Results 11 to 20 of about 46,149 (202)
A statistical learning based approach for parameter fine-tuning of metaheuristics [PDF]
, 2016 Metaheuristics are approximation methods used to solve combinatorial optimization problems. Their performance usually depends on a set of parameters that need to be adjusted.
Calvet Liñán, Laura +3 more
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MEVO: A Metamodel-Based Evolutionary Optimizer for Building Energy Optimization
Energies, 2023 A deep energy retrofit of building envelopes is a vital strategy to reduce final energy use in existing buildings towards their net-zero emissions performance.
Rafael Batres +3 more
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Metaheuristic Algorithms for Convolution Neural Network [PDF]
, 2016 A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry.
Arymurthy, Aniati Murni +2 more
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Conceptual Comparison of Population Based Metaheuristics for Engineering Problems
The Scientific World Journal, 2015 Metaheuristic algorithms are well-known optimization tools which have been employed for solving a wide range of optimization problems. Several extensions of differential evolution have been adopted in solving constrained and nonconstrained multiobjective
Oluwole Adekanmbi, Paul Green
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A new human-based metahurestic optimization method based on mimicking cooking training
Scientific Reports, 2022 Metaheuristic algorithms have a wide range of applications in handling optimization problems. In this study, a new metaheuristic algorithm, called the chef-based optimization algorithm (CBOA), is developed.
Eva Trojovská, Mohammad Dehghani
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A novel metaheuristic optimization algorithm: the monarchy metaheuristic
TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES, 2019 In this paper, we introduce a novel metaheuristic optimization algorithm named the monarchy metaheuristic (MN). Our proposed metaheuristic was inspired by the monarchy government system. Unlike many other metaheuristics, it is easy to implement and does not need a lot of parameters. This makes it applicable to a wide range of optimization problems.
Ibtissam Ahmia, Méziane Aïder
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Metaheuristic optimization frameworks: a survey and benchmarking [PDF]
Soft Computing, 2011 This paper performs an unprecedented comparative study of Metaheuristic optimization frameworks. As criteria for comparison a set of 271 features grouped in 30 characteristics and 6 areas has been selected. These features include the different metaheuristic techniques covered, mechanisms for solution encoding, constraint handling, neighborhood ...
José Antonio Parejo +3 more
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Scholarpedia, 2011 Many real life problems are so complex in their nature that they cannot be efficiently and reliably solved within a reasonable amount of time by using traditional methods which guaranty the optimality of the solution. However, in recent years metaheuristics have proved to offer a promising approach to tackle this kind of problems.
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Enhanced Metaheuristic Optimization: Wind-Driven Flower Pollination Algorithm
IEEE Access, 2019 The flower pollination algorithm is a new metaheuristic optimization technique that simulates the pollination behavior of flowers in nature. The global and local search processes of the algorithm are performed by simulating the self-pollination and cross-
Mengyi Lei, Yongquan Zhou, Qifang Luo
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Accelerated Particle Swarm Optimization and Support Vector Machine for Business Optimization and Applications [PDF]
, 2011 Business optimization is becoming increasingly important because all business activities aim to maximize the profit and performance of products and services, under limited resources and appropriate constraints.
A. Chatterjee +23 more
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