Results 11 to 20 of about 177,718 (299)
A Hybrid Optimization Framework with Dynamic Transition Scheme for Large-Scale Portfolio Management
Algorithms, 2022 Meta-heuristic algorithms have successfully solved many real-world problems in recent years. Inspired by different natural phenomena, the algorithms with special search mechanisms can be good at tackling certain problems.
Zhenglong Li, Vincent Tam
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A Self-Parametrization Framework for Meta-Heuristics [PDF]
Mathematics, 2022 Even while the scientific community has shown great interest in the analysis of meta-heuristics, the analysis of their parameterization has received little attention. It is the parameterization that will adapt a meta-heuristic to a problem, but it is still performed, mostly, empirically. There are multiple parameterization techniques; however, they are
André S. Santos +2 more
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A comprehensive review on meta-heuristic algorithms and their classification with novel approach
Journal of Applied Research on Industrial Engineering, 2021 Conventional and classical optimization methods are not efficient enough to deal with complicated, NP-hard, high-dimensional, non-linear, and hybrid problems.
Hojatollah Rajabi Moshtaghi +2 more
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Meta-heuristics for portfolio optimization
Soft Computing, 2023 AbstractPortfolio optimization has been studied extensively by researchers in computer science and finance, with new and novel work frequently published. Traditional methods, such as quadratic programming, are not computationally effective for solving complex portfolio models.
Kyle Erwin, Andries P. Engelbrecht
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Review of Quantum-inspired Metaheuristic Algorithms and Its Applications [PDF]
Jisuanji kexueThe quantum meta heuristic algorithm is developed by applying quantum computing to the meta-heuristic algorithm.This kind of algorithm is good at solving combinatorial and numerical optimization problems,and has the characteristics of acce-lerated ...
RUAN Ning, LI Chun, MA Haoyue, JIA Yi, LI Tao
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Meta-heuristic algorithms in car engine design: a literature survey [PDF]
, 2015 Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy.
Tayarani-N, Mohammad-H. +2 more
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Meta Reinforcement Learning for Heuristic Planing
Proceedings of the International Conference on Automated Planning and Scheduling, 2021 Heuristic planning has a central role in classical planning applications and competitions. Thanks to this success, there has been an increasing interest in using Deep Learning to create high-quality heuristics in a supervised fashion, learning from optimal solutions of previously solved planning problems.
Ricardo Luna Gutierrez 0001 +1 more
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An Opposition-Based Chaotic Salp Swarm Algorithm for Global Optimization
IEEE Access, 2020 The salp swarm algorithm (SSA) is a bio-heuristic optimization algorithm proposed in 2017. It has been proved that SSA has competitive results compared to several other well-known meta-heuristic algorithms on various optimization problem.
Xiaoqiang Zhao +3 more
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The probabilistic heuristic in local (PHIL) search meta-strategy [PDF]
, 2005 Local search, in either best or first admissible form, generally suffers from poor solution qualities as search cannot be continued beyond locally optimal points. Even multiple start local search strategies can suffer this problem.
A. Ernst +10 more
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Optimizing quantum heuristics with meta-learning [PDF]
Quantum Machine Intelligence, 2021 AbstractVariational quantum algorithms, a class of quantum heuristics, are promising candidates for the demonstration of useful quantum computation. Finding the best way to amplify the performance of these methods on hardware is an important task. Here, we evaluate the optimization of quantum heuristics with an existing class of techniques called “meta-
Max Wilson 0001 +5 more
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