Search Results - comparing optimization algorithms

Comparison of two optimization algorithms (VOLOTM, SEQU) for CyberKnife® treatment of acoustic neuromas, lung metastases, and liver metastases by Thiele, Martin, Galonske, Kirsten, Ernst, Iris

“…Introduction Two optimization algorithms VOLO™ and sequential optimization algorithm (SEQU…”
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Best practices for comparing optimization algorithms by Beiranvand, Vahid, Hare, Warren, Lucet, Yves

“…Comparing, or benchmarking, of optimization algorithms is a complicated task that involves many subtle considerations to yield a fair and unbiased evaluation…”
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A novel multi-objective spiral optimization algorithm for an innovative solar/biomass-based multi-generation energy system: 3E analyses, and optimization algorithms comparison by Cao, Yan, Nikafshan Rad, Hima, Hamedi Jamali, Danial, Hashemian, Nasim, Ghasemi, Amir

“…•The multi-objective spiral optimization algorithm is designed for the system.•The proposed MOSPO algorithm is compared with NSGA-II, MOPSO, PESA-II, and SPEA-II…”
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A methodology for comparing optimization algorithms for auto-tuning by Willemsen, Floris-Jan, Schoonhoven, Richard, Filipovič, Jiří, Tørring, Jacob O., van Nieuwpoort, Rob, van Werkhoven, Ben

“… As each publication varies in the way experiments are conducted, metrics used, and results reported, comparing the performance of optimization algorithms among publications is infeasible…”
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Advanced Optimization of Artificial Neural Networks for a Syngas Power Plant Using Bio-Inspired Optimization Algorithms: : Comparison Between PSO, GA and a Novel Mosquito Inspired Optimization Algorithm by Aguila-Leon, Jesus, Vargas-Salgado, Carlos, Vega-Gomez, Carlos, Lucero-Tenorio, Miriam

“… As part of the proposed approach, a novel Mosquito Mating Swarm Optimization (MMSO) algorithm is presented…”
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Enhancing predictions of blast-induced ground vibration in open-pit mines: Comparing swarm-based optimization algorithms to optimize self-organizing neural networks by Nguyen, Hoang, Bui, Xuan-Nam, Topal, Erkan

“…) and metaheuristic algorithms. In order to improve the accuracy of the SONIA model, several metaheuristic algorithms were employed, including the Manta Ray Foraging Optimization (MRFO…”
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Comparing optimization algorithms for conventional and freeform optical design by Houllier, Thomas, Lépine, Thierry

“…Five search algorithms from the literature of black-box optimization were implemented and applied to optical design problems…”
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Comparing optimization algorithms for conventional and freeform optical design: erratum by Houllier, Thomas, Lépine, Thierry

“…We want to acknowledge prior research with a very similar focus to our own which we failed to discover before the publication of our paper.We want to…”
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On the Potential of the Particle Swarm Algorithm for the Optimization of Detailed Kinetic Mechanisms. Comparison with the Genetic Algorithm by El Rassy, Elissa, Delaroque, Aurélie, Sambou, Patrick, Chakravarty, Harish Kumar, Matynia, Alexis

“…This work investigates the potential of the particle swarm algorithm for the optimization of detailed kinetic mechanisms…”
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Comparing optimization algorithms for parameter identification of sigmoid model for MR damper by da Silva, Davi Matias Dutra, Avila, Suzana Moreira, de Morais, Marcus Vinicius Girão, Cavallini, Aldemir Aparecido

“…This paper proposes a new hybrid optimization technique that merges a differential evolution algorithm with a local strategy using the Nelder–Mead…”
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New measures for comparing optimization algorithms on dynamic optimization problems by Kordestani, Javidan Kazemi, Rezvanian, Alireza, Meybodi, Mohammad Reza

“…Dynamic optimization problems have emerged as an important field of research during the last two decades, since many real-world optimization problems are changing over time…”
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Hyperparameter Optimization: Comparing Genetic Algorithm against Grid Search and Bayesian Optimization by Alibrahim, Hussain, Ludwig, Simone A.

“…The performance of machine learning algorithms are affected by several factors, some of these factors are related to data quantity, quality, or its features…”
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Development of Hybrid Artificial Neural Network–Particle Swarm Optimization Model and Comparison of Genetic and Particle Swarm Algorithms for Optimization of Machining Fixture Layout by Ramesh, M., Sundararaman, K. A., Sabareeswaran, M., Srinivasan, R.

“…–particle swarm optimization (ANN–PSO) and evolutionary techniques to optimize the fixture layout by minimizing the maximum workpiece deformation on a 2D fixture workpiece system in end milling operation…”
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Multiobjective optimization framework for designing a vehicle suspension system. A comparison of optimization algorithms by Llopis-Albert, Carlos, Rubio, Francisco, Zeng, Shouzhen

“…•We couple several computer-aided design tools (CAD) and computer-aided engineering (CAE) software.•A comparison of optimization algorithms is carried out and the Pareto-optimal front obtained…”
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Comparing Optimization Algorithms in ANN Models for House Price Prediction in Pekanbaru by Winarso, Doni, Edo Arribe, Syahril, Aryanto, Muhardi, Shahrulniza Musa

“…This study evaluates the performance of five optimization algorithms in Artificial Neural Network (ANN…”
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Variational Bayesian Approximation (VBA): Implementation and Comparison of Different Optimization Algorithms by Fallah Mortezanejad, Seyedeh Azadeh, Mohammad-Djafari, Ali

“…: Joint MAP optimization; Posterior expectation computations that require integration methods…”
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Paradox-Free Data Analysis in Numerical Comparisons of Optimization Algorithms: A Review and Case Study by Zhi, Yunchao, Liu, Qunfeng, Yan, Yuan, Jing, Yunpeng

“…Numerical comparison is essential for evaluating an optimization algorithm. Unfortunately, recent research has shown that two paradoxes may occur, namely the cycle ranking paradox and survival of the nonfittest paradox…”
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Comparing Optimization Algorithms for Item Selection in Mokken Scale Analysis by Straat, J. Hendrik, van der Ark, L. Andries, Sijtsma, Klaas

“… This study investigates a variation on Mokken’s item selection procedure, which alleviates the first problem, and proposes a genetic algorithm, which alleviates both problems…”
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A New Optimization Algorithm and Its Comparison on Traditional Optimization Algorithms by Yumin, YIN, Bolin, LIAO, Shuai, LI

“… This kind of algorithm can solve many complex optimization problems, such as assignment problem, knapsack problem, travel path problem and so on, which are difficult to solve by traditional methods…”
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A probabilistic metric for comparing metaheuristic optimization algorithms by Gomes, Wellison J.S., Beck, André T., Lopez, Rafael H., Miguel, Leandro F.F.

“…•Metaheuristic optimization algorithm runs are based on random numbers.•Several runs required for comparing performance of algorithms…”
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