Comparison in the application of Multi-objective Genetic Algorithm, Particle Swarm Optimization and Firefly Algorithm in unbalance identification in rotating machinery.

2015 ◽  
Author(s):  
Andrei Bavaresco Rezende ◽  
Cassio Pereira de Paula ◽  
Helio Fiori de Castro
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Firefly Algorithm ◽  
Particle Swarm ◽  
Rotating Machinery ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

scholarly journals Enhanced Feature Subset Selection Using Niche Based Bat Algorithm

Computation ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 49 ◽  
Author(s):  
Noman Saleem ◽  
Kashif Zafar ◽  
Alizaa Sabzwari
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Bat Algorithm ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
Relevant Feature ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

Redundant and irrelevant features disturb the accuracy of the classifier. In order to avoid redundancy and irrelevancy problems, feature selection techniques are used. Finding the most relevant feature subset that can enhance the accuracy rate of the classifier is one of the most challenging parts. This paper presents a new solution to finding relevant feature subsets by the niche based bat algorithm (NBBA). It is compared with existing state of the art approaches, including evolutionary based approaches. The multi-objective bat algorithm (MOBA) selected 8, 16, and 248 features with 93.33%, 93.54%, and 78.33% accuracy on ionosphere, sonar, and Madelon datasets, respectively. The multi-objective genetic algorithm (MOGA) selected 10, 17, and 256 features with 91.28%, 88.70%, and 75.16% accuracy on same datasets, respectively. Finally, the multi-objective particle swarm optimization (MOPSO) selected 9, 21, and 312 with 89.52%, 91.93%, and 76% accuracy on the above datasets, respectively. In comparison, NBBA selected 6, 19, and 178 features with 93.33%, 95.16%, and 80.16% accuracy on the above datasets, respectively. The niche multi-objective genetic algorithm selected 8, 15, and 196 features with 93.33%, 91.93%, and 79.16 % accuracy on the above datasets, respectively. Finally, the niche multi-objective particle swarm optimization selected 9, 19, and 213 features with 91.42%, 91.93%, and 76.5% accuracy on the above datasets, respectively. Hence, results show that MOBA outperformed MOGA and MOPSO, and NBBA outperformed the niche multi-objective genetic algorithm and the niche multi-objective particle swarm optimization.

Multi-objective integrated planning and scheduling model for operating rooms under uncertainty

2018 ◽  
Vol 232 (9) ◽  
pp. 930-948 ◽  
Author(s):  
Javad Ansarifar ◽  
Reza Tavakkoli-Moghaddam ◽  
Faezeh Akhavizadegan ◽  
Saman Hassanzadeh Amin
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Particle Swarm Optimization ◽  
Heuristic Algorithms ◽  
Particle Swarm ◽  
Operating Rooms ◽  
Programming Approach ◽  
Test Problems ◽  
Swarm Optimization ◽  
Multi Objective

This article formulates the operating rooms considering several constraints of the real world, such as decision-making styles, multiple stages for surgeries, time windows for resources, and specialty and complexity of surgery. Based on planning, surgeries are assigned to the working days. Then, the scheduling part determines the sequence of surgeries per day. Moreover, an integrated fuzzy possibilistic–stochastic mathematical programming approach is applied to consider some sources of uncertainty, simultaneously. Net revenues of operating rooms are maximized through the first objective function. Minimizing a decision-making style inconsistency among human resources and maximizing utilization of operating rooms are considered as the second and third objectives, respectively. Two popular multi-objective meta-heuristic algorithms including Non-dominated Sorting Genetic Algorithm and Multi-Objective Particle Swarm Optimization are utilized for solving the developed model. Moreover, different comparison metrics are applied to compare the two proposed meta-heuristics. Several test problems based on the data obtained from a public hospital located in Iran are used to display the performance of the model. According to the results, Non-dominated Sorting Genetic Algorithm-II outperforms the Multi-Objective Particle Swarm Optimization algorithm in most of the utilized metrics. Moreover, the results indicate that our proposed model is more effective and efficient to schedule and plan surgeries and assign resources than manual scheduling.

A Multi-Objective Hybrid Algorithm for Optimization of Grid Structures

2018 ◽  
Vol 10 (01) ◽  
pp. 1850009 ◽  
Author(s):  
Zhe Xiong ◽  
Xiao-Hui Li ◽  
Jing-Chang Liang ◽  
Li-Juan Li
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Convergence Rate ◽  
Pareto Optimality ◽  
Hybrid Algorithm ◽  
Particle Swarm ◽  
Mathematical Functions ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Structural Problems

In this study, a novel multi-objective hybrid algorithm (MHGH, multi-objective HPSO-GA hybrid algorithm) is developed by crossing the heuristic particle swarm optimization (HPSO) algorithm with a genetic algorithm (GA) based on the concept of Pareto optimality. To demonstrate the effectiveness of the MHGH, the optimizations of four unconstrained mathematical functions and four constrained truss structural problems are tested and compared to the results using several other classic algorithms. The results show that the MHGH improves the convergence rate and precision of the particle swarm optimization (PSO) and increases its robustness.

Multi-Objective Particle Swarm Optimization for Control Laws Design

2013 ◽  
Vol 333-335 ◽  
pp. 1361-1365
Author(s):  
Xiao Xiong Liu ◽  
Heng Xu ◽  
Yan Wu ◽  
Peng Hui Li
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Tracking Error ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Control Laws ◽  
Reference Models ◽  
Multi Objective ◽  
Simulation Results

In order to overcome the difficult of large amount of calculation and to satisfy multiple design indicators in the design of control laws, an improved multi-objective particle swarm optimization (PSO) algorithm was used to design control laws of aircraft. Firstly, the hybrid concepts of genetic algorithm were introduced to particle swarm optimization (PSO) algorithm to improve the algorithm. Then based on aircraft flying quality the reference models were built, and then the tracking error, settling time and overshoot were used as the optimization goal of the control laws design. Based on this multi-objective optimize problem the attitude hold control laws were designed. The simulation results show the effectiveness of the algorithm.

scholarly journals Comparison of Hybrid Firefly Algorithms for Binary and Continuous Optimization Problems in a TV White Space Network

2020 ◽  
Vol 19 ◽  
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Power Allocation ◽  
Firefly Algorithm ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Continuous Optimization ◽  
Spectrum Allocation ◽  
Swarm Optimization ◽  
Continuous Optimization Problem

TV white spaces (TVWS) can be used by Secondary Users (SUs) through Dynamic Spectrum Access (DSA) as long as they do not cause harmful interference to Primary Users (PUs). Due to spectrum scarcity, there is increasing demand for DSA. When there is a high density of SUs in a TVWS network such as cellular access to TVWS, problem of interference among SUs will arise. Possibility of harmful interference to PUs may also arise. Power and spectrum allocation optimization is therefore necessary to reduce the level of interference among SUs and to protect PUs against harmful interference. In this paper different hybrid firefly algorithms with particle swarm optimization and genetic algorithm for optimization of spectrum allocation in a TVWS network as a discrete optimization problem and that of power allocation as a continuous optimization problem are compared. Simulation was done using Matlab. Simulation results show that hybrid firefly algorithm with genetic algorithm outperforms other hybrid firefly algorithms for spectrum allocation. On the other hand, hybrid firefly algorithm with genetic algorithm and particle swarm optimization outperforms all other algorithms for power allocation as continuous optimization problem

Four-Variable Simultaneous Optimization of the Cooling and Acoustic Power with Particle Swarm Optimization

2020 ◽  
Vol 28 (02) ◽  
pp. 2050012
Author(s):  
It Sing Chan ◽  
Normah Mohd Ghazali ◽  
Nor Atiqah Zolpakar ◽  
Maziah Mohamad
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Acoustic Power ◽  
Simultaneous Optimization ◽  
Cooling Power ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Thermoacoustic Refrigerator

The low performance of the thermoacoustic refrigerator has made it uncompetitive to currently available refrigeration systems and hence its path towards commercialization is being restricted. Recently, evolutionary algorithm such as genetic algorithm has become popular among researchers in optimizing the performance of the thermoacoustic refrigerator due to its capability to provide a solution with a global maximum or minimum through simultaneous optimization of several objectives. The purpose of this study was to maximize the performance of the thermoacoustic refrigerator using the Multi-Objective Particle Swarm Optimization (MOPSO), an evolutionary optimization tool that has not been tried in this field before. By optimizing the two conflicting objectives which are maximizing the cooling power and minimizing the acoustic power required, simultaneous optimization of inter-dependent controlling parameters has been performed for two, three and four parameters. Comparing with the results of past studies, MOPSO has improved the stack COP by 6.92% compared to the parametric optimization approach and 2.96% higher than the maximum COP achieved by multi-objective genetic algorithm (MOGA) with an optimum COP of 1.39. Also, a maximum cooling power of 10.8 W was obtained. This study has highlighted the potential of MOPSO in providing optimized conditions for conflicting objectives desired for a thermoacoustic system.

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