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Kombinatorinis optmizavimas ir metaeuristiniai metodai: teoriniai aspektai
Informacijos Mokslai, 2008 Straipsnyje aptariami kombinatorinio optimizavimo ir intelektualių optimizavimo priemonių, t. y. metaeuristinių metodų (metaeuristikų), teoriniai aspektai. Apibūdinami kombinatorinio optimizavimo uždaviniai, jų savybės, specifika.
Alfonsas Misevičius +2 more
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Two-Stage Eagle Strategy with Differential Evolution [PDF]
, 2012 Efficiency of an optimization process is largely determined by the search algorithm and its fundamental characteristics. In a given optimization, a single type of algorithm is used in most applications.
Deb, Suash, Yang, Xin-She
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On the use of biased-randomized algorithms for solving non-smooth optimization problems [PDF]
, 2020 Soft constraints are quite common in real-life applications. For example, in freight transportation, the fleet size can be enlarged by outsourcing part of the distribution service and some deliveries to customers can be postponed as well; in inventory ...
Ferrer Biosca, Albert +4 more
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International Journal of Photoenergy, 2021 A transition to solar energy systems is considered one of the most important alternatives to conventional fossil fuels. Until recently, solar air heaters (SAHs) were among the other solar energy systems that have been widely used in various households ...
Jean De Dieu Niyonteze +7 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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Firefly Algorithm, Stochastic Test Functions and Design Optimisation [PDF]
, 2010 Modern optimisation algorithms are often metaheuristic, and they are very promising in solving NP-hard optimization problems. In this paper, we show how to use the recently developed Firefly Algorithm to solve nonlinear design problems.
Yang, Xin-She
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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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Dwarf Mongoose Optimization Metaheuristics for Autoregressive Exogenous Model Identification
Mathematics, 2022 Nature-inspired metaheuristic algorithms have gained great attention over the last decade due to their potential for finding optimal solutions to different optimization problems.
Khizer Mehmood +6 more
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Efficiency Analysis of Swarm Intelligence and Randomization Techniques [PDF]
, 2012 Swarm intelligence has becoming a powerful technique in solving design and scheduling tasks. Metaheuristic algorithms are an integrated part of this paradigm, and particle swarm optimization is often viewed as an important landmark.
Yang, Xin-She
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