A novel parallel multi-swarm algorithm based on comprehensive learning particle swarm optimization

Gülcü, Şaban; Kodaz, Halife

doi:10.1016/j.engappai.2015.06.013

Cited by 94 publications

(43 citation statements)

References 49 publications

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“…The effectiveness and the productivity of many metaheuristic algorithms worsen as the dimensionality of the problem increases [29]. To overcome this problem, we proposed the MMPSO algorithm for obtaining the optimal solutions in a short time.…”

Section: Multimean Particle Swarm Optimization Algorithmmentioning

confidence: 99%

A Novel Multimean Particle Swarm Optimization Algorithm for Nonlinear Continuous Optimization: Application to Feed-Forward Neural Network Training

Hacıbeyoğlu

Ibrahim

2018

Scientific Programming

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Multilayer feed-forward artificial neural networks are one of the most frequently used data mining methods for classification, recognition, and prediction problems. The classification accuracy of a multilayer feed-forward artificial neural networks is proportional to training. A well-trained multilayer feed-forward artificial neural networks can predict the class value of an unseen sample correctly if provided with the optimum weights. Determining the optimum weights is a nonlinear continuous optimization problem that can be solved with metaheuristic algorithms. In this paper, we propose a novel multimean particle swarm optimization algorithm for multilayer feed-forward artificial neural networks training. The proposed multimean particle swarm optimization algorithm searches the solution space more efficiently with multiple swarms and finds better solutions than particle swarm optimization. To evaluate the performance of the proposed multimean particle swarm optimization algorithm, experiments are conducted on ten benchmark datasets from the UCI repository and the obtained results are compared to the results of particle swarm optimization and other previous research in the literature. The analysis of the results demonstrated that the proposed multimean particle swarm optimization algorithm performed well and it can be adopted as a novel algorithm for multilayer feedforward artificial neural networks training.

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Section: Multimean Particle Swarm Optimization Algorithmmentioning

confidence: 99%

A Novel Multimean Particle Swarm Optimization Algorithm for Nonlinear Continuous Optimization: Application to Feed-Forward Neural Network Training

Hacıbeyoğlu

Ibrahim

2018

Scientific Programming

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“…PCLPSO handles to overcome this deficiency like many PSO variants. The PCLPSO algorithm based on CLPSO was proposed by Gülcü and Kodaz [10]. The solution quality is enhanced through multiswarm and cooperation properties.…”

Section: Pclpsomentioning

confidence: 99%

“…The cluster specifications are so: windows XP operating system, pentium i5 3.10 GHz, 2 GB memory, java se 1.7, Jade 4.2 and gigabit ethernet. The flowchart of the PCLPSO algorithm is given in [10]. Figure 1.…”

Section: Pclpsomentioning

confidence: 99%

“…Figure 1. The communication topology [10] In the communication topology, there isn't any directly communication between slave swarms as shown in Figure 1. Migration process occurs periodically after a certain number of cycles.…”

Section: Pclpsomentioning

confidence: 99%

“…PSO has been applied to many various fields such as automotive industry [5], energy [6], synchronous motor design [7], bioinformatics [8]. In recent years, many algorithms based on PSO have been developed such as the comprehensive learning PSO (CLPSO) algorithm [9] and the parallel comprehensive learning particle swarm optimization (PCLPSO) algorithm [10]. In recent years, devising parallel models of algorithms has been a healthy field for developing more efficient optimization procedures [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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Analysing the Migration Period Parameter in Parallel Multi-Swarm Particle Swarm Optimization

Gülcü¹,

Kodaz²

2016

IJCSIT

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Study on Wave Absorption and Multilayer Optimum Design of γ‐Fe₂O₃/Graphene Oxide/Ultrahigh‐Performance Concrete Sheet

Zhao

ChangAn

et al. 2023

Adv Eng Mater

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Combining ultrahigh‐performance concrete (UHPC) with graphene oxide (GO) and γ‐Fe2O3 nanoparticles, the γ‐Fe2O3/GO/UHPC composite wave absorbing material has a broad application prospect in the field of military and civil wave absorbing and shielding. Herein, the physical characteristics and wave absorption properties of monolayer γ‐Fe2O3/GO/UHPC composite plate are investigated and analyzed. Then, the multilayer γ‐Fe2O3/GO/UHPC composite absorbing material with three layers of doped ceramics, resin, and air layer is optimized by orthogonal experiments and particle swarm optimization (PSO) algorithm. The results show that the influence of the three kinds of media on the doped composite plate is as follows: steel fiber is larger than γ‐Fe2O3 and lager than GO. When the contents of γ‐Fe2O3 and GO steel fiber in single‐layer Fe2O3/GO/UHPC composite board are 1.9%, 0.2%, and 1.5%, respectively, the wave absorption effect is the best at −15.603 dB. The reflectivity of multilayer γ‐Fe2O3/GO/UHPC composite plate with ceramic, resin, and air layer is −4 to −19 dB, −1 to −20 dB, and 0 to −25 dB, respectively. The reflectivity of the multilayer γ‐Fe2O3/GO/UHPC composite plate optimized using PSO algorithm is in the range of −10 to −20 dB, and the effective bandwidth is 2–18 GHz.

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A novel parallel multi-swarm algorithm based on comprehensive learning particle swarm optimization

Cited by 94 publications

References 49 publications

A Novel Multimean Particle Swarm Optimization Algorithm for Nonlinear Continuous Optimization: Application to Feed-Forward Neural Network Training

A Novel Multimean Particle Swarm Optimization Algorithm for Nonlinear Continuous Optimization: Application to Feed-Forward Neural Network Training

Analysing the Migration Period Parameter in Parallel Multi-Swarm Particle Swarm Optimization

Study on Wave Absorption and Multilayer Optimum Design of γ‐Fe₂O₃/Graphene Oxide/Ultrahigh‐Performance Concrete Sheet

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A novel parallel multi-swarm algorithm based on comprehensive learning particle swarm optimization

Cited by 94 publications

References 49 publications

A Novel Multimean Particle Swarm Optimization Algorithm for Nonlinear Continuous Optimization: Application to Feed-Forward Neural Network Training

A Novel Multimean Particle Swarm Optimization Algorithm for Nonlinear Continuous Optimization: Application to Feed-Forward Neural Network Training

Analysing the Migration Period Parameter in Parallel Multi-Swarm Particle Swarm Optimization

Study on Wave Absorption and Multilayer Optimum Design of γ‐Fe2O3/Graphene Oxide/Ultrahigh‐Performance Concrete Sheet

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About

Study on Wave Absorption and Multilayer Optimum Design of γ‐Fe₂O₃/Graphene Oxide/Ultrahigh‐Performance Concrete Sheet