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A Hybrid Algorithm for Metaheuristic Optimization [article]

Sujit Pramod Khanna, Alexander Ororbia II
2019 arXiv   pre-print
Finally,we apply our proposed algorithm to classification problems involving theoptimization of support-vector machine classifiers.  ...  Our approach treatsthe constituent optimizers as a team of complex agents that communicateinformation amongst each other at various intervals during the simulationprocess.  ...  population-based metaheuristics, outperforming other optimization algorithms by a clear margin in terms of speed and accuracy.  ... 
arXiv:1906.02010v1 fatcat:7awolisr2zfwtnhok2nc4dhkme

Implementing Metaheuristic Optimization Algorithms with JECoLi

Pedro Evangelista, Paulo Maia, Miguel Rocha
2009 2009 Ninth International Conference on Intelligent Systems Design and Applications  
This work proposes JECoLi -a novel Java-based library for the implementation of metaheuristic optimization algorithms with a focus on Genetic and Evolutionary Computation based methods.  ...  The library was developed based on the principles of flexibility, usability, adaptability, modularity, extensibility, transparency, scalability, robustness and computational efficiency.  ...  Architecture and main features Main features Available algorithms JECoLi implements a large set of metaheuristic algorithms, namely: • General purpose Evolutionary Algorithms, including Genetic Algorithms  ... 
doi:10.1109/isda.2009.161 dblp:conf/isda/EvangelistaMR09 fatcat:osd7yn52m5aqfebry5joltopmi

Parallel Ant Colony Algorithms [chapter]

Stefan Janson, Daniel Merkle, Martin Middendorf
2005 Parallel Metaheuristics  
Acknowledgments Support by the Deutsche Forschungsgemeinschaft within the project "Methods of Swarm Intelligence on Reconfigurable Architectures" is greatfully acknowledged.  ...  The Ant Colony metaheuristic is described in Section 1.2. Different strategies for parallelization of Ant Colony algorithms are described in section 1.3.  ...  The authors compare their parallel ACO algorithm to other metaheuristics but effect of parallelization on the results was not studied in further detail.  ... 
doi:10.1002/0471739383.ch8 fatcat:grnkr5qe3ff55h7kukzirmmzxy

Collaboration of Metaheuristic Algorithms through a Multi-Agent System [chapter]

Richard Malek
2009 Lecture Notes in Computer Science  
This paper introduces a framework based on multi-agent system for solving problems of combinatorial optimization. The framework allows running various metaheuristic algorithms simultaneously.  ...  By the collaboration of various metaheuristics, we can achieve better results in more classes of problems.  ...  For this purpose, an architecture based on multi-agent system paradigm has been chosen. Algorithm agents and the solution pool agent are the base of the system.  ... 
doi:10.1007/978-3-642-03668-2_7 fatcat:po42rjxcvrhinpuurmn7jxoxte

Cheetah chase algorithm (CCA): a nature-inspired metaheuristic algorithm

M Goudhaman
2018 International Journal of Engineering & Technology  
In this paper, another populace based algorithm, the Cheetah Chase Algorithm (CCA), is presented.  ...  In recent years, appreciable attention among analysts to take care of the extraordinary enhancement issues utilizing metaheuristic algorithms in the domain area of Swarm Intelligence.  ...  This article aims to introduce the basics of metaheuristic optimization, and also some prevalent metaheuristic algorithms.  ... 
doi:10.14419/ijet.v7i3.18.14616 fatcat:b7vjxohdn5bvrlcpumqs56xnqm

Survey of Metaheuristic Algorithms for Combinatorial Optimization

Malti Baghel, Shikha Agrawal, Sanjay Silakari
2012 International Journal of Computer Applications  
Population based methods deal with a set of solutions. These include genetic algorithm, ant colony optimization and particle swarm optimization.  ...  This paper aims to present a brief survey of different metaheuristic algorithms for solving the combinatorial optimization problems.  ...  The effectiveness of the proposed PSO based algorithm is demonstrated by comparing it with another population based heuristic known as genetic algorithm.  ... 
doi:10.5120/9391-3813 fatcat:7ur2f64xvvb6jc4ojsd5jujfeq

Ebola Optimization Search Algorithm (EOSA): A new metaheuristic algorithm based on the propagation model of Ebola virus disease [article]

Olaide N. Oyelade, Absalom E. Ezugwu
2021 arXiv   pre-print
This paper presents a novel metaheuristic algorithm named Ebola optimization algorithm (EOSA).  ...  Extensive simulation results indicate that the EOSA outperforms other state-of-the-art popular metaheuristic optimization algorithms such as the Particle Swarm Optimization Algorithm (PSO), Genetic Algorithm  ...  Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper  ... 
arXiv:2106.01416v2 fatcat:qe6suxypandyfia7ueb5xqzxhe

AutoMH: Automatically Create Evolutionary Metaheuristic Algorithms Using Reinforcement Learning

Boris Almonacid
2022 Entropy  
However, the time required to find an appropriate metaheuristic algorithm, that would have the convenient configurations to solve a set of optimisation problems properly presents a problem.  ...  This process employs an extension of the reinforcement learning approach that considers multi-agents in their environment, and a learning agent composed of an analysis process and a process of modification  ...  The Environment is composed of a set of non-intelligent agents. Each agent has a base template of a metaheuristic algorithm which evolves in each episode by modifying its structure.  ... 
doi:10.3390/e24070957 pmid:35885180 pmcid:PMC9321416 fatcat:ggvio6roebeglas22jbobqxm5u

Metaheuristic algorithms for building Covering Arrays: A review

Jimena Adriana Timaná-Peña, Carlos Alberto Cobos-Lozada, Jose Torres-Jimenez
2016 Revista Facultad de Ingeniería  
This paper presents a description of the major contributions made by a selection of different metaheuristics, including simulated annealing, tabu search, genetic algorithms, ant colony algorithms, particle  ...  It is worth noting that simulated annealing-based algorithms have evolved as the most competitive, and currently form the state of the art.  ...  Algorithms based on metaheuristics A.  ... 
doi:10.19053/01211129.v25.n43.2016.5295 fatcat:57duxmwlw5hefcfkeyufwvr6n4

Ebola Optimization Search Algorithm: A New Nature-Inspired Metaheuristic Optimization Algorithm

Olaide Nathaniel Oyelade, Absalom El-Shamir Ezugwu, Tehnan I. A. Mohamed, Laith Abualigah
2022 IEEE Access  
This study presents a novel metaheuristic algorithm named Ebola Optimization Search Algorithm (EOSA) based on the propagation mechanism of the Ebola virus disease.  ...  A combination of the propagation and mathematical models was adapted for developing the new metaheuristic algorithm.  ...  ACKNOWLEDGMENT The authors wish to state that a previous version of this paper is presented as a preprint and available online as: Oyelade,  ... 
doi:10.1109/access.2022.3147821 fatcat:lbb57sp75vah7aji52hylsr4uu

Combining Metaheuristics and CSP Algorithms to Solve Sudoku

Marlos C. Machado, Luiz Chaimowicz
2011 2011 Brazilian Symposium on Games and Digital Entertainment  
In this paper, we propose the combination of metaheuristics with techniques from the Constraint Satisfaction Problem (CSP) domain that speed up the solution's search process by decreasing its search space  ...  Experiments performed with boards of size 3, 4 and 5 show that this approach allows the resolution of a greater number of instances when compared to an initial baseline.  ...  The first presented a multi-agent approach to the resolution of NP-Complete problems, exemplified by the Sudoku resolution; while the second presented a probabilistic representation of Sudoku, being based  ... 
doi:10.1109/sbgames.2011.18 dblp:conf/sbgames/MachadoC11 fatcat:ppwac62zizbp7duixg776taz6e

Optimal Power Allocation Based on Metaheuristic Algorithms in Wireless Network

Qiushi Sun, Haitao Wu, Ovanes Petrosian
2022 Mathematics  
This paper considers the metaheuristic algorithms for the power allocation issue. A series of state-of-the-art stochastic algorithms are compared with the benchmark algorithm on network scales.  ...  The simulation results demonstrate the superiority of the proposed algorithms against the conventional benchmark algorithms.  ...  We use a simple generation-evaluation method for the metaheuristic algorithm for tuning the parameters. A set of a priori candidate configurations is generated.  ... 
doi:10.3390/math10183336 fatcat:3ie2efpo2ffg3pf4iyepsn6wgq

A framework for implementing metaheuristic algorithms using intercellular communication [article]

Martín Eduardo Gutiérrez, Yerko Miguel Ortiz, Javier Carrión
2020 bioRxiv   pre-print
As a proof-of-concept, we also implemented the workflow associated to the framework, and tested the execution of two specific MH (Genetic Algorithms and Simulated Annealing) encoded as synthetic circuits  ...  This work seeks to lay the foundations of mappings for implementing AI algorithms in a general manner using Synthetic Biology constructs in cell colonies.  ...  The output design of this interpreter is generated based on 2) and configured based on 1). A depiction of the framework elements and their relationship is shown in Figure 1 .  ... 
doi:10.1101/2020.02.06.937979 fatcat:xacoy5gcr5garoczwiak3adgfy

Multi-objective optimization using metaheuristics: non-standard algorithms

El-Ghazali Talbi, Matthieu Basseur, Antonio J. Nebro, Enrique Alba
2012 International Transactions in Operational Research  
Most studies on metaheuristics for multiobjective optimization are focused on Evolutionary Algorithms, and some of the state-of-the-art techniques belong to this class of algorithms.  ...  In recent years, the application of metaheuristic techniques to solve multi-objective optimization problems (MOPs) has become an active research area.  ...  Teamwork hybrids represent cooperative optimization models, in which several agents cooperate in a parallel way, each agent carrying out a search in a solution space.  ... 
doi:10.1111/j.1475-3995.2011.00808.x fatcat:wkcurdztanek5i5ptwfikqha3a

A Hybrid Metaheuristic Algorithm for the Efficient Placement of UAVs

Stephanie Alvarez Fernandez, Marcelo M. Carvalho, Daniel G. Silva
2020 Algorithms  
problem, and we propose a hybrid metaheuristic algorithm to solve it.  ...  A series of numerical experiments illustrate the efficiency of the proposed algorithm against traditional optimization tools, which achieves high-quality results in very short time intervals, thus making  ...  The Q-learning-based algorithm was applied when UAVs were moving.  ... 
doi:10.3390/a13120323 fatcat:azm6yygkabh7tey3ryzvey55nm
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