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Journal articles on the topic 'Nelder-Mead optimization algorithm'

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Published: 7 June 2025

Last updated: 2 August 2025

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1

T. Nguyen, Son, Linh V. Trieu, Tu M. Pham, and Anh Hoang. "Improved Parameter Estimation of Three-Phase Squirrel-Cage Induction Motors Using the Nelder-Mead Simplex Algorithm." International journal of electrical and computer engineering systems 15, no. 8 (2024): 695–703. http://dx.doi.org/10.32985/ijeces.15.8.7.

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This work presents a technique for precisely determining the characteristics of a squirrel-cage three-phase induction motor using the Nelder-Mead simplex algorithm. This approach is a frequently employed numerical optimization technique for determining the minimal value of a multi-dimensional objective function. An advantageous feature of the Nelder-Mead simplex algorithm is its independence from the need to calculate partial derivatives of the objective function. Nevertheless, similar to several optimization techniques, the Nelder-Mead simplex approach can also exhibit sensitivity to the init

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Fajfar, Iztok, Janez Puhan, and Árpád Bűrmen. "Evolving a Nelder–Mead Algorithm for Optimization with Genetic Programming." Evolutionary Computation 25, no. 3 (2017): 351–73. http://dx.doi.org/10.1162/evco_a_00174.

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We used genetic programming to evolve a direct search optimization algorithm, similar to that of the standard downhill simplex optimization method proposed by Nelder and Mead ( 1965 ). In the training process, we used several ten-dimensional quadratic functions with randomly displaced parameters and different randomly generated starting simplices. The genetically obtained optimization algorithm showed overall better performance than the original Nelder–Mead method on a standard set of test functions. We observed that many parts of the genetically produced algorithm were seldom or never execute

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Benaouicha, Karim, Hamza Akroum, Abdelhakim Idir, and Yassine Bensafia. "Optimizing a FOPID controller for liquid level control in a three-tank system with the Nelder-Mead algorithm." STUDIES IN ENGINEERING AND EXACT SCIENCES 5, no. 2 (2024): e12198. https://doi.org/10.54021/seesv5n2-790.

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This paper proposes an optimization approach for a Fractional Order PID (FOPID) controller in a three-tank system using the Nelder-Mead algorithm. The FOPID controller is designed to control the liquid level in the three-tank system, and the Nelder-Mead algorithm is used to optimize the controller parameters for optimal control performance. The optimization problem is formulated to minimize the error between the desired and actual liquid levels, and the Nelder-Mead algorithm is used to search for the optimal values of the FOPID controller parameters. The results show that the optimized FOPID c

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Bilal, Muhammad, Shakoor Muhammad, Nekmat Ullah, Fazal Hanan та Subahan Ullah. "Optimum solutions of partial diﬀerential equation with initial condition using optimization techniques". VFAST Transactions on Mathematics 10, № 2 (2022): 118–36. http://dx.doi.org/10.21015/vtm.v10i2.1170.

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This paper proposes a new minimization technique for the solutions of partial diﬀerential equation with initial conditions. The proposed procedure is used to minimize the obtained solutions through any numerical technique. Forthe minimization process, Non-linear Nelder-Mead Simplex algorithm and genetic algorithm are used as optimization techniques. The designed partial diﬀerential equation has been calculated as an error function for the minimization process. Both Non-linear Nelder-Mead Simplex and genetic algorithm guarantees the minimization of nonlinear partial diﬀerential equation with in

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Lewis, Andrew, David Abramson, and Tom Peachey. "RSCS: A Parallel Simplex Algorithm for the Nimrod/O Optimization Toolset." Scientific Programming 14, no. 1 (2006): 1–11. http://dx.doi.org/10.1155/2006/906394.

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This paper describes a method of parallelisation of the popular Nelder-Mead simplex optimization algorithms that can lead to enhanced performance on parallel and distributed computing resources. A reducing set of simplex vertices are used to derive search directions generally closely aligned with the local gradient. When tested on a range of problems drawn from real-world applications in science and engineering, this reducing set concurrent simplex (RSCS) variant of the Nelder-Mead algorithm compared favourably with the original algorithm, and also with the inherently parallel multidirectional

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Selvam, Manjula, M. Ramachandran, and Vimala Saravanan. "Nelder–Mead Simplex Search Method - A Study." Data Analytics and Artificial Intelligence 2, no. 2 (2022): 117–22. http://dx.doi.org/10.46632/daai/2/2/7.

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Nelder-Mead in n dimensions facilitates the set of n + 1 test points. It finds a new test point, Makes one of the old test points new, and so the technique progresses into objective behavior process is measured at each test point. The Nelder-Mead Simplex system uses Simplex to find the minimum space. The algorithm operates using a design framework with n + 1 points (called simplex), Where n is for simplex based operation Number of input dimensions. The Nelder-Mead method is one of the most popular non-derivative methods, using only the values of f to search. Only in the simplex formation of n

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Boukhari, Noureddine, Fatima Debbat, Nicolas Monmarché, and Mohamed Slimane. "An Efficient Hybrid Evolution Strategy Algorithm with Direct Search Method for Global Optimization." International Journal of Organizational and Collective Intelligence 9, no. 3 (2019): 63–78. http://dx.doi.org/10.4018/ijoci.2019070104.

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The main purpose of this article is to demonstrate how evolution strategy optimizers can be improved by incorporating an efficient hybridization scheme with restart strategy in order to jump out of local solution regions. The authors propose a hybrid (μ, λ)ES-NM algorithm based on the Nelder-Mead (NM) simplex search method and evolution strategy algorithm (ES) for unconstrained optimization. At first, a modified NM, called Adaptive Nelder-Mead (ANM) is used that exhibits better properties than standard NM and self-adaptive evolution strategy algorithm is applied for better performance, in addi

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Pošík, Petr, and Waltraud Huyer. "Restarted Local Search Algorithms for Continuous Black Box Optimization." Evolutionary Computation 20, no. 4 (2012): 575–607. http://dx.doi.org/10.1162/evco_a_00087.

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Several local search algorithms for real-valued domains (axis parallel line search, Nelder-Mead simplex search, Rosenbrock's algorithm, quasi-Newton method, NEWUOA, and VXQR) are described and thoroughly compared in this article, embedding them in a multi-start method. Their comparison aims (1) to help the researchers from the evolutionary community to choose the right opponent for their algorithm (to choose an opponent that would constitute a hard-to-beat baseline algorithm), (2) to describe individual features of these algorithms and show how they influence the algorithm on different problem

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Panagant, Natee, Mustafa Yıldız, Nantiwat Pholdee, Ali Rıza Yıldız, Sujin Bureerat, and Sadiq M. Sait. "A novel hybrid marine predators-Nelder-Mead optimization algorithm for the optimal design of engineering problems." Materials Testing 63, no. 5 (2021): 453–57. http://dx.doi.org/10.1515/mt-2020-0077.

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Abstract The marine predators optimization algorithm (MPA) is a recently developed nature-inspired algorithm. In this paper, the Nelder-Mead algorithm is utilized to improve the local exploitation powers of the MPA when described as a hybrid marine predators and Nelder-Mead (HMPANM). Due to the harsh competitive conditions as well as the transition to new vehicles such as hybrid and full-electrical cars, the interest in the design of light and low-cost vehicles is increasing. In this study, a recent metaheuristic addition, a hybrid marine predators optimization algorithm, is used to solve a st

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Li, Wei, Haonan Luo, Lei Wang, Qiaoyong Jiang, and Qingzheng Xu. "Enhanced Brain Storm Optimization Algorithm Based on Modified Nelder–Mead and Elite Learning Mechanism." Mathematics 10, no. 8 (2022): 1303. http://dx.doi.org/10.3390/math10081303.

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Brain storm optimization algorithm (BSO) is a popular swarm intelligence algorithm. A significant part of BSO is to divide the population into different clusters with the clustering strategy, and the blind disturbance operator is used to generate offspring. However, this mechanism is easy to lead to premature convergence due to lacking effective direction information. In this paper, an enhanced BSO algorithm based on modified Nelder–Mead and elite learning mechanism (BSONME) is proposed to improve the performance of BSO. In the proposed BSONEM algorithm, the modified Nelder–Mead method is used

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Atri, Roozbeh, J. Marquez, Connie Leung, et al. "Smart Data-Driven Optimization of Powered Prosthetic Ankles Using Surface Electromyography." Sensors 18, no. 8 (2018): 2705. http://dx.doi.org/10.3390/s18082705.

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The advent of powered prosthetic ankles provided more balance and optimal energy expenditure to lower amputee gait. However, these types of systems require an extensive setup where the parameters of the ankle, such as the amount of positive power and the stiffness of the ankle, need to be setup. Currently, calibrations are performed by experts, who base the inputs on subjective observations and experience. In this study, a novel evidence-based tuning method was presented using multi-channel electromyogram data from the residual limb, and a model for muscle activity was built. Tuning using this

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Pholdee, Nantiwat, Vivek K. Patel, Sadiq M. Sait, Sujin Bureerat, and Ali Rıza Yıldız. "Hybrid spotted hyena–Nelder-Mead optimization algorithm for selection of optimal machining parameters in grinding operations." Materials Testing 63, no. 3 (2021): 293–98. http://dx.doi.org/10.1515/mt-2020-0043.

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Abstract In this research, a novel optimization algorithm, which is a hybrid spotted hyena-Nelder-Mead optimization algorithm (HSHO-NM) algorithm, has been introduced in solving grinding optimization problems. A well-known grinding optimization problem is solved to prove the superiority of the HSHO-NM over other algorithms. The results of the HSHO-NM are compared with others. The results show that HSHO-NM is an efficient optimization approach for obtaining the optimal manufacturing variables in grinding operations.

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Haitan, О., and О. Nazarov. "HYBRID APPROACH TO SOLVING OF THE AUTOMATED TIMETABLING PROBLEM IN HIGHER EDUCATIONAL INSTITUTION." Системи управління, навігації та зв’язку. Збірник наукових праць 2, no. 60 (2020): 60–69. http://dx.doi.org/10.26906/sunz.2020.2.060.

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The paper describes a hybrid approach to solving of the automated timetabling problem in higher educational institution based on the ant colony optimization, the genetic algorithm, and the Nelder–Mead method. The ant colony method is the basis of this algorithm, which forms the initial population for the genetic algorithm. The combination of this method with the genetic algorithm and the Nelder–Mead method reduces time of the convergence of an algorithm and eliminates the strong dependence of the results on the initial search parameters, which usually are selected experimentally. The Nelder–Me

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Audet, Charles, and Christophe Tribes. "Mesh-based Nelder–Mead algorithm for inequality constrained optimization." Computational Optimization and Applications 71, no. 2 (2018): 331–52. http://dx.doi.org/10.1007/s10589-018-0016-0.

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Mehta, Pranav, Betül S. Yildiz, Sumit Kumar, et al. "A Nelder Mead-infused INFO algorithm for optimization of mechanical design problems." Materials Testing 64, no. 8 (2022): 1172–82. http://dx.doi.org/10.1515/mt-2022-0119.

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Abstract Nature-inspired metaheuristic algorithms have wide applications that have greater emphasis over the classical optimization techniques. The INFO algorithm is developed on the basis of the weighted mean of the vectors, which enhances the superior vector position that enables to get the global optimal solution. Moreover, it evaluates the fitness function within the updating stage, vectors combining, and local search stage. Accordingly, in the present article, a population-based algorithm named weighted mean of vectors (INFO) is hybridized with the Nelder–Mead algorithm (HINFO-NM) and ada

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Guo, Jianhua, Weilun Liu, Liang Chu, and Jingyuan Zhao. "Fractional-Order Modeling and Parameter Identification for Ultracapacitors with a New Hybrid SOA Method." Energies 12, no. 22 (2019): 4251. http://dx.doi.org/10.3390/en12224251.

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This paper deals with an ultracapacitor (UC) model and its identification procedure. To take UC’s fractional characteristic into account, two constant phase elements (CPEs) are used to construct a model structure according to impedance spectrum analysis. The different behaviors of UC such as capacitance, resistance, and charge distribution dynamics are simulated by the corresponding part in the model. The resistance under different voltages is calculated through the voltage rebound method to explore its non-linear characteristics and create a look-up table. A nonlinear fractional model around

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17

Yas, M. H., M. Shakeri, and N. Khaksar. "Stacking sequence optimization of a composite cylindrical shell for vibrations by hybrid continuous optimization." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 222, no. 12 (2008): 2385–94. http://dx.doi.org/10.1243/09544062jmes900.

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In this paper, stacking sequence optimization of a laminated cylindrical shell for obtaining maximum natural frequency has been studied. The anisotropic cylindrical shell has finite length with simply supported conditions at both ends. Three-dimensional elasticity approaches are used to obtain the objective function. To perform optimization, a hybrid method, combining Tabu search (TS) and simplex search (SS) is used. TS allows the solution space to be widely covered. To stimulate the search towards solutions far from the current one, and to avoid the risk of being trapped into a local maximum,

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Vala, Jiří, and Petra Jarošová. "On a Derivative-free Optimization Approach to Some Problems of Civil Engineering." WSEAS TRANSACTIONS ON MATHEMATICS 22 (September 26, 2023): 641–51. http://dx.doi.org/10.37394/23206.2023.22.71.

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Development of advanced materials and structures for civil engineering, due to the requirements of green and sustainable building, including the reduction of energy consumption and the balance between occupant comfort and environmental friendliness, needs proper analysis of related physical, chemical, etc. processes, whose mathematical description leads to direct, sensitivity and inverse initial and boundary value problems for nonlinear partial differential equations, analysed numerically using finite element, difference and similar techniques. Design optimization requires to implement a set o

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Santhosh, M., and P. Sudhakar. "Nelder Mead with Grasshopper Optimization Algorithm for Node Localization in Wireless Sensor Networks." Journal of Computational and Theoretical Nanoscience 17, no. 12 (2020): 5409–21. http://dx.doi.org/10.1166/jctn.2020.9434.

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Node localization in wireless sensor network (WSN) becomes essential to calculate the coordinate points of the unknown nodes with the use of known or anchor nodes. The efficiency of the WSN has significant impact on localization accuracy. Node localization can be considered as an optimization problem and bioinspired algorithms finds useful to solve it. This paper introduces a novel Nelder Mead with Grasshopper Optimization Algorithm (NMGOA) for node localization in WSN. The Nelder-Mead simplex search method is employed to improve the effectiveness of GOA because of its capability of faster con

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Tang, Yundong, Hang Su, Rodolfo C. C. Flesch, and Tao Jin. "An optimization method for magnetic hyperthermia considering Nelder-Mead algorithm." Journal of Magnetism and Magnetic Materials 545 (March 2022): 168730. http://dx.doi.org/10.1016/j.jmmm.2021.168730.

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Gao, Zhenxiao, Tianyuan Xiao, and Wenhui Fan. "Hybrid differential evolution and Nelder–Mead algorithm with re-optimization." Soft Computing 15, no. 3 (2010): 581–94. http://dx.doi.org/10.1007/s00500-010-0566-2.

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Liu, An, and Ming-Ta Yang. "A New Hybrid Nelder-Mead Particle Swarm Optimization for Coordination Optimization of Directional Overcurrent Relays." Mathematical Problems in Engineering 2012 (2012): 1–18. http://dx.doi.org/10.1155/2012/456047.

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Coordination optimization of directional overcurrent relays (DOCRs) is an important part of an efficient distribution system. This optimization problem involves obtaining the time dial setting (TDS) and pickup current (Ip) values of each DOCR. The optimal results should have the shortest primary relay operating time for all fault lines. Recently, the particle swarm optimization (PSO) algorithm has been considered an effective tool for linear/nonlinear optimization problems with application in the protection and coordination of power systems. With a limited runtime period, the conventional PSO

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Ahandani, Morteza Alinia, and Hosein Alavi-Rad. "Hybridizing Shuffled Frog Leaping and Shuffled Complex Evolution Algorithms Using Local Search Methods." International Journal of Applied Evolutionary Computation 5, no. 1 (2014): 30–51. http://dx.doi.org/10.4018/ijaec.2014010103.

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In this research, a study was carried out to exploit the hybrid schemes combining two classical local search techniques i.e. Nelder–Mead simplex search method and bidirectional random optimization with two meta-heuristic methods i.e. the shuffled frog leaping and the shuffled complex evolution, respectively. In this hybrid methodology, each subset of meta-heuristic algorithms is improved by a hybrid strategy that is combined from evolutionary process of each subset in related algorithm and a local search method. These hybrid algorithms are evaluated on low and high dimensional continuous bench

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Tymkiv, Pavlo, Roman Tkachuk, and Oleksiy Yanenko. "OPTIMIZATION METHODS FOR PARAMETER IDENTIFICATION MODEL OF TEST ELECTRORETINOSIGNAL TO ASSESS NEUROTOXICITY RISKS." Bulletin of Kyiv Polytechnic Institute. Series Instrument Making, no. 68(2) (December 26, 2024): 80–86. https://doi.org/10.20535/1970.68(2).2024.318208.

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Introduction. The development of advanced optimization methods plays a crucial role in the enhancement of diagnostic tools in the biomedical field, particularly in the analysis of complex physiological signals. Electroretinography (ERG) is a widely used diagnostic technique that records electrical responses generated by the retina in response to light stimuli, providing valuable insights into the functional health of retinal cells. ERG is instrumental in diagnosing conditions such as retinitis pigmentosa, diabetic retinopathy, and neurotoxicity. However, the analysis of low-intensity electrore

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CHOUAIBI, DHOUHA, and WASSILA CHAGRA. "A NEW HYBRID OPTIMIZATION METHOD USED IN PREDICTIVE CONTROL OF A NONLINEAR FRACTIONAL MODEL BASED ON FRACTIONAL HAMMERSTEIN STRUCTURE." REVUE ROUMAINE DES SCIENCES TECHNIQUES — SÉRIE ÉLECTROTECHNIQUE ET ÉNERGÉTIQUE 69, no. 4 (2024): 437–48. http://dx.doi.org/10.59277/rrst-ee.2024.69.4.12.

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Fractional Hammerstein models represent various nonlinear processes, such as thermal and mechanical. Their major drawback is the non-convex optimization problem in a nonlinear model predictive control scheme due to its static nonlinearity. Indeed, an efficient optimization algorithm is needed. This work proposes a hybrid optimization algorithm combining the Nelder Mead optimization method and the Honey Badger one to synthesize a predictive control algorithm based on fractional Hammerstein models. As illustrated through simulation results, the proposed method offers clear improvements in conver

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Bazargan-Lari, Yousef. "Design and shape optimization of MR brakes using Nelder–Mead optimization algorithm." Mechanics & Industry 20, no. 6 (2019): 602. http://dx.doi.org/10.1051/meca/2019017.

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Magnetorheological (MR) brakes have attracted many attentions for controlling mechanical systems such as robots, e-bicycles, and haptic devices. A large number of researchers have delved into enhancing MR brake effectiveness. Herein, a new MR brake is proposed in which the braking torque is improved and the configuration is simplified. Numerical simulations were based on finite element method (FEM) was employed to achieve the brake model. In order to verify the obtained results, they were compared with the available ones in the literature and they have a good agreement with each other. Then, t

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Zandavi, Seid Miad, Vera Chung, and Ali Anaissi. "Multi-user Remote Lab: Timetable Scheduling Using Simplex Nondominated Sorting Genetic Algorithm." ACM/IMS Transactions on Data Science 2, no. 2 (2021): 1–13. http://dx.doi.org/10.1145/3437260.

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The scheduling of multi-user remote laboratories is modeled as a multimodal function for the proposed optimization algorithm. The hybrid optimization algorithm, hybridization of the Nelder-Mead Simplex algorithm, and Non-dominated Sorting Genetic Algorithm (NSGA), named Simplex Non-dominated Sorting Genetic Algorithm (SNSGA), is proposed to optimize the timetable problem for the remote laboratories to coordinate shared access. The proposed algorithm utilizes the Simplex algorithm in terms of exploration and NSGA for sorting local optimum points with consideration of potential areas. SNSGA is a

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Majhi, Neha, and Rajashree Mishra. "A novel hybrid genetic algorithm and Nelder-Mead approach and it’s application for parameter estimation." F1000Research 13 (September 19, 2024): 1073. http://dx.doi.org/10.12688/f1000research.154598.1.

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Background Traditional optimization methods often struggle to balance global exploration and local refinement, particularly in complex real-world problems. To address this challenge, we introduce a novel hybrid optimization strategy that integrates the Nelder-Mead (NM) technique and the Genetic Algorithm (GA), named the Genetic and Nelder-Mead Algorithm (GANMA). This hybrid approach aims to enhance performance across various benchmark functions and parameter estimation tasks. Methods GANMA combines the global search capabilities of GA with the local refinement strength of NM. It is first teste

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Majhi, Neha, and Rajashree Mishra. "A novel hybrid genetic algorithm and Nelder-Mead approach and it’s application for parameter estimation." F1000Research 13 (April 7, 2025): 1073. https://doi.org/10.12688/f1000research.154598.3.

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Background Traditional optimization methods often struggle to balance global exploration and local refinement, particularly in complex real-world problems. To address this challenge, we introduce a novel hybrid optimization strategy that integrates the Nelder-Mead (NM) technique and the Genetic Algorithm (GA), named the Genetic and Nelder-Mead Algorithm (GANMA). This hybrid approach aims to enhance performance across various benchmark functions and parameter estimation tasks. Methods GANMA combines the global search capabilities of GA with the local refinement strength of NM. It is first teste

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Majhi, Neha, and Rajashree Mishra. "A novel hybrid genetic algorithm and Nelder-Mead approach and it’s application for parameter estimation." F1000Research 13 (March 10, 2025): 1073. https://doi.org/10.12688/f1000research.154598.2.

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Background Traditional optimization methods often struggle to balance global exploration and local refinement, particularly in complex real-world problems. To address this challenge, we introduce a novel hybrid optimization strategy that integrates the Nelder-Mead (NM) technique and the Genetic Algorithm (GA), named the Genetic and Nelder-Mead Algorithm (GANMA). This hybrid approach aims to enhance performance across various benchmark functions and parameter estimation tasks. Methods GANMA combines the global search capabilities of GA with the local refinement strength of NM. It is first teste

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Elhashash, Mahmoud. "An efficient hybrid simplex bat algorithm (HSBA) for solving the general nonlinear global optimization problem." International Journal of Contemporary Mathematical Sciences 20, no. 1 (2025): 73–87. https://doi.org/10.12988/ijcms.2025.92003.

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In this paper, we propose an efficient hybrid meta-heuristic algorithm combining the Nelder–Mead (NM) simplex search method with the Bat Algorithm (BA), termed as the Hybrid Simplex-Bat Algorithm (HSBA). The proposed method aims to leverage the local search strength of NM and the global exploration ability of BA to solve complex nonlinear global optimization problems. HSBA is tested on a suite of standard benchmark functions and compared with existing meta-heuristic and hybrid algorithms. Experimental results demonstrate that HSBA outperforms the standalone BA and many other existing hybrid ap

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Khoja, Intissar, Taoufik Ladhari, Faouzi M’sahli, and Anis Sakly. "Cuckoo Search Approach for Parameter Identification of an Activated Sludge Process." Computational Intelligence and Neuroscience 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/3476851.

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A parameter identification problem for a hybrid model is presented. The latter describes the operation of an activated sludge process used for waste water treatment. Parameter identification problem can be considered as an optimization one by minimizing the error between simulation and experimental data. One of the new and promising metaheuristic methods for solving similar mathematical problem is Cuckoo Search Algorithm. It is inspired by the parasitic brood behavior of cuckoo species. To confirm the effectiveness and the efficiency of the proposed algorithm, simulation results will be compar

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Kheireddine, Bourahla, Belli Zoubida, and Hacib Tarik. "Improvements of bat algorithm using crossover technique and hybridization with Nelder-Mead simplex method." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 38, no. 3 (2019): 977–89. http://dx.doi.org/10.1108/compel-09-2018-0374.

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Purpose This study aims to improve the bat algorithm (BA) performance for solving optimization problems in electrical engineering. Design/methodology/approach For this task, two strategies were investigated. The first one is based on including the crossover technique into classical BA, in the same manner as in the genetic algorithm method. Therefore, the newly generated version of BA is called the crossover–bat algorithm (C-BA). In the second strategy, a hybridization of the BA with the Nelder–Mead (NM) simplex method was performed; it gives the NM-BA algorithm. Findings First, the proposed st

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Gürses, Dildar, Nantiwat Pholdee, Sujin Bureerat, Sadiq M. Sait, and Ali Rıza Yıldız. "A novel hybrid water wave optimization algorithm for solving complex constrained engineering problems." Materials Testing 63, no. 6 (2021): 560–64. http://dx.doi.org/10.1515/mt-2020-0093.

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Abstract In this work, a new hybrid optimization algorithm (HWW-NM), which combines the Nelder-Mead local search algorithm with the water wave algorithm, is introduced to solve real-world engineering optimization problems. This paper is one of the first studies in which both the water wave algorithm and the HWW-NM are applied to processing parameters optimization for manufacturing processes. HWW-NM performance is measured using the cantilever beam problem. Additionally, a problem for milling manufacturing optimization is posed and solved to evaluate HWW-NM performance in real-world application

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Mohsin, Armaghan, Yazan Alsmadi, Ali Arshad Uppal, and Sardar Muhammad Gulfam. "A modified simplex based direct search optimization algorithm for adaptive transversal FIR filters." Science Progress 104, no. 2 (2021): 003685042110254. http://dx.doi.org/10.1177/00368504211025409.

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In this paper, a novel modified optimization algorithm is presented, which combines Nelder-Mead (NM) method with a gradient-based approach. The well-known Nelder Mead optimization technique is widely used but it suffers from convergence issues in higher dimensional complex problems. Unlike the NM, in this proposed technique we have focused on two issues of the NM approach, one is shape of the simplex which is reshaped at each iteration according to the objective function, so we used a fixed shape of the simplex and we regenerate the simplex at each iteration and the second issue is related to

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Arkananta, Muhammad Hafizh, and Wikky Fawwaz Al Maki. "Boosting Performance of SVM in Koi Classification Using Direct Methods-Based Optimization." JOIV : International Journal on Informatics Visualization 8, no. 4 (2024): 2354. https://doi.org/10.62527/joiv.8.4.2474.

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Many koi fish enthusiasts keep or buy them just for their attractive colors without knowing what type of koi fish they are. The manual classification of koi fish species is still frequently incorrect. As a result, it is critical to apply a machine learning technique to identify various koi fish species. This research implemented a computer vision algorithm to classify koi fish species using the Support Vector Machine (SVM) as the classifier. However, the maximum accuracy SVM can achieve in our koi fish classification system is 79%. To achieve better performance, the SVM was optimized by applyi

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Liu, Xinran, Zhongju Wang, Long Wang, Chao Huang, and Xiong Luo. "A Hybrid Rao-NM Algorithm for Image Template Matching." Entropy 23, no. 6 (2021): 678. http://dx.doi.org/10.3390/e23060678.

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This paper proposes a hybrid Rao-Nelder–Mead (Rao-NM) algorithm for image template matching is proposed. The developed algorithm incorporates the Rao-1 algorithm and NM algorithm serially. Thus, the powerful global search capability of the Rao-1 algorithm and local search capability of NM algorithm is fully exploited. It can quickly and accurately search for the high-quality optimal solution on the basis of ensuring global convergence. The computing time is highly reduced, while the matching accuracy is significantly improved. Four commonly applied optimization problems and three image dataset

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Rosić, Maja B., Mirjana I. Simić, and Predrag V. Pejović. "Passive Target Localization Problem Based on Improved Hybrid Adaptive Differential Evolution and Nelder-Mead Algorithm." Journal of Sensors 2020 (February 27, 2020): 1–20. http://dx.doi.org/10.1155/2020/3482463.

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This paper considers a passive target localization problem in Wireless Sensor Networks (WSNs) using the noisy time of arrival (TOA) measurements, obtained from multiple receivers and a single transmitter. The objective function is formulated as a maximum likelihood (ML) estimation problem under the Gaussian noise assumption. Consequently, the objective function of the ML estimator is a highly nonlinear and nonconvex function, where conventional optimization methods are not suitable for this type of problem. Hence, an improved algorithm based on the hybridization of an adaptive differential evo

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Rohman, Miftahur, and Farid Baskoro. "The effect of modulation and pathloss coefficient on Estx optimum and ESB minimum wireless sensor networks." E3S Web of Conferences 513 (2024): 02002. http://dx.doi.org/10.1051/e3sconf/202451302002.

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Hardware devices in the Wireless Sensor Network (WSN) have several limitations, one of which is in terms of energy. Energy is needed so that activities running on the WSN keep running. WSN consists of many sensor nodes. Each sensor node has limited energy. In this study, the energy efficiency and bandwidth of the WSN were carried out using the Nelder Mead Simplex method, which uses the principle of differentiation of derivatives to simple forms. Nelder Mead Simplex has been widely used to manage energy efficiency in WSNs. The energy efficiency of this WSN is enhanced by the addition of the PSM

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Dong, Jiaqi, Qing-Hua Qin, and Yi Xiao. "Nelder–Mead Optimization of Elastic Metamaterials via Machine-Learning-Aided Surrogate Modeling." International Journal of Applied Mechanics 12, no. 01 (2020): 2050011. http://dx.doi.org/10.1142/s1758825120500118.

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One of the fundamental challenges of structural optimization of elastic metamaterials (EMMs) with complex geometry lies within the high consumption of computational power associated with finite element analysis (FEA) simulations, which often render the numerical solution an ineffective and costly attempt. Additionally, due to the inherent mesh dependence of the FEA method, minuscule geometry features, which often arise during optimization, demand very fine elements, resulting in enormously high time consumption, especially when repetitive solutions are needed for objective function validations

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Sushchenko, Olha, and Olexander Saluyk. "H2/Hinf Optimization of System for Stabilization of Moving Vehicles Equipment Using Two Types of Penalty Functions." Electronics and Control Systems 2, no. 76 (2023): 61–67. http://dx.doi.org/10.18372/1990-5548.76.17669.

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The article deals with features of H2/Hinf optimization of the stabilization system using two types of penalty function directed on provision of both the system’s stability and operating characteristics given to the system. Researched systems are assigned for stabilization of equipment operated on moving vehicles. The novelty of the research is introducing a new type of penalty function. The expressions for basic operating requirements are represented. The choice of the optimization algorithms is grounded including the Nelder–Mead method and genetic algorithm. The features of the genetic algor

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Mahmoud, K. R. "Central Force Optimization: Nelder-Mead Hybrid Algorithm for Rectangular Microstrip Antenna Design." Electromagnetics 31, no. 8 (2011): 578–92. http://dx.doi.org/10.1080/02726343.2011.621110.

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Zhu, Cheng, Yong Zhang, Xuhua Pan, Qi Chen, and Qingyu Fu. "Improved Harris Hawks Optimization algorithm based on quantum correction and Nelder-Mead simplex method." Mathematical Biosciences and Engineering 19, no. 8 (2022): 7606–48. http://dx.doi.org/10.3934/mbe.2022358.

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<abstract> <p>Harris Hawks Optimization (HHO) algorithm is a kind of intelligent algorithm that simulates the predation behavior of hawks. It suffers several shortcomings, such as low calculation accuracy, easy to fall into local optima and difficult to balance exploration and exploitation. In view of the above problems, this paper proposes an improved HHO algorithm named as QC-HHO. Firstly, the initial population is generated by Hénon Chaotic Map to enhance the randomness and ergodicity. Secondly, the quantum correction mechanism is introduced in the local search phase to improve

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Zitouni, Farouq, and Saad Harous. "Integrating the Opposition Nelder–Mead Algorithm into the Selection Phase of the Genetic Algorithm for Enhanced Optimization." Applied System Innovation 6, no. 5 (2023): 80. http://dx.doi.org/10.3390/asi6050080.

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In this paper, we propose a novel methodology that combines the opposition Nelder–Mead algorithm and the selection phase of the genetic algorithm. This integration aims to enhance the performance of the overall algorithm. To evaluate the effectiveness of our methodology, we conducted a comprehensive comparative study involving 11 state-of-the-art algorithms renowned for their exceptional performance in the 2022 IEEE Congress on Evolutionary Computation (CEC 2022). Following rigorous analysis, which included a Friedman test and subsequent Dunn’s post hoc test, our algorithm demonstrated outstan

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Koulianos, Athanasios, Antonios Litke, and Nikolaos K. Papadakis. "A Hybrid Whale Optimization Approach for Fast-Convergence Global Optimization." Journal of Experimental and Theoretical Analyses 3, no. 2 (2025): 17. https://doi.org/10.3390/jeta3020017.

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In this paper, we introduce the Levy Flight-enhanced Whale Optimization Algorithm with Tabu Search elements (LWOATS), an innovative hybrid optimization approach that enhances the standard Whale Optimization Algorithm (WOA) with advanced local search techniques and elite solution management to improve performance on global optimization problems. Techniques from the Tabu Search algorithm are adopted to balance the exploration and exploitation phases, while an elite reintroduction strategy is implemented to retain and refine the best solutions. The efficient optimization of LWOATS is further aide

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Zulkarnain, Norsyahidah, Muhammad Salihi Abdul Hadi, Nurul Farahain Mohammad, and Ibrahim Shogar. "Fitting Time-varying Coefficients SEIRD Model to COVID-19 Cases in Malaysia." International Journal of Innovative Computing 13, no. 1 (2023): 59–68. http://dx.doi.org/10.11113/ijic.v13n1.397.

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This paper proposes a compartmental Susceptible-Exposed-Infected-Recovered-Death (SEIRD) model for COVID-19 cases in Malaysia. This extended model is more relevant to describe the disease transmission than the SIRD model since the exposed (E) compartment represents individuals in the disease's incubation period. The mathematical model is a system of ordinary differential equations (ODEs) with time-varying coefficients as opposed to the conventional model with constant coefficients. This time dependency treatment is necessary as the epidemiological parameters such as infection rate β, recovery

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Cheng, Xiang, Su Li Wang, and Shu Wu Cheng. "Application of Optimization Algorithm for Ceramic Formulations." Advanced Materials Research 518-523 (May 2012): 3938–42. http://dx.doi.org/10.4028/www.scientific.net/amr.518-523.3938.

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ceramic formula; genetic algorithm; the simplex method; particle swarm optimization Abstract. The composition of ceramic materials determines its performance, so ceramic formula determines the performance of the product. The factors, such as supply of local raw materials, chemical composition, and raw material price, determine the formula. Selecting raw materials from a variety of raw materials which are consistent with the target formula and the percentage content of the chemical composition is a very important job. The chemical composition in the ceramic material is various, and the traditio

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Sreerambatla, Saandeep. "Comparative Analysis of Inverse Problem-Solving Techniques: Integrating Deep Learning with Advanced Optimization Methods." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 10 (2024): 1–7. http://dx.doi.org/10.55041/ijsrem29303.

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This paper presents a comparative analysis of four distinct approaches to solving inverse problems in engineering and scientific applications. The studies under comparison integrate deep learning techniques, specifically using TensorFlow, with various optimization methods: Genetic Algorithm (GA), Ant Colony Optimization (ACO), LMFIT (employing Nelder-Mead and Powell methods), and Particle Swarm Optimization (PSO). All four approaches aim to predict design parameters for achieving desired response profiles. This analysis examines the methodologies, performance metrics, and unique features of ea

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Ayadi, A., Bouchaib Radi, Abel Cherouat, and A. El Hami. "Optimization and Identification of the Characteristics of an Hydroformed Structures." Applied Mechanics and Materials 62 (June 2011): 11–20. http://dx.doi.org/10.4028/www.scientific.net/amm.62.11.

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In this study, we present an experimental/numerical methodology which aims to improve 3D thin sheet hydroforming. The experimental study is dedicated to the identification of stress-strain flow by using the Nelder-Mead simplex algorithm optimization from the global measure of displacement and force. Applications are made to the simulation of thin sheet hydroforming using different die geometry to show the efficiency of the proposed methodology to localize plastic instability, thinning of the blanks and damage initiation under different forming condition.

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Radi, Bouchaib, A. Ayadi, Abel Cherouat, Laurence Giraud-Moreau, and A. El Hami. "Identification of the Characteristics of Hydroformed Structures Using Optimization Methods." Key Engineering Materials 473 (March 2011): 723–30. http://dx.doi.org/10.4028/www.scientific.net/kem.473.723.

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In this study, we present an experimental/numerical methodology which aims to improve 3D thin sheet hydroforming. The experimental study is dedicated to the identification of stress-strain flow by using the Nelder-Mead simplex algorithm optimization from the global measure of displacement and force. Applications are made to the simulation of thin sheet hydroforming using different die geometry to show the efficiency of the proposed methodology to localize plastic instability, thinning of the blanks and damage initiation under different forming condition.

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