Bridges Reinforcement Through Conversion of Tied-Arch Using Crow Search Algorithm

Valdivia, Sergio; Crawford, Broderick; Soto, Ricardo; Lemus-Romani, José; Astorga, Gino; Misra, Sanjay; Salas-Fernández, Agustín; Rubio, José Miguel

doi:10.1007/978-3-030-24308-1_42

Cited by 7 publications

(3 citation statements)

References 10 publications

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“…It considers whether their models were naturally inspired. There are bio-inspired algorithms, such as Crow Search Algorithm (CSA) [ 11 ] and Intelligent Water Drops (IWD) [ 2 ], and those not inspired by nature, for example Tabu Search (TS) and Local Iterate Search (ILS). This classification is not very significant after hybrid algorithms have been developed.…”

Section: Metaheuristicsmentioning

confidence: 99%

Solving the 0/1 Knapsack Problem Using a Galactic Swarm Optimization with Data-Driven Binarization Approaches

Vásquez

Lemus-Romani

Crawford

et al. 2020

Lecture Notes in Computer Science

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Metaheuristics are used to solve high complexity problems, where resolution by exact methods is not a viable option since the resolution time when using these exact methods is not acceptable. Most metaheuristics are defined to solve problems of continuous optimization, which forces these algorithms to adapt its work in the discrete domain using discretization techniques to solve complex problems. This paper proposes data-driven binarization approaches based on clustering techniques. We solve different instances of Knapsack Problems with Galactic Swarm Optimization algorithm using this machine learning techniques.

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Section: Metaheuristicsmentioning

confidence: 99%

Solving the 0/1 Knapsack Problem Using a Galactic Swarm Optimization with Data-Driven Binarization Approaches

Vásquez

Lemus-Romani

Crawford

et al. 2020

Lecture Notes in Computer Science

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“…It is important to consider that social and human phenomena must be evaluated considering the cultural context of the moment. For example, wars, pandemics (as is COVID-19 today) or important revolutions like the industrial one or the current digital transformation [ 6 , 37 , 38 ]. There are various human and social behaviours that can be used as inspiration.…”

Section: Introductionmentioning

confidence: 99%

Ambidextrous Socio-Cultural Algorithms

Lemus-Romani

Crawford

Soto

et al. 2020

Computational Science and Its Applications – ICCSA 2020

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Metaheuristics are a class of algorithms with some intelligence and self-learning capabilities to find solutions to difficult combinatorial problems. Although the promised solutions are not necessarily globally optimal, they are computationally economical. In general, these types of algorithms have been created by imitating intelligent processes and behaviors observed in nature, sociology, psychology and other disciplines. Metaheuristic-based search and optimization is currently widely used for decision making and problem solving in different contexts. The inspiration for metaheuristic algorithms are mainly based on nature’s behaviour or biological behaviour. Designing a good metaheurisitcs is making a proper trade-off between two forces: Exploration and exploitation. It is one of the most basic dilemmas that both individuals and organizations constantly are facing. But there is a little researched branch, which corresponds to the techniques based on the social behavior of people or communities, which are called Social-inspired. In this paper we explain and compare two socio-inspired metaheuristics solving a benchmark combinatorial problem.

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“…studied approach was the integration between the crow search algorithm and the Kmean method (CSAKmean). Both hybridizations were proposed by Valdivia et al in [51]. Finally, we employ an improved version of the cuckoo search algorithm with the Kmean transition algorithm (KMTA), recently proposed by García et al in [52].…”

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confidence: 99%

A Self-Adaptive Cuckoo Search Algorithm Using a Machine Learning Technique

et al. 2021

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Metaheuristics are intelligent problem-solvers that have been very efficient in solving huge optimization problems for more than two decades. However, the main drawback of these solvers is the need for problem-dependent and complex parameter setting in order to reach good results. This paper presents a new cuckoo search algorithm able to self-adapt its configuration, particularly its population and the abandon probability. The self-tuning process is governed by using machine learning, where cluster analysis is employed to autonomously and properly compute the number of agents needed at each step of the solving process. The goal is to efficiently explore the space of possible solutions while alleviating human effort in parameter configuration. We illustrate interesting experimental results on the well-known set covering problem, where the proposed approach is able to compete against various state-of-the-art algorithms, achieving better results in one single run versus 20 different configurations. In addition, the result obtained is compared with similar hybrid bio-inspired algorithms illustrating interesting results for this proposal.

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Bridges Reinforcement Through Conversion of Tied-Arch Using Crow Search Algorithm

Cited by 7 publications

References 10 publications

Solving the 0/1 Knapsack Problem Using a Galactic Swarm Optimization with Data-Driven Binarization Approaches

Solving the 0/1 Knapsack Problem Using a Galactic Swarm Optimization with Data-Driven Binarization Approaches

Ambidextrous Socio-Cultural Algorithms

A Self-Adaptive Cuckoo Search Algorithm Using a Machine Learning Technique

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