Search Results - (( based evaluation swarm algorithm ) OR ( parallel optimization method algorithm ))
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Single and Multiple variables control using Tree Physiology Optimization
Published 2017“…The proposed algorithm is also compared with deterministic gradient-free algorithm: Nelder-Mead simplex (NMS) and another metaheuristic algorithm: Particle Swarm Optimization (PSO). …”
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Evaluation of Vector Evaluated Particle Swarm Optimisation Enhanced with Non-dominated Solutions and Multiple Nondominated Leaders based on WFG Test Functions
Published 2014“…Multi Objective Optimisation (MOO) problem involves simultaneous minimization or maximization of many objective functions. One of MOO algorithms is Vector Evaluated Particle Swarm Optimization (VEPSO) algorithm. …”
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Voting algorithms for large scale fault-tolerant systems
Published 2011“…To solve this problem and gain benefits of this algorithm, we employed parallel algorithm technique and by using optimal number of processors, we could propose optimal algorithms known as Parallel Average Voting and Parallel Weighted Average Voting which both have optimal time complexity and less calculation cost. …”
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Thesis -
4
Topology-aware hypergraph based approach to optimize scheduling of parallel applications onto distributed parallel architectures
Published 2020“…Any optimization algorithm is suitable for only a specific domain of optimization problems. …”
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Improved particle swarm optimization by fast annealing algorithm
Published 2019“…To evaluate its performance, we examined the algorithm on 14 benchmark functions. …”
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Proceeding Paper -
6
Review of Multi-Objective Swarm Intelligence Optimization Algorithms
Published 2021“…The review is based on how the algorithms deal with objective functions using MOO approaches, the benchmark MOPs used in the evaluation and performance metrics. …”
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7
Swarm intelligence-based feature selection for amphetamine-type stimulants (ATS) drug 3D molecular structure classification
Published 2021“…Their performance is evaluated and compared based on seven performance criteria. …”
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Improving Ant Swarm Optimization With Embedded Vaccination For Optimum Reducts Generation
Published 2011“…This paper has evaluated the immune algorithm in 12 common benchmark dataset to evaluate the performance of rough reducts-based on attribute reduction. …”
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An enhanced swap sequence-based particle swarm optimization algorithm to solve TSP
Published 2021“…To evaluate the proposed algorithm, the solutions to the TSP problem obtained from the proposed algorithm and swap sequence based PSO are compared in terms of the best solution, mean solution, and time taken to converge to the optimal solution. …”
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Bats echolocation-inspired algorithms for global optimisation problems
Published 2016“…The aim of the research is to introduce novel form of swarm intelligence algorithms based on real echolocation behaviour of bats. …”
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Thesis -
12
Evaluation Algorithm - Based on PID Controller Design for the Unstable Systems
Published 2015“…In this project, the evaluating PSO algorithm based on PID controller design for unstable system was proposed. …”
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Levy tunicate swarm algorithm for solving numerical and real-world optimization problems
Published 2022“…The proposed Levy Tunicate Swarm Algorithm (LTSA) is a novel metaheuristic algorithm that integrates the Levy distribution into a new metaheuristic algorithm called Tunicate Swarm Algorithm (TSA) to solve numerical and real-world optimization problems. …”
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Probabilistic ensemble fuzzy ARTMAP optimization using hierarchical parallel genetic algorithms
Published 2015“…This was achieved by mitigating convergence in the genetic algorithms by employing a hierarchical parallel architecture. …”
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An Improved Multi-State Particle Swarm Optimization for Discrete Optimization Problems
Published 2015“…Recently, a state-based algorithm called multi-state particle swarm optimization (MSPSO) has been proposed to solve discrete combinatorial optimization problems. …”
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Document clustering based on firefly algorithm
Published 2015“…Document clustering is widely used in Information Retrieval however, existing clustering techniques suffer from local optima problem in determining the k number of clusters.Various efforts have been put to address such drawback and this includes the utilization of swarm-based algorithms such as particle swarm optimization and Ant Colony Optimization.This study explores the adaptation of another swarm algorithm which is the Firefly Algorithm (FA) in text clustering.We present two variants of FA; Weight- based Firefly Algorithm (WFA) and Weight-based Firefly Algorithm II (WFAII).The difference between the two algorithms is that the WFAII, includes a more restricted condition in determining members of a cluster.The proposed FA methods are later evaluated using the 20Newsgroups dataset.Experimental results on the quality of clustering between the two FA variants are presented and are later compared against the one produced by particle swarm optimization, K-means and the hybrid of FA and -K-means. …”
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Improving particle swarm optimization via adaptive switching asynchronous – synchronous update
Published 2018“…Particle swarm optimization (PSO) is a population-based metaheuristic optimization algorithm that solves a problem through iterative operations. …”
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Indexed Article -
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Improved Hierarchical Structure Poly-particle particle Swarm Optimization
Published 2023“…The performance of the IHSPPSO algorithm was tested and evaluated using four benchmark test functions. …”
Conference paper -
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Investigation on the dynamic of computation of semi autonomous evolutionary computation for syntactic optimization of a set of programming codes
Published 2007“…Genetic Algorithm as one of the Evolutionary Computation method improve the execution of parallel programming codes by optimizing the number of processors and the distribution of data. …”
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Research Report -
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Improving particle swarm optimization via adaptive switching asynchronous – synchronous update
Published 2018“…Particle swarm optimization (PSO) is a population-based metaheuristic optimization algorithm that solves a problem through iterative operations. …”
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