Cultural Evolution Algorithm for Global Optimizations and its Applications
H. C. Kuo,C. H. Lin +1 more
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TLDR
The socio-cultural model based on a system thought framework could be used to develop a cultural evolution algorithm (CEA) and the results show that all of the four strategies have better performance when compared with relevant literatures.About:
This article is published in Journal of Applied Research and Technology.The article was published on 2013-08-01 and is currently open access. It has received 67 citations till now. The article focuses on the topics: Cultural algorithm & Population.read more
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Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications
TL;DR: The comparison results on the benchmark functions suggest that MRFO is far superior to its competitors, and the real-world engineering applications show the merits of this algorithm in tackling challenging problems in terms of computational cost and solution precision.
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An intensify Harris Hawks optimizer for numerical and engineering optimization problems
TL;DR: The proposed hHHO-SCA optimization algorithm is much better than standard sine–cosine optimization algorithm, Harris Hawks Optimizer, Ant Lion Optimizer algorithm, Moth Flame Optimization algorithm, grey wolf optimizer algorithm and others recently described meta-heuristics, heuristics and hybrid type optimization search algorithm and endorses its effectiveness in multi-disciplinary design and engineering optimization problems.
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A novel atom search optimization for dispersion coefficient estimation in groundwater
TL;DR: The experimental results demonstrate that ASO can outperform other well-known approaches such as Particle Swarm Optimization, Genetic Algorithm and Bacterial Foraging Optimization and thatASO is competitive to its competitors for parameter estimation problems.
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Socio evolution & learning optimization algorithm: A socio-inspired optimization methodology
TL;DR: Results indicate that SELO demonstrates comparable performance to other comparison algorithms, which gives ground to the authors to further establish the effectiveness of this metaheuristic by solving purposeful and real world problems.
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Supply-Demand-Based Optimization: A Novel Economics-Inspired Algorithm for Global Optimization
TL;DR: The proposed supply-demand-based optimization algorithm is compared with other state-of-the-art counterparts on 29 benchmark test functions and six engineering optimization problems and proves that SDO is able to provide very promising results in terms of exploration, exploitation, local optima avoidance, and convergence rate.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Proceedings ArticleDOI
Particle swarm optimization
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Book
Adaptation in natural and artificial systems
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Journal ArticleDOI
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces
Rainer Storn,Kenneth Price +1 more
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.
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Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces
Rainer Storn,Kenneth Price +1 more