scholarly journals Design of Gas Cyclone Using Hybrid Particle Swarm Optimization Algorithm

2021 ◽  
Vol 11 (20) ◽  
pp. 9772
Author(s):  
Xueli Shen ◽  
Daniel C. Ihenacho
Keyword(s):  
Particle Swarm Optimization ◽  
Research Effort ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Design Parameters ◽  
Initial Population ◽  
Swarm Optimization ◽  
Hybrid Particle Swarm Optimization ◽  
Fitness Value ◽  
Sensitivity Approach

The method of searching for an optimal solution inspired by nature is referred to as particle swarm optimization. Differential evolution is a simple but effective EA for global optimization since it has demonstrated strong convergence qualities and is relatively straightforward to comprehend. The primary concerns of design engineers are that the traditional technique used in the design process of a gas cyclone utilizes complex mathematical formulas and a sensitivity approach to obtain relevant optimal design parameters. The motivation of this research effort is based on the desire to simplify complex mathematical models and the sensitivity approach for gas cyclone design with the use of an objective function, which is of the minimization type. The process makes use of the initial population generated by the DE algorithm, and the stopping criterion of DE is set as the fitness value. When the fitness value is not less than the current global best, the DE population is taken over by PSO. For each iteration, the new velocity and position are updated in every generation until the optimal solution is achieved. When using PSO independently, the adoption of a hybridised particle swarm optimization method for the design of an optimum gas cyclone produced better results, with an overall efficiency of 0.70, and with a low cost at the rate of 230 cost/second.

Metaheuristic Optimization of Constrained Large Portfolios using Hybrid Particle Swarm Optimization

2017 ◽  
Vol 8 (1) ◽  
pp. 1-23 ◽  
Author(s):  
G. A. Vijayalakshmi Pai ◽  
Thierry Michel
Keyword(s):  
Particle Swarm Optimization ◽  
Stock Exchange ◽  
Particle Swarm ◽  
Initial Point ◽  
Optimal Solution ◽  
Evolutionary Strategy ◽  
Classical Particle ◽  
Swarm Optimization ◽  
Local Optima ◽  
Hybrid Particle Swarm Optimization

Classical Particle Swarm Optimization (PSO) that has been attempted for the solution of complex constrained portfolio optimization problem in finance, despite its noteworthy track record, suffers from the perils of getting trapped in local optima yielding inferior solutions and unrealistic time estimates for diversification even in medium level portfolio sets. In this work the authors present the solution of the problem using a hybrid PSO strategy. The global best particle position arrived at by the hybrid PSO now acts as the initial point to the Sequential Quadratic Programming (SQP) algorithm which efficiently obtains the optimal solution for even large portfolio sets. The experimental results of the hybrid PSO-SQP model have been demonstrated over Bombay Stock Exchange, India (BSE200 index, Period: July 2001-July 2006) and Tokyo Stock Exchange, Japan (Nikkei225 index, Period: March 2002-March 2007) data sets, and compared with those obtained by Evolutionary Strategy, which belongs to a different genre.

scholarly journals Intelligent discrete particle swarm optimization for multiprocessor task scheduling problem

2016 ◽  
Vol 11 (1) ◽  
pp. 58-67 ◽  
Author(s):  
S Sarathambekai ◽  
K Umamaheswari
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Initial Population ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Discrete Particle

Discrete particle swarm optimization is one of the most recently developed population-based meta-heuristic optimization algorithm in swarm intelligence that can be used in any discrete optimization problems. This article presents a discrete particle swarm optimization algorithm to efficiently schedule the tasks in the heterogeneous multiprocessor systems. All the optimization algorithms share a common algorithmic step, namely population initialization. It plays a significant role because it can affect the convergence speed and also the quality of the final solution. The random initialization is the most commonly used method in majority of the evolutionary algorithms to generate solutions in the initial population. The initial good quality solutions can facilitate the algorithm to locate the optimal solution or else it may prevent the algorithm from finding the optimal solution. Intelligence should be incorporated to generate the initial population in order to avoid the premature convergence. This article presents a discrete particle swarm optimization algorithm, which incorporates opposition-based technique to generate initial population and greedy algorithm to balance the load of the processors. Make span, flow time, and reliability cost are three different measures used to evaluate the efficiency of the proposed discrete particle swarm optimization algorithm for scheduling independent tasks in distributed systems. Computational simulations are done based on a set of benchmark instances to assess the performance of the proposed algorithm.

scholarly journals Particle swarm optimization to produce optimal solution

2018 ◽  
Vol 7 (1.7) ◽  
pp. 210 ◽  
Author(s):  
C Saranya Jothi ◽  
V Usha ◽  
R Nithya
Keyword(s):  
Particle Swarm Optimization ◽  
Test Data ◽  
Fitness Function ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Optimization Techniques ◽  
Optimal Test ◽  
Swarm Optimization ◽  
Test Information ◽  
Fitness Value

Search-Based Software Testing is the utilization of a meta-heuristic improving scan procedure for the programmed age of test information. Particle Swarm Optimization (PSO) is one of those technique. It can be used in testing to generate optimal test data solution based on an objective function that utilises branch coverage as criteria. Software under test is given as input to the algorithm. The problem becomes a minimization problem where our aim is to obtain test data with minimum fitness value. This is called the ideal test information for the given programming under test. PSO algorithm is found to outperform most of the optimization techniques by finding least value for fitness function. The algorithm is applied to various software under tests and checked whether it can produce optimal test data. Parameters are tuned so as to obtain better results.

scholarly journals A Novel Particle Swarm Optimization with Genetic Operator and Its Application to TSP

2021 ◽  
Vol 15 (4) ◽  
pp. 0-0
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Population Diversity ◽  
Benchmark Problems ◽  
Genetic Operators ◽  
Swarm Optimization ◽  
Genetic Operator ◽  
Hybrid Particle Swarm Optimization ◽  
The Traveling Salesman Problem

To solve some problems of particle swarm optimization, such as the premature convergence and falling into a sub-optimal solution easily, we introduce the probability initialization strategy and genetic operator into the particle swarm optimization algorithm. Based on the hybrid strategies, we propose a improved hybrid particle swarm optimization, namely IHPSO, for solving the traveling salesman problem. In the IHPSO algorithm, the probability strategy is utilized into population initialization. It can save much more computing resources during the iteration procedure of the algorithm. Furthermore, genetic operators, including two kinds of crossover operator and a directional mutation operator, are used for improving the algorithm’s convergence accuracy and population diversity. At last, the proposed method is benchmarked on 9 benchmark problems in TSPLIB and the results are compared with 4 competitors. From the results, it is observed that the proposed approach significantly outperforms others on most the 9 datasets.

scholarly journals Application of Hybrid Particle Swarm Optimization Algorithm in the Optimal Water Operation of Cascade Reservoirs in Dry Season

2018 ◽  
Vol 246 ◽  
pp. 01064 ◽  
Author(s):  
Sen Wang ◽  
Fang Yang ◽  
Zhipeng Ma ◽  
Shanzong Li ◽  
Yunyun Shi
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Dry Season ◽  
Weighting Coefficient ◽  
Initial Population ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Cascade Reservoirs ◽  
Hybrid Particle ◽  
Hybrid Particle Swarm Optimization

In this paper, a hybrid particle swarm optimization (HPSO) algorithm is proposed to solve the problem of optimal water operation of cascade reservoirs in dry season. Based on the basic particle swarm optimization (PSO) algorithm, chaos algorithm is introduced to traverse the search space to generate the initial population and improve the global searching ability of the algorithm. A self-adaptive inertial weighting method based on optimized inertial weighting coefficient is adopted to improve the ability of particle individual search and avoid local optimum. The proposed algorithm is applied to the optimal water operation in dry season of cascade reservoirs on the mainstream of Xijiang River. The results show that the HPSO algorithm can effectively improve the guarantee degree of ecological flow and suppressing salinity flow in the control reach of Wuzhou station under different typical dry year scenarios.

A Hybrid Meta-Heuristic Approach for QoS-Aware Cloud Service Composition

2018 ◽  
Vol 15 (2) ◽  
pp. 1-20 ◽  
Author(s):  
S. Bharath Bhushan ◽  
Pradeep C. H. Reddy
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Fruit Fly ◽  
Convergence Speed ◽  
Optimization Techniques ◽  
Cloud Services ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Hybrid Particle Swarm Optimization ◽  
Fitness Value

Cloud is evolving as an outstanding platform to deliver cloud services on a pay-as-you-go basis. The selection and composition of cloud services based on QoS criteria is formulated as NP hard optimization problem. Traditionally, many optimization techniques are applied to solve it, but it suffers from slow convergence speed, large number of calculations, and falling into local optimum. This article proposes a hybrid particle swarm optimization (HPSO) technique that combines particle swarm optimization (PSO) and fruit fly (FOA) to perform the evolutionary search process. The following determines a pareto optimal service set which is non-dominated solution set as input to the proposed HPSO. In the proposed HPSO, the parameters such as position and velocity are redefined, and while updating, the smell operator of fruit fly is used to overcome the prematurity of PSO. The FOA enhances the convergence speed with good fitness value. The experimental results show that the proposed HPSO outperforms the simple particle swarm optimization in terms of fitness value, execution time, and error rate.

Weight Coefficient Setting of Current Predictive Control for Permanent Magnet Synchronous Machine using HPSO

2021 ◽  
Vol 69 (4) ◽  
pp. 17-25
Author(s):  
Yinhang Luo ◽  
◽  
Fengyang Gao ◽  
Kaiwen Yang ◽  
◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Permanent Magnet ◽  
Predictive Control ◽  
Particle Swarm ◽  
Weight Coefficient ◽  
Switching Frequency ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Hybrid Particle Swarm Optimization ◽  
Fitness Value

Aiming at the problem of multi-objective weight coefficient setting of model predictive control (MPC) for permanent magnet synchronous motor (PMSM), a hybrid particle swarm optimization (HPSO) algorithm with low computational complexity of fitness value is proposed to realize the self-setting of weight coefficient of cost function. In the proposed strategy, good particles update velocity and position through particle swarm optimization (PSO) algorithm, while bad particles not only do the same but generate the offspring by cross and mutation, and then the worse offspring will be replaced by their extremum individuals. It is faster that the adaptive cross and mutation rate makes the offspring get closer to the good particles, and it increases the diversity of particles without destroying the good particles. Experimental results show that compared with other optimization algorithms, the proposed algorithm. Firstly, is more inclined to escape from the local optimum. Secondly, it has higher search accuracy and faster convergence speed. Moreover, with setting weight coefficient, the system speed regulation time is shortened, the current total harmonic distortion (THD) is reduced significantly, and the switching frequency is effectively reduced without affecting the output power quality.

scholarly journals A Novel Particle Swarm Optimization With Genetic Operator and Its Application to TSP

2021 ◽  
Vol 15 (4) ◽  
pp. 1-17
Author(s):  
Bo Wei ◽  
Ying Xing ◽  
Xuewen Xia ◽  
Ling Gui
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Population Diversity ◽  
Benchmark Problems ◽  
Genetic Operators ◽  
Swarm Optimization ◽  
Genetic Operator ◽  
Hybrid Particle Swarm Optimization ◽  
The Traveling Salesman Problem

To solve some problems of particle swarm optimization, such as the premature convergence and falling into a sub-optimal solution easily, we introduce the probability initialization strategy and genetic operator into the particle swarm optimization algorithm. Based on the hybrid strategies, we propose a improved hybrid particle swarm optimization, namely IHPSO, for solving the traveling salesman problem. In the IHPSO algorithm, the probability strategy is utilized into population initialization. It can save much more computing resources during the iteration procedure of the algorithm. Furthermore, genetic operators, including two kinds of crossover operator and a directional mutation operator, are used for improving the algorithm’s convergence accuracy and population diversity. At last, the proposed method is benchmarked on 9 benchmark problems in TSPLIB and the results are compared with 4 competitors. From the results, it is observed that the proposed approach significantly outperforms others on most the 9 datasets.

scholarly journals Optimized Production of Biodiesel Using Internet of Things Sensed Temperature with Hybrid Particle Swarm Optimization

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
G. Loganathan ◽  
M. Kannan
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Cost Effective ◽  
Biodiesel Production ◽  
Biofuel Production ◽  
Swarm Optimization ◽  
Hybrid Particle ◽  
Hybrid Particle Swarm Optimization ◽  
Fitness Value ◽  
Increase Productivity

Biofuel production offers a non-fossil fuel that can be utilized in modern engines without any redesign. Regardless of receiving rising attention, many researchers have explored microalgae-based biofuel production and found biodiesel production is cost-effective compared to petroleum-centered conventional fuels. The primary reason is that the lipid accumulation of microalgae is possible. An efficient technique is proposed for optimized biodiesel manufacturing with microalgae through an IoT device with the hybrid particle swarm optimization (HPSO) algorithm for elapsing such drawbacks. First, the component of biodiesel is determined. Then, from the components, the temperature value is sensed through the IoT device. Based on the obtained temperature, the reaction parameters are optimized with HPSO to increase productivity and reduce cost. Finally, we observed performance and comparative analysis. The experimental results contrasted with the existent particle swarm optimization (PSO) and genetic algorithm (GA) concerning iteration’s temperature, concentration, production, and fitness. The present HPSO algorithm has differed from the existing PSO and GA concerning IoT sensed temperature and production function. Fitness value and instance concentration are the performance parameters. It varies based on the iteration values. Thus, the proposed optimized biodiesel production is advanced when weighed down with the top-notch methods.

scholarly journals Optimal Synthesis of six bar Mechanism using Particle Swarm Optimization

2020 ◽  
Vol 8 (6) ◽  
pp. 5287-5292
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Optimal Synthesis ◽  
Design Parameters ◽  
Swarm Optimization ◽  
Circular Arcs ◽  
Final Design ◽  
Evolutionary Technique ◽  
The Given

This paper presents the optimal synthesis of a six-bar Watt-II mechanism with rotational joints in which a tracing point on the coupler traces a desired path. The objective of the work is to synthesize a six-bar mechanism whose coupler point generates the path as close to the given path as possible. The tracing path is the combination of two circular arcs with eighteen precision points with prescribed timing. A mathematical model is formed by two vector loop closure equations, and design constrained are imposed to improve the result quality. ‘Particle Swarm Optimization’ (PSO), an evolutionary technique, is used to obtain the optimal solution of the given six bar mechanism at first time, which has been used by various authors in case of four bar mechanism. Final design parameters and convergence rate of the optimal solutions are presented.

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