Portfolio optimization with mean-variance approach using hunting search meta-heuristic algorithm [PDF]
تحقیقات مالی, 2014 This paper presents a new meta-heuristic solution to find the efficient frontier using the mean-variance approach. Portfolio optimization problem is a quadratic programming model and, changes to NP-hard if the number of assets and constraints has ...
Morteza Elahi +2 more
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Sensing cloud optimization to solve ED of units with valve-point effects and multi-fuels [PDF]
, 2013 In this paper a solution to an highly constrained and non-convex economical dispatch (ED) problem with a meta-heuristic technique named Sensing Cloud Optimization (SCO) is presented.
A.I. Selvakumar +8 more
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Meta-Heuristics: An Overview [PDF]
, 1996 Meta-heuristics are the most recent development in approximate search methods for solving complex optimization problems, that arise in business, commerce, engineering, industry, and many other areas. A meta-heuristic guides a subordinate heuristic using concepts derived from artificial intelligence, biological, mathematical, natural and physical ...
Ibrahim H. Osman, James P. Kelly
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A new hybrid method DSM for parameter setting of meta-heuristic algorithms [PDF]
مجله مدل سازی در مهندسی, 2021 Parameters of meta-heuristic algorithms are very effective in their performance and are usually done experimentally, which is very time-consuming. In this research, a hybrid method for selecting the optimal parameters of meta-heuristic algorithms is ...
Elham Shadkam, Mehrnaz Ghayoor
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Effective heuristics and meta-heuristics for the quadratic assignment problem with tuned parameters and analytical comparisons [PDF]
, 2012 Quadratic assignment problem (QAP) is a well-known problem in the facility location and layout. It belongs to the NP-complete class. There are many heuristic and meta-heuristic methods, which are presented for QAP in the literature.
Bashiri, Mahdi, Karimi, Hossein
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Optimization of machining processes using pattern search algorithm [PDF]
, 2014 Optimization of machining processes not only increases machining efficiency and economics, but also the end product quality. In recent years, among the traditional optimization methods, stochastic direct search optimization methods such as meta-heuristic
Miloš Madić, Miroslav Radovanović
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Quality-Of-Control-Aware Scheduling of Communication in TSN-Based Fog Computing Platforms Using Constraint Programming [PDF]
, 2020 In this paper we are interested in real-time control applications that are implemented using Fog Computing Platforms consisting of interconnected heterogeneous Fog Nodes (FNs).
Barzegaran, Mohammadreza +2 more
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Investigating Evaluation Measures in Ant Colony Algorithms for Learning Decision Tree Classifiers [PDF]
, 2015 Ant-Tree-Miner is a decision tree induction algorithm that is based on the Ant Colony Optimization (ACO) meta- heuristic. Ant-Tree-Miner-M is a recently introduced extension of Ant-Tree-Miner that learns multi-tree classification models.
Abdelbar, Ashraf M. +2 more
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Nonparametric Comparison of Two Dynamic Parameter Setting Methods in a Meta-Heuristic Approach [PDF]
Journal of Systemics, Cybernetics and Informatics, 2007 Meta-heuristics are commonly used to solve combinatorial problems in practice. Many approaches provide very good quality solutions in a short amount of computational time; however most meta-heuristics use parameters to tune the performance of the meta ...
Seyhun HEPDOGAN +3 more
doaj
IET Software, 2021 In this research, a new method for population initialisation in meta‐heuristic algorithms based on the Pareto 80/20 rule is presented. The population in a meta‐heuristic algorithm has two important tasks, including pushing the algorithm toward the real ...
Mohammad Reza Hasanzadeh, Farshid Keynia
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