, Felix Marmolejo-Saucedo, Jose A. Garcia-Jacas, Cesar R. Murillo-Suarez, Alfonso Univ Panamer Fac Ingn Ciudad De Mexico Mexico CICESE Dept Ciencias Comp Ensenada Baja California Mexico

In this paper, the authors present a new algorithm efficient solution to the packing problem in two dimensions. The authors propose a new heuristic using the value of the electromagnetic field to determine the best position to place a circular object in a configuration of other circular objects previously packed. Also, this algorithm simulates two processes to compact objects already placed, inspired by gravitational forces, to minimize the empty space in the container and maximizing the number of objects in the container. To determine the efficacy of this algorithm, the authors carried out experiments with twenty-four instances. Parallel computing can contribute to making decision processes such as optimization and prediction more agile and faster. Real-time decision making involves the use of solution methodologies and algorithms. For this reason the present manuscript shows an alternative for the solution of a classic industry problem that must be solved quickly. Packaging optimization can help reduce waste of container material. The material used to transport the products can reduce its environmental impact due to an efficient packaging process. Light-weighting can also be accomplished by reducing the amount of packaging material used.

关键词： Packing problem Optimization nature-inspired algorithm Multi-threaded algorithms Gravitational algorithms Industry 4 0

来源：评论

学校读者我要写书评

暂无评论

A New nature-inspired algorithm for Load Balancing

A New Nature-inspired Algorithm for Load Balancing

引用

11th IEEE Singapore International Conference on Communication Systems

作者： Feng, Xiang Lau, Francis C. M. Shuai, Dianxun East China Univ Sci & Technol Dept Comp Sci Shanghai 200237 Peoples R China Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

ISBN: (纸本)9781424424238

The classical Load Balancing Problem (LBP) is to map tasks to processors so as to minimize the maximum load. Solving the LBP successfully would lead to better utilization of resources and better performance. The LBP has been proven to be NP-hard, thus generating the exact solutions in a tractable amount of time becomes infeasible when the problems become large. We present a new nature-inspired approximation algorithm based on the Particle Mechanics (PM) model to compute in parallel approximate efficient solutions for LBPs. Just like other nature-inspired algorithms (NAs) drawing from observations of physical processes that occur in nature, the PM algorithm is inspired by physical models of particle kinematics and dynamics. The PM algorithm maps the classical LBP to the movement of particles in a force field by a corresponding mathematical model in which all particles move according to certain defined rules until reaching a stable state. By anti-mapping the stable state, the solution to LBP can be obtained.

关键词： Load balancing approximation algorithm nature-inspired algorithm particle mechanics model distributed and parallel algorithm

来源：评论

学校读者我要写书评

暂无评论

Hybrid nature-inspired Optimization algorithm: Hydrozoan and Sea Turtle Foraging algorithms for Solving Continuous Optimization Problems

引用

IEEE ACCESS 2020年 8卷 65780-65800页

作者： Tansui, Daranat Thammano, Arit King Mongkuts Inst Technol Ladkrabang Fac Informat Technol Computat Intelligence Lab Bangkok 10520 Thailand

In this paper, we develop a hybrid optimization algorithm inspired by the reproduction processes of hydrozoans and the foraging behavior of sea turtles for solving continuous optimization problems. Our hybrid algorithm combines the exploration capability of the hydrozoan algorithm with the exploitation capability of the sea turtle foraging algorithm. Moreover, a new adaptive crossover operator was introduced and integrated into the hybrid algorithm to further enhance exploration capability. Our hybrid algorithm was evaluated and compared to the individual algorithms and 12 state-of-the-art algorithms. Results on 21 standard benchmark functions showed that our algorithm was very effective and was among the best of the group, specifically it converged faster than the individual algorithms on most functions and reached optimal or near-optimal results on all functions.

关键词： Hybrid algorithm hydrozoan nature-inspired algorithm optimization sea turtle

来源：评论

学校读者我要写书评

暂无评论

A New nature-inspired algorithm for Load Balancing

A New Nature-inspired Algorithm for Load Balancing

引用

The 11th IEEE International Conference on Communications Systems(第11届电子和电气工程师协会国际通信系统会议)(IEEE ICCS 2008)

作者： Xiang Feng Francis C.M.Lau Dianxun Shuai Department of Computer Science East China University of Science and Technology Shanghai China 200237 Department of Computer Science The University of Hong Kong Hong Kong Department of Computer Science East China University of Science and Technology Shanghai China 20023

The classical Load Balancing Problem (LBP) is to map tasks to processors so as to minimize the maximum load. Solving the LBP successfully would lead to better utilization of resources and better performance. The LBP has been proven to be NP-hard, thus generating the exact solutions in a tractable amount of time becomes infeasible when the problems become *** present a new nature-inspired approximation algorithm based on the Particle Mechanics (PM) model to compute in parallel approximate *** solutions for LBPs. Just like other nature-inspired algorithms (NAs) drawing from observations of physical processes that occur in nature, the PM algorithm is inspired by physical models of particle kinematics and dynamics. The PM algorithm maps the classical LBP to the movement of particles in a *** by a corresponding mathematical model in which all particles move according to certain *** rules until reaching a stable state. By anti-mapping the stable state, the solution to LBP can be obtained.

关键词： Load balancing approximation algorithm nature-inspired algorithm particle mechanics model distributed and parallel algorithm.

来源：评论

学校读者我要写书评

暂无评论

Multi objective elk herd optimization for efficient structural design

引用

SCIENTIFIC REPORTS 2025年第1期15卷 1-35页

作者： Patel, Pinank Adalja, Divya Mashru, Nikunj Jangir, Pradeep Arpita Jangid, Reena Gulothungan, G. Khishe, Mohammad Marwadi Univ Dept Mech Engn Rajkot 360003 India Marwadi Univ Dept Math Rajkot 360003 India Marwadi Univ Dept Mech Engn Rajkot 3630003 India Yuan Ze Univ Innovat Ctr Artificial Intelligence Applicat Taoyuan 320315 Taiwan Chandigarh Univ Univ Ctr Res & Dev Mohali 140413 India Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Biosci Chennai 602105 India Graph Era Hill Univ Dept CSE Dehra Dun 248002 India Graph Era Deemed Be Univ Dept CSE Dehra Dun 248002 Uttarakhand India Chitkara Univ Chitkara Univ Inst Engn & Technol Ctr Res Impact & Outcome Rajpura 140401 Punjab India SRM Inst Sci & Technol Dept Elect & Commun Engn Kattankulathur Chengalpattu 603203 Tamilnadu India Imam Khomeini Naval Sci Univ Nowshahr Dept Elect Engn Nowshahr Iran Jadara Univ Jadara Univ Res Ctr Irbid Jordan JJ Coll Engn & Technol Dept Elect & Elect Engn Tiruchirappalli Tamilnadu India

This research presents an advancement of the Elk Herd Optimization targeting specific real-world multi-objective optimization problems, this algorithm is stated as the multi-objective Elk Herd Optimization (MOEHO). MOEHO exploits reproductive behaviour among elk herds for balancing exploration and exploitation within the optimization procedure toward diversification and convergence. The algorithm performed better over the set of small-to-medium scale structural design problems thus is widely applicable in engineering design. Further, when compared with eight benchmark truss structures against five well-established algorithms the MOEHO has outperformed them in the perspective of performance parameters like Spacing (SP), Hypervolume (HV) and Inverted Generational Distance (IGD). More concrete statistical analysis through Friedman rank test also ascertains the robustness and efficiency of the algorithm, especially at high complexities in optimization. The research attracts attention to the ability of such an algorithm which maintains a balance between the exploration and exploitation. Computational efficiency of MOEHO and qualitatively diversifying solutions along Pareto front, makes it especially applicable in complex engineering applications. Further research into extension of MOEHO with applicability on more dimensional problems, applied even in energy systems optimization.

关键词： nature-inspired algorithm Multi-Objective optimization with structure Performance matrices Compliance

来源：评论

学校读者我要写书评

暂无评论

Galapagos giant tortoise mating algorithm: revolutionizing wireless charging trajectories and secure data transmission in sustainable power plants

引用

RESULTS IN ENGINEERING 2025年 26卷

作者： Gharaei, Niayesh Alabdali, Aliaa M. Almagrabi, Alaa Omran Hosseingholipourasl, Ali OSTIM Tech Univ Fac Engn Dept Comp Engn Ankara Turkiye King Abdulaziz Univ Fac Comp & Informat Technol Dept Informat Technol POB 344 Rabigh 21911 Saudi Arabia King Abdulaziz Univ Fac Comp & Informat Technol Dept Informat Syst Jeddah Saudi Arabia Atilim Univ Dept Elect & Elect Engn TR-06830 Ankara Turkiye

This paper presents the Galapagos Giant Tortoise Mating Optimization algorithm (GGTMOA), a novel nature-inspired metaheuristic developed to optimize the trajectory paths of Wireless Mobile Chargers (WMCs) and ensure secure data transmission in power plants. The algorithm addresses critical challenges such as energy-efficient charging, the spatial distribution of wireless sensor nodes, limited operational energy resources, dynamic trajectory planning, and data encryption for secure communication. inspired by the unique mating behaviors of galapagos giant tortoises, GGTMOA achieves a robust balance between exploration and exploitation through innovative initialization techniques, movement strategies, mating mechanisms, and selection processes. In this study, the proposed algorithm is first employed to optimize the trajectory paths of WMCs, addressing key challenges in energy-efficient charging and dynamic path planning. Following this, the algorithm integrates advanced encryption methods to ensure the secure transmission of data between sensor nodes and base stations, safeguarding sensitive information and enhancing the overall security of the system. This two-fold approach not only optimizes charging efficiency and reduces energy consumption but also fortifies data communication, making the system more robust and reliable in industrial environments. Simulation results demonstrate that GGTMOA outperforms existing metaheuristics by generating optimal trajectories that enhance charging efficiency, reduce energy consumption, ensure secure data communication, and satisfy plant-specific energy constraints. These findings establish GGTMOA as a powerful tool for sustainable energy management, wireless charging optimization, and secure data handling in industrial environments.

关键词： Galapagos giant tortoise Metaheuristic algorithm nature-inspired algorithm Energy Efficiency Power Plants

来源：评论

学校读者我要写书评

暂无评论

A novel nature-inspired meta-heuristic algorithm for optimization: bear smell search algorithm

引用

SOFT COMPUTING 2020年第17期24卷 13003-13035页

作者： Ghasemi-Marzbali, Ali Mazandaran Univ Sci & Technol Dept Comp & Elect Engn Babol Sar Iran

In the recent years, the optimization problems show that they are a big challenge for engineering regarding the fast growth of new nature-inspired optimization algorithms. Therefore, this paper presents a novel nature-inspired meta-heuristic algorithm for optimization which is called as bear smell search algorithm (BSSA) that takes into account the powerful global and local search operators. The proposed algorithm imitates both dynamic behaviors of bear based on sense of smell mechanism and the way bear moves in the search of food in thousand miles farther. Among all animals, bears have inconceivable sense of smell due to their huge olfactory bulbs that manage the sense of different odors. Since the olfactory bulb is a neural model of the vertebrate forebrain, it can make a strong exploration and exploitation for optimization. According to the odors value, bear moves the next location. Therefore, this paper mathematically models these structures. To demonstrate and evaluate the BSSA ability, numerous types of benchmark functions and four engineering problems are employed to compare the obtained results of BSSA with other available optimization methods with several analyzed indices such as pair-wise test, Wilcoxon rank and statistical analysis. The numerical results revealed that proposed BSSA presents competitive and greater results compared to other optimization algorithms.

关键词： nature-inspired algorithm Bear smell search algorithm Benchmark functions Meta-heuristic algorithm Bear's sense of smell

来源：评论

学校读者我要写书评

暂无评论

Artificial lizard search optimization (ALSO): a novel nature-inspired meta-heuristic algorithm

引用

SOFT COMPUTING 2021年第8期25卷 6179-6201页

作者： Kumar, Neetesh Singh, Navjot Vidyarthi, Deo Prakash Indian Inst Technol Roorkee Dept Comp Sci & Engn Roorkee 247667 Uttar Pradesh India Motilal Nehru Natl Inst Technol Allahabad Prayagraj 211004 India Jawaharlal Nehru Univ New Delhi 110067 India

Redheaded Agama lizards attack their prey in a well-organized manner. This work models the dynamic foraging behaviour of Agama lizards and their effective way of capturing prey into a mathematical model named as artificial lizard search optimization (ALSO) algorithm. The idea is based on a recent study in which the researchers reported that the lizards control the swing of their tails in a measured manner to redirect angular momentum from their bodies to their tails, stabilizing body attitude in the sagittal plane. A balanced lumping (between body and tail angles) plays a significant role in capturing the prey in a shot. In formulating the optimization problem, a swarm of lizard are considered that are hunting for the prey. To study the performance of the proposed ALSO, it has been simulated. A comparative study is done with some well-known nature-inspired optimization techniques on classical unimodal, multimodal and other benchmark functions. Further, the algorithm is also tested on an object detection application. The result proves the effectiveness of the proposed ALSO algorithm over other nature-inspired state of the art.

关键词： Soft computing Meta-heuristic Optimization techniques Agama lizard nature-inspired algorithm

来源：评论

学校读者我要写书评

暂无评论

Hazelnut tree search algorithm: a nature-inspired method for solving numerical and engineering problems

引用

ENGINEERING WITH COMPUTERS 2022年第SUPPL 4期38卷 3191-3215页

作者： Emami, Hojjat Univ Bonab Bonab Iran

In this paper, a novel nature-inspired optimization algorithm, hazelnut tree search (HST) is proposed for solving numerical and engineering optimization problems. HST is a multi-agent algorithm that simulates the search process for finding the best hazelnut tree in a forest. The algorithm is composed of three main actuators: growth, fruit scattering, and root spreading. In the growth phase, trees compete with each other on shared resources to grow up and improve their fitness. In the fruit scattering phase, HTS performs exploration by simulating the movement of hazelnuts around the forest with the help of animals and rodents. In the root spreading, HTS performs exploitation by modeling the root spreading mechanism of trees around themselves. The performance of the proposed algorithm is evaluated on multi-variable unconstraint numerical optimization benchmarks and constraint engineering problems. Comparing the proposed algorithm with a few other optimization algorithms shows the superiority of the HTS in terms of problem-solving success and finding the global optimum on most benchmark problems.

关键词： Evolution Numerical optimization nature-inspired algorithm Hazelnut tree search algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：