scholarly journals Multi-objective PSO with Pareto Neighborhood topology for Clustering

2021 ◽  
Author(s):  
Yassmine Soussi ◽  
Nizar Rokbani ◽  
Ali Wali ◽  
Adel Alimi
Keyword(s):  
Real Life ◽  
Objective Functions ◽  
Validity Index ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Silhouette Index ◽  
A New Technique ◽  
Clustering Optimization ◽  
Neighborhood Topology ◽  
Pareto Optimal Fronts

In this paper a new technique is integrated to Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, named Pareto Neighborhood (PN) topology, to produce MOPSO-PN algorithm. This technique involves iteratively selecting a set of best solutions from the Pareto-Optimal-Fronts and trying to explore them in order to find better clustering results in the next iteration. MOPSO-PN was then used as a Multi?Objective Clustering Optimization (MOCO) Algorithm, it was tested on various datasets (real-life and artificial datasets). Two scenarios have been used to test the performances of MOPSO-PN for clustering: In the first scenario MOPSO-PN utilizes, as objective functions, two clusters validity index (Silhouette?Index and overall-cluster-deviation), three datasets for test, four algorithms for comparison and the average Minkowski Score as metric for evaluating the final clustering result; In the second scenario MOPSO-PN used, as objectives functions, three clusters validity index (I-index, Con-index and Sym?index), 20 datasets for test, ten algorithms for comparison and the F-Measure as metric for evaluating the final clustering result. In both scenarios, MOPSO-PN provided a competitive clustering results and a correct number of clusters for all datasets.

scholarly journals Multi-objective PSO with Pareto Neighborhood topology for Clustering

2021 ◽  
Author(s):  
Yassmine Soussi ◽  
Nizar Rokbani ◽  
Ali Wali ◽  
Adel Alimi
Keyword(s):  
Real Life ◽  
Objective Functions ◽  
Validity Index ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Silhouette Index ◽  
A New Technique ◽  
Clustering Optimization ◽  
Neighborhood Topology ◽  
Pareto Optimal Fronts

In this paper a new technique is integrated to Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, named Pareto Neighborhood (PN) topology, to produce MOPSO-PN algorithm. This technique involves iteratively selecting a set of best solutions from the Pareto-Optimal-Fronts and trying to explore them in order to find better clustering results in the next iteration. MOPSO-PN was then used as a Multi?Objective Clustering Optimization (MOCO) Algorithm, it was tested on various datasets (real-life and artificial datasets). Two scenarios have been used to test the performances of MOPSO-PN for clustering: In the first scenario MOPSO-PN utilizes, as objective functions, two clusters validity index (Silhouette?Index and overall-cluster-deviation), three datasets for test, four algorithms for comparison and the average Minkowski Score as metric for evaluating the final clustering result; In the second scenario MOPSO-PN used, as objectives functions, three clusters validity index (I-index, Con-index and Sym?index), 20 datasets for test, ten algorithms for comparison and the F-Measure as metric for evaluating the final clustering result. In both scenarios, MOPSO-PN provided a competitive clustering results and a correct number of clusters for all datasets.

scholarly journals Multi-objective PSO with Pareto Neighborhood topology for Clustering

2021 ◽  
Author(s):  
Yassmine Soussi ◽  
Nizar Rokbani ◽  
Seyedali Mirjalili ◽  
Ali Wali ◽  
Adel Alimi
Keyword(s):  
Real Life ◽  
Objective Functions ◽  
Validity Index ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Silhouette Index ◽  
A New Technique ◽  
Clustering Optimization ◽  
Neighborhood Topology ◽  
Pareto Optimal Fronts

In this paper a new technique is integrated to Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, named Pareto Neighborhood (PN) topology, to produce MOPSO-PN algorithm. This technique involves iteratively selecting a set of best solutions from the Pareto-Optimal-Fronts and trying to explore them in order to find better clustering results in the next iteration. MOPSO-PN was then used as a Multi?Objective Clustering Optimization (MOCO) Algorithm, it was tested on various datasets (real-life and artificial datasets). Two scenarios have been used to test the performances of MOPSO-PN for clustering: In the first scenario MOPSO-PN utilizes, as objective functions, two clusters validity index (Silhouette?Index and overall-cluster-deviation), three datasets for test, four algorithms for comparison and the average Minkowski Score as metric for evaluating the final clustering result; In the second scenario MOPSO-PN used, as objectives functions, three clusters validity index (I-index, Con-index and Sym?index), 20 datasets for test, ten algorithms for comparison and the F-Measure as metric for evaluating the final clustering result. In both scenarios, MOPSO-PN provided a competitive clustering results and a correct number of clusters for all datasets.

scholarly journals Multi-objective Quantum Moth Flame Optimization for Clustering

2020 ◽  
Author(s):  
yassmine Soussi ◽  
Nizar Rokbani ◽  
Ali Wali ◽  
Adel Alimi
Keyword(s):  
Real Life ◽  
Objective Functions ◽  
Cluster Validity ◽  
Number Of Clusters ◽  
Clustering Techniques ◽  
Multi Objective ◽  
Validity Criteria ◽  
Clustering Optimization ◽  
Clustering Problems ◽  
Artificial Datasets

This paper defines a new Moth-Flame optimization version with Quantum behaved moths, QMFO. The multi-objective version of QMFO (MOQMFO) is then applied to solve clustering problems. MOQMFO used three cluster validity criteria as objective functions (the I-index, Con-index and Sym-index) to establish the multi-objective clustering optimization. This paper details the proposal and the preliminary obtained results for clustering real-life datasets (including Iris, Cancer, Newthyroid, Wine, LiverDisorder and Glass) and artificial datasets (including Sph_5_2, Sph_4_3, Sph_6_2, Sph_10_2, Sph_9_2, Pat 1, Pat 2, Long 1, Sizes 5, Spiral, Square 1, Square 4, Twenty and Fourty). Compared with key multi-objectives clustering techniques, the proposal showed interesting results essentially for Iris, Newthyroid, Wine, LiverDisorder, Sph_4_3, Sph_6_2, Long 1, Sizes 5, Twenty and Fourty; and was able to provide the exact number of clusters for all datasets.

scholarly journals Multi-objective Quantum Moth Flame Optimization for Clustering

2020 ◽  
Author(s):  
yassmine Soussi ◽  
Nizar Rokbani ◽  
Ali Wali ◽  
Adel Alimi
Keyword(s):  
Real Life ◽  
Objective Functions ◽  
Cluster Validity ◽  
Number Of Clusters ◽  
Clustering Techniques ◽  
Multi Objective ◽  
Validity Criteria ◽  
Clustering Optimization ◽  
Clustering Problems ◽  
Artificial Datasets

This paper defines a new Moth-Flame optimization version with Quantum behaved moths, QMFO. The multi-objective version of QMFO (MOQMFO) is then applied to solve clustering problems. MOQMFO used three cluster validity criteria as objective functions (the I-index, Con-index and Sym-index) to establish the multi-objective clustering optimization. This paper details the proposal and the preliminary obtained results for clustering real-life datasets (including Iris, Cancer, Newthyroid, Wine, LiverDisorder and Glass) and artificial datasets (including Sph_5_2, Sph_4_3, Sph_6_2, Sph_10_2, Sph_9_2, Pat 1, Pat 2, Long 1, Sizes 5, Spiral, Square 1, Square 4, Twenty and Fourty). Compared with key multi-objectives clustering techniques, the proposal showed interesting results essentially for Iris, Newthyroid, Wine, LiverDisorder, Sph_4_3, Sph_6_2, Long 1, Sizes 5, Twenty and Fourty; and was able to provide the exact number of clusters for all datasets.

Multi-Objective Optimization of Squeeze Casting Process using Evolutionary Algorithms

2016 ◽  
Vol 7 (1) ◽  
pp. 55-74 ◽  
Author(s):  
Manjunath Patel G C ◽  
Prasad Krishna ◽  
Mahesh B. Parappagoudar ◽  
Pandu Ranga Vundavilli
Keyword(s):  
Particle Swarm Optimization ◽  
Evolutionary Algorithms ◽  
Squeeze Casting ◽  
Particle Swarm ◽  
Casting Process ◽  
Process Variable ◽  
Objective Functions ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Squeeze Casting Process

The present work focuses on determining optimum squeeze casting process parameters using evolutionary algorithms. Evolutionary algorithms, such as genetic algorithm, particle swarm optimization, and multi objective particle swarm optimization based on crowing distance mechanism, have been used to determine the process variable combinations for the multiple objective functions. In multi-objective optimization, there are no single optimal process variable combination due to conflicting nature of objective functions. Four cases have been considered after assigning different combination of weights to the individual objective function based on the user importance. Confirmation tests have been conducted for the recommended process variable combinations obtained by genetic algorithm (GA), particle swarm optimization (PSO), and multiple objective particle swarm optimization based on crowing distance (MOPSO-CD). The performance of PSO is found to be comparable with that of GA for identifying optimal process variable combinations. However, PSO outperformed GA with regard to computation time.

Cultural-Based Multi-Objective Particle Swarm Optimization for EEG Channel Reduction in Multi-Class Brain-Computer Interfaces

2012 ◽  
Vol 239-240 ◽  
pp. 1027-1032 ◽  
Author(s):  
Qing Guo Wei ◽  
Yan Mei Wang ◽  
Zong Wu Lu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Real Life ◽  
Particle Swarm ◽  
Channel Selection ◽  
L1 Norm ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Channel Reduction ◽  
Novel Method

Applying many electrodes is undesirable for real-life brain-computer interface (BCI) application since the recording preparation can be troublesome and time-consuming. Multi-objective particle swarm optimization (MOPSO) has been widely utilized to solve multi-objective optimization problems and thus can be employed for channel selection. This paper presented a novel method named cultural-based MOPSO (CMOPSO) for channel selection in motor imagery based BCI. The CMOPSO method introduces a cultural framework to adapt the personalized flight parameters of the mutated particles. A comparison between the proposed algorithm and typical L1-norm algorithm was conducted, and the results showed that the proposed approach is more effective in selecting a smaller subset of channels while maintaining the classification accuracy unreduced.

scholarly journals Optimization using evolutionary metaheuristic techniques: a brief review

2018 ◽  
Vol 15 (1) ◽  
pp. 44-53 ◽  
Author(s):  
Sajja Radhika ◽  
Aparna Chaparala
Keyword(s):  
Optimization Problems ◽  
Real Life ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Life Problems ◽  
Evolutionary Metaheuristic ◽  
Teaching Learning Based Optimization ◽  
Metaheuristic Techniques ◽  
Teaching Learning

Optimization is necessary for finding appropriate solutions to a range of real life problems. Evolutionary-approach-based meta-heuristics have gained prominence in recent years for solving Multi Objective Optimization Problems (MOOP). Multi Objective Evolutionary Approaches (MOEA) has substantial success across a variety of real-world engineering applications. The present paper attempts to provide a general overview of a few selected algorithms, including genetic algorithms, ant colony optimization, particle swarm optimization, and simulated annealing techniques. Additionally, the review is extended to present differential evolution and teaching-learning-based optimization. Few applications of the said algorithms are also presented. This review intends to serve as a reference for further work in this domain.

scholarly journals Distributed Generators Optimization Based on Multi-Objective Functions Using Manta Rays Foraging Optimization Algorithm (MRFO)

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3847
Author(s):  
Mahmoud G. Hemeida ◽  
Salem Alkhalaf ◽  
Al-Attar A. Mohamed ◽  
Abdalla Ahmed Ibrahim ◽  
Tomonobu Senjyu
Keyword(s):  
Optimization Algorithm ◽  
Power Loss ◽  
Stability Index ◽  
Objective Functions ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Voltage Deviation ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Manta Ray

Manta Ray Foraging Optimization Algorithm (MRFO) is a new bio-inspired, meta-heuristic algorithm. MRFO algorithm has been used for the first time to optimize a multi-objective problem. The best size and location of distributed generations (DG) units have been determined to optimize three different objective functions. Minimization of active power loss, minimization of voltage deviation, and maximization of voltage stability index has been achieved through optimizing DG units under different power factor values, unity, 0.95, 0.866, and optimum value. MRFO has been applied to optimize DGs integrated with two well-known radial distribution power systems: IEEE 33-bus and 69-bus systems. The simulation results have been compared to different optimization algorithms in different cases. The results provide clear evidence of the superiority of MRFO that defind before (Manta Ray Foraging Optimization Algorithm. Quasi-Oppositional Differential Evolution Lévy Flights Algorithm (QODELFA), Stochastic Fractal Search Algorithm (SFSA), Genetics Algorithm (GA), Comprehensive Teaching Learning-Based Optimization (CTLBO), Comprehensive Teaching Learning-Based Optimization (CTLBO (ε constraint)), Multi-Objective Harris Hawks Optimization (MOHHO), Multi-Objective Improved Harris Hawks Optimization (MOIHHO), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Particle Swarm Optimization (MOWOA) in terms of power loss, Voltage Stability Index (VSI), and voltage deviation for a wide range of operating conditions. It is clear that voltage buses are improved; and power losses are decreased in both IEEE 33-bus and IEEE 69-bus system for all studied cases. MRFO algorithm gives good results with a smaller number of iterations, which means saving the time required for solving the problem and saving energy. Using the new MRFO technique has a promising future in optimizing different power system problems.

scholarly journals Numerical study and parameter optimization of partial journal bearing using MOPSO algorithm

2019 ◽  
Vol 234 (1) ◽  
pp. 145-158
Author(s):  
SZ Mikaeeli ◽  
C Aghanajafi ◽  
P Akbarzadeh
Keyword(s):  
Particle Swarm Optimization ◽  
Numerical Study ◽  
Particle Swarm ◽  
Journal Bearings ◽  
Fluid Film ◽  
Radial Clearance ◽  
Objective Functions ◽  
Governing Equations ◽  
Swarm Optimization ◽  
Multi Objective

In this paper, multi-objective particle swarm optimization method is developed for optimizing thermo-hydrodynamic journal bearings. This paper focuses on the use of multi-objective particle swarm optimization algorithm with a combination of the thermal hydrodynamic governing equations of the fluid film (i.e. momentum and energy equations) to optimize hydrodynamic partial pad journal bearings and compare with other articles. The governing equations are solved by the central difference method with a successive over-relaxation scheme and the backward difference with an iterative technique. In the paper, the lubricant viscosity changes with the temperature variation in whole fluid film. In this optimization, the bearing power loss, the minimum oil film thickness, and the maximum oil temperature are considered as objective functions and the radial clearance and length to diameter ratio are selected as design variables. The results of the objective functions are compared to other articles. Also, this study discusses the entropy and availability of two concentric cylinders with low curvature and constant wall temperature. Calculations showed that by increasing the Eckert number, the availability increases.

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