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Journal ArticleDOI

Hybrid Harmony Search Algorithms

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TLDR
This article presents a review of hybrid harmony search algorithms, a music-inspired population-based meta-heuristic search and optimization algorithm that combines HSA with other algorithms to improve exploration or global search ability and increase convergence speed.
Abstract
Harmony search algorithm (HSA) is a music-inspired population-based meta-heuristic search and optimization algorithm. In order to improve exploration or global search ability, exploit local search more effectively, increase convergence speed, improve solution quality, and minimize computational cost, researchers have advanced the concept of hybridizing HSA with other algorithms. This article presents a review of hybrid harmony search algorithms.

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Citations
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Nature Inspired Computing: An Overview and Some Future Directions

TL;DR: An overview of significant advances made in the emerging field of nature-inspired computing with a focus on the physics- and biology-based approaches and algorithms is presented.
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Invited Review: Recent developments in vibration control of building and bridge structures

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Optimization of University Course Scheduling Problem using Particle Swarm Optimization with Selective Search

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Simulated Annealing, Its Variants and Engineering Applications

TL;DR: A review of research reported on simulated annealing (SA) finds different cooling/annealing schedules are summarized and recent applications of SA in engineering are reviewed.
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Covariance Matrix Adaptation Pareto Archived Evolution Strategy with Hypervolume-sorted Adaptive Grid Algorithm

TL;DR: The proposed algorithm, named Covariance Matrix Adaptation Pareto Archived Evolution Strategy with Hypervolume-sorted Adaptive Grid Algorithm (CMA-PAES-HAGA), overcomes the limitation of CMA- PAES in handling more than two objectives and displays a remarkably good performance on a scalable test suite in five, seven, and ten-objective problems.
References
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Journal ArticleDOI

A simplex method for function minimization

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Some methods for classification and analysis of multivariate observations

TL;DR: The k-means algorithm as mentioned in this paper partitions an N-dimensional population into k sets on the basis of a sample, which is a generalization of the ordinary sample mean, and it is shown to give partitions which are reasonably efficient in the sense of within-class variance.
Journal ArticleDOI

Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
Book

Pattern Recognition with Fuzzy Objective Function Algorithms

TL;DR: Books, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with, becomes what you need to get.
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