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physics-based algorithm to simulate tree dynamics under wind load

International Journal of Agricultural and Biological Engineering 2020年第2期13卷 26-32页

作者： Lifeng Xu Zhongzhu Yang Weilong Ding Gerhard Buck-Sorlin College of Computer Science&Technology Zhejiang University of TechnologyHangzhou 310026China IRHS INRAEAGROCAMPUS OUESTUniversitéd'AngersSFR 4207 QUASAV42 rue Georges Morel49071 BeaucouzécedexFrance

Rapid development in the different computer science fields during the recent decades has facilitated the creation of new applications in the area of dynamic simulation of plant *** these new applications,simulation of trees swaying in the wind is of great importance,as those computer graphics related areas,e.g.,computer games,tree cultivation and forest management simulations,help a lot in revealing the mechanisms of tree dynamics under wind ***,it is a big challenge to balance the effect of visualization in real time and calculation efficiency for any simulation algorithm.A physics-based algorithm to simulate tree dynamics under wind load was proposed in this study.A mechanistic model simulating the bending of a cantilever beam was used within the algorithm to simulate deformation of stems,and the algorithm was integrated with a landscape model in which different types of trees were constructed with an L-system-based *** results show that realistic dynamic effects can be achieved with reasonably high computational efficiency.

关键词： physics-based algorithm tree model L-system Cantilever beam

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Nuclear Reaction Optimization: A novel and powerful physics-based algorithm for global optimization

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IEEE ACCESS 2019年 7卷 66084-66109页

作者： Wei, Zhenglei Huang, Changqiang Wang, Xiaofei Han, Tong Li, Yintong Air Force Engn Univ Inst Aeronaut Engn Xian 710038 Shaanxi Peoples R China

Meta-heuristic algorithms have gained substantial popularity in recent decades and have focused on applications in a wide spectrum of fields. In this paper, a new and powerful physics-based algorithm named nuclear reaction optimization (NRO) is presented. Meanwhile, NRO imitates the nuclear reaction process and consists of two phases, namely, a nuclear fission (NFi) phase and a nuclear fusion (NFu) phase. The Gaussian walk and differential evolution operators between nucleus and neutron are employed for exploitation and appropriate exploration in the (NFi) phase, respectively. Meanwhile, the variants of differential evolution operator are utilized for exploration in the NFu phase, which consists of the ionization and fusion stages. Additionally, the variants of Levy flight are used for random searching to escape from the local optima in each stage of the NFu phase. The exploration and exploitation abilities of NRO can be balanced due to a combination of the two phases. Both constrained and unconstrained benchmark functions are employed for testing the performance of NRO. To make comparisons between NRO and the state-of-the-art algorithms, 23 classic benchmark functions and twenty-night modern benchmark functions are performed. Moreover, three engineering design optimization problems are solved as constrained benchmark functions by using NRO and the compared algorithms. The results illustrate that the proposed nuclear reaction optimization algorithm is a potential and powerful approach for global optimization.

关键词： Differential evolution operator engineering design optimization global optimization Levy flight strategy nuclear reaction optimization (NRO) physics-based algorithm

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A physics-based Metaheuristic algorithm based on Doppler Effect Phenomenon and Mean Euclidian Distance Threshold

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PERIODICA POLYTECHNICA-CIVIL ENGINEERING 2022年第3期66卷 820-842页

作者： Kaveh, Ali Hosseini, Seyed Milad Zaerreza, Ataollah Iran Univ Sci & Technol Sch Civil Engn Tehran *** Iran

Doppler Effect (DE) is a physical phenomenon observed by Doppler, an Austrian mathematician, in 1842. In recent years, the mathematical formulation of this phenomenon has been used to improve the frequency equation of the standard Bat algorithm (BA) developed by Yang in 2010. In this paper, we use the mathematical formulation of DE with some idealized rules to update the observer velocity existing in the Doppler equation. Thus, a new physics-based Metaheuristic (MH) optimizer is developed. In the proposed algorithm, the observers??? velocities as the algorithm???s search agents are updated based on the DE equation. A new mechanism named Mean Euclidian Distance Threshold (MEDT) is introduced to enhance the quality of the observers. The proposed MEDT mechanism is also employed to avoid the locally optimum solutions and increase the convergence rate of the presented optimizer. Since the proposed algorithm simultaneously utilizes the DE equation and MEDT mechanism, it is called the Doppler Effect Mean Euclidian Distance Threshold (DE-MEDT) metaheuristic algorithm. The proposed DE-MEDT algorithm???s efficiency is evaluated by solving well-known unconstrained and constrained optimization problems. In the unconstrained optimization problems, 23 wellknown optimization functions are used to assess the exploratory, exploitative, and convergence behaviors of the DE-MEDT algorithm.

关键词： Doppler effect optimization metaheuristics physics-based algorithm engineering design problems

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Optimizing plasma arc cutting processes using physics-based metaheuristic algorithms: a comparative analysis

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INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM 2024年 1-35页

作者： Pendokhare, Devendra Chakraborty, Shankar Jadavpur Univ Dept Prod Engn Kolkata West Bengal India Govt Polytech Dept Mech Engn Thana Maharashtra India

Plasma arc cutting (PAC) has become a flexible and effective method for precisely cutting complex profiles on various difficult-to-machine materials, including superalloys and composites, due to its many benefits, like higher dimensional accuracy, productivity and ability to cut thicker materials. Process optimization is important for obtaining high-quality cuts, reducing material wastage and increasing overall productivity. However, it is difficult to optimize this process because of involvement of many variables, intricate cutting mechanism, and interaction between the process parameters and responses. In this paper, applications of five newly developed physics-based metaheuristic algorithms, i.e. Archimedes optimization algorithm (AOA), atom search optimization (ASO), nuclear reaction optimization (NRO), electromagnetic field optimization (EFO) and gravitational search algorithm (GSA) are proposed for optimizing two PAC processes. Their optimization performance is compared in terms of computing effort, convergence time and solution quality. To find out the best parametric intermixes for resolving the multi-objective optimization problems, an effort is also put forward to develop the corresponding Pareto optimal fronts. For both the examples, compared to its competitors, EFO appears as the most effective metaheuristic for achieving the best combinations of the relevant process parameters. For the first example, EFO achieves 42.50, 35.85 and 25.55% improvements for single-objective optimization;and 35.71, 18.19 and 9% improvements for multi-objective optimization, in material removal rate, kerf taper and heat affected zone, respectively against the observations of the past researchers. In case of the second example, these improvements are 44.2, 26.42 and 17.12% for single-objective optimization;and 22.1, 10.36 and 6.21% for multi-objective optimization in surface roughness, kerf width and microhardness, respectively. With respect to average computation time, for mu

关键词： Plasma arc cutting Optimization Metaheuristics physics-based algorithm Parameter Response

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Atom search optimization: a systematic review of current variants and applications

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KNOWLEDGE AND INFORMATION SYSTEMS 2025年第6期67卷 4813-4914页

作者： Mugemanyi, Sylvere Qu, Zhaoyang Rugema, Francois Xavier Dong, Yunchang Wang, Lei Mutuyimana, Felicite Pacifique Mutabazi, Emmanuel Habumuremyi, Providence Mutabazi, Rita Clemence Muhirwa, Alexis Bananeza, Christophe Nshimiyimana, Arcade Kagaju, Clarisse Nsengumuremyi, Jean Rwanda Polytech Tumba Coll Dept Elect & Elect Engn POB 6638 Rulindo Rwanda Northeast Elect Power Univ Sch Elect Engn Jilin 132012 Peoples R China Jilin Engn Technol Res Ctr Intelligent Elect Power Jilin 132012 Peoples R China Univ Rwanda Coll Business & Econ Dept Business Informat Technol POB 4285 Kigali Rwanda Univ Quebec Chicoutimi Dept Sci Fondamentales Quebec City PQ G7H 2B1 Canada Univ Rwanda Coll Educ Dept Math Sci & Phys Educ POB 55 Rwamagana Rwanda Univ Rwanda Coll Educ African Ctr Excellence Innovat Teaching & Learning POB 55 Rwamagana Rwanda Hohai Univ Coll IoT Engn Changzhou 310000 Peoples R China Beijing Jiaotong Univ Sch Civil Engn Beijing 100044 Peoples R China Rwanda Polytech Tumba Coll Adm POB 6638 Rulindo Rwanda Hohai Univ Sch Energy & Elect Engn Nanjing 210098 Peoples R China Rwanda Polytech Tumba Coll Dept Gen Courses POB 6638 Rulindo Rwanda

Atom search optimization (ASO) is a renowned physics-based metaheuristic algorithm which takes inspiration from the fundamental theory of molecular dynamics and imitates the natural atomic movement characteristics in conformity with classical mechanics. ASO has demonstrated its preeminence over many well-known algorithms in solving real-world optimization problems and flexible implementation;hence, it has great recognition in academic communities. Due to this rising attraction, numerous modifications and improved variants of ASO have been designed and ASO finds applications in many fields including engineering, computer science, medicine, health sector, etc. Therefore, this study aims at reviewing and analyzing the ASO along with its variants. In this review, many reported studies based on ASO have been identified, collected, discussed and summed up. We have identified and selected 141 ASO-related papers published in recognized journals. The strengths as well as weaknesses of ASO have been discussed. The performance of ASO and its selected variants in convergence analysis was evaluated on 23 benchmark test functions with dimension 30. Furthermore, statistical analysis was performed to rank and validate the performance of ASO and its variants. The results reveal that QASO exhibits better overall effectiveness of 91.304% and ranks first in dimension 30 based on both Friedman and Kruskal-Wallis tests. The results also illustrate that the conventional ASO is the fastest algorithm amidst the compared algorithms in terms of computational time. In addition, the results show that the ESA-ASO achieves better results regarding the performance index and mean absolute error test. According to analysis of variance (ANOVA) test, Tukey HSD (honestly significant difference) post hoc test and Kruskal-Wallis test, the findings demonstrate that there are no statistically significant differences between ASO and its variants. Finally, the prospects for future trends are suggested.

关键词： Atom search optimization Swarm intelligence Metaheuristic physics-based algorithm Exploration Exploitation

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Chaos in metaheuristic based artificial intelligence algorithms: a short review

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2021年第3期29卷 1354-1367页

作者： Atali, Gokhan PehlIvan, Ihsan Gurevin, Bilal Seker, Halil Ibrahim Sakarya Univ Appl Sci Fac Technol Mechatron Engn Dept Sakarya Turkey Sakarya Univ Appl Sci Fac Technol Elect & Elect Engn Dept Sakarya Turkey Sakarya Univ Appl Sci Inst Grad Studies Mechatron Engn Dept Sakarya Turkey Sakarya Univ Appl Sci Inst Grad Studies Elect Elect Engn Dept Sakarya Turkey

Metaheuristic based artificial intelligence algorithms are commonly used in the solution of optimization problems. Another area-besides engineering systems-where chaos theory is widely employed is optimization problems. Being applied easily and not trapping in local optima, chaos-based search algorithms have attracted great attention. For example, it has been reported that when random number sequences generated from different chaotic systems are replaced with parameter values in bioinspired and swarm intelligence algorithms, an increase in the performance of metaheuristic algorithms is observed. Many scientific studies on developing hybrid algorithms in which metaheuristic algorithms and chaos theory are used together are already in process. In this article, scientific studies that cover the most popular metaheuristic algorithms in the literature in recent years and chaos theory subtopics together are examined. Great number of studies on metaheuristic algorithms and chaos issues exist in scientific literature. This article, hitherto, had to be limited to the most common meta-heuristic algorithms. In chaos-based metaheuristic algorithms, some advantages such as easy implementation, short application time and search acceleration have been addressed. This article is believed to contribute to two groups of researchers: The first group includes researchers already using meta-heuristic algorithms and who will come to understand that they can even improve their current techniques with chaos theory subarguments. The second group includes those who currently utilize chaotic analysis and methods in areas like nonlinear prediction modeling design and who will realize that they can make their existing methods even smarter with metaheuristic methods. Some metaheuristic algorithms use chaotic maps to solve problems such as trapping in local optimal solutions and premature convergence. In this study, such algorithms are examined through the benchmark functions they use. In additio

关键词： Chaos artificial intelligence metaheuristic algorithm chaotic map swarm bioinspired nature-inspired evolutionary algorithm physics-based algorithm benchmark function

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A multi-objective thermal exchange optimization model for solving optimal power flow problems in hybrid power systems

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Decision Analytics Journal 2023年 8卷

作者： Agrawal, Sunilkumar Pandya, Sundaram Jangir, Pradeep Kalita, Kanak Chakraborty, Shankar Government Engineering College Gujarat Gandhinagar India Department of Electrical Engineering Shri K.J. Polytechnic Bharuch India Rajasthan Rajya Vidyut Prasaran Nigam Ltd. Rajasthan Sikar India Department of Mechanical Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi India Department of Production Engineering Jadavpur University Kolkata India

This study addresses the challenges associated with optimal power flow (OPF) management in hybrid power systems incorporating diverse energy sources, particularly focusing on the unpredictability of renewable energy sources (RESs). A novel analytics approach is introduced using Multi-Objective Thermal Exchange Optimization (MOTEO). MOTEO is based on modeling energy transfer grounded on Newton's Law of Cooling. The model integrates innovative non-dominated sorting and crowing distance strategies to effectively solve the multi-objective optimization problem. The proposed hybrid OPF model incorporates four primary types of energy resources: thermal, wind, solar, and small-hydro, offering a holistic approach to power management in hybrid systems. Our model's practical applicability and efficiency are validated through rigorous testing on a modified IEEE 30-Bus system, benchmarked against other contemporary optimization methodologies. The results demonstrate that the MOTEO model successfully identifies optimal solutions for the multi-objective optimal power flow (MOOPF) problem while maintaining compliance with stringent power system constraints. This novel contribution enhances the field of analytics by providing a robust and efficient model to handle the complex problem of OPF in hybrid systems, thereby ensuring increased system reliability. © 2023 The Author(s)

关键词： Metaheuristics Multi-objective optimization Optimal power flow Pareto front physics-based algorithm Renewable energy resources

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physics-based search and optimization: Inspirations from nature

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EXPERT SYSTEMS 2016年第6期33卷 607-623页

作者： Siddique, Nazmul Adeli, Hojjat Univ Ulster Sch Comp & Intelligent Syst Coleraine Londonderry North Ireland Ohio State Univ Dept Biomed Engn Columbus OH 43210 USA Ohio State Univ Dept Biomed Informat Columbus OH 43210 USA Ohio State Univ Dept Civil Environm & Geodet Engn Columbus OH 43210 USA Ohio State Univ Dept Elect & Comp Engn Columbus OH 43210 USA Ohio State Univ Dept Neurosci Columbus OH 43210 USA Ohio State Univ Dept Neurol Columbus OH 43210 USA

This paper presents a review of recently developed physics-based search and optimization algorithms that have been inspired by natural phenomena. They include Big Bang-Big Crunch, black hole search, galaxy-based search, artificial physics optimization, electromagnetism optimization, charged system search, colliding bodies optimization, and particle collision algorithm.

关键词： Artificial physics optimization Big Bang-Big Crunch search black hole search charged system search colliding bodies optimization electromagnetism optimization galaxy-based search particle collision algorithm physics-based algorithm

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