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 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 Particle Swarm Optimization Combined with Inertia-Free Velocity and Direction Search

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 597
Author(s):  
Kun Miao ◽  
Qian Feng ◽  
Wei Kuang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Small Region ◽  
Benchmark Problems ◽  
Swarm Optimization ◽  
Local Searches ◽  
Engineering Design Problems ◽  
Direction Search

The particle swarm optimization algorithm (PSO) is a widely used swarm-based natural inspired optimization algorithm. However, it suffers search stagnation from being trapped into a sub-optimal solution in an optimization problem. This paper proposes a novel hybrid algorithm (SDPSO) to improve its performance on local searches. The algorithm merges two strategies, the static exploitation (SE, a velocity updating strategy considering inertia-free velocity), and the direction search (DS) of Rosenbrock method, into the original PSO. With this hybrid, on the one hand, extensive exploration is still maintained by PSO; on the other hand, the SE is responsible for locating a small region, and then the DS further intensifies the search. The SDPSO algorithm was implemented and tested on unconstrained benchmark problems (CEC2014) and some constrained engineering design problems. The performance of SDPSO is compared with that of other optimization algorithms, and the results show that SDPSO has a competitive performance.

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 A New Auto Adaptive Fuzzy Hybrid Particle Swarm Optimization and Genetic Algorithm

2020 ◽  
Vol 10 (2) ◽  
pp. 95-111 ◽  
Author(s):  
Piotr Dziwiński ◽  
Łukasz Bartczuk ◽  
Józef Paszkowski
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Landscape ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Fuzzy Rules ◽  
Local Optimum ◽  
Genetic Operators ◽  
Adaptation Mechanism ◽  
Swarm Optimization ◽  
Hybrid Particle Swarm Optimization

AbstractThe social learning mechanism used in the Particle Swarm Optimization algorithm allows this method to converge quickly. However, it can lead to catching the swarm in the local optimum. The solution to this issue may be the use of genetic operators whose random nature allows them to leave this point. The degree of use of these operators can be controlled using a neuro-fuzzy system. Previous studies have shown that the form of fuzzy rules should be adapted to the fitness landscape of the problem. This may suggest that in the case of complex optimization problems, the use of different systems at different stages of the algorithm will allow to achieve better results. In this paper, we introduce an auto adaptation mechanism that allows to change the form of fuzzy rules when solving the optimization problem. The proposed mechanism has been tested on benchmark functions widely adapted in the literature. The results verify the effectiveness and efficiency of this solution.

scholarly journals An optimal solution for software testing case generation based on particle swarm optimization

Open Physics ◽  
2018 ◽  
Vol 16 (1) ◽  
pp. 355-363 ◽  
Author(s):  
Shi Jianqi ◽  
Huang Yanhong ◽  
Li Ang ◽  
Cai Fangda
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Software Testing ◽  
Fitness Function ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Population Diversity ◽  
Generation Efficiency ◽  
Swarm Optimization ◽  
Testing Case

AbstractSearching based testing case generation technology converts the problem of testing case generation to function optimizations, through a fitness function, which is usually optimized using heuristic search algorithms. The particle swarm optimization (PSO) optimized testing case generation algorithm tends to lose population diversity of locally optimal solutions with low accuracy of local search. To overcome the above defects, a self-adaptive PSO based software testing case optimization algorithm is proposed. It adjusts the inertia weight dynamically according to the current iteration and average relative speed, to improve the performance of standard PSO. An improved alternating variable method is put forward to accelerate local search speed, which can coordinate both global and local search ability thereby improving the overall generation efficiency of testing cases. The experimental results demonstrate that the approach outlined here keeps higher testing case generation efficiency, and it shows certain advantages in coverage, evolution generation amount and running time when compared to standard PSO and GA-PSO.

Dynamic Small World Network Topology for Particle Swarm Optimization

2016 ◽  
Vol 30 (09) ◽  
pp. 1660009 ◽  
Author(s):  
Qingxue Liu ◽  
Barend Jacobus van Wyk ◽  
Shengzhi Du ◽  
Yanxia Sun
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Small World ◽  
Population Diversity ◽  
Benchmark Problems ◽  
Small World Network ◽  
Exploration And Exploitation ◽  
Swarm Optimization ◽  
Dynamic Topology ◽  
Neighborhood Topology

A new particle optimization algorithm with dynamic topology is proposed based on small world network. The technique imitates the dissemination of information in a small world network by dynamically updating the neighborhood topology of the Particle Swarm Optimization (PSO). In comparison with other four classic topologies and two PSO algorithms based on small world network, the proposed dynamic neighborhood strategy is more effective in coordinating the exploration and exploitation ability of PSO. Simulations demonstrated that the convergence of the swarms is faster than its competitors. Meanwhile, the proposed method maintains population diversity and enhances the global search ability for a series of benchmark problems.

scholarly journals Hybrid Particle Swarm Optimization Algorithm for Process Planning

Mathematics ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1745
Author(s):  
Xu Zhang ◽  
Pan Guo ◽  
Hua Zhang ◽  
Jin Yao
Keyword(s):  
Particle Swarm Optimization ◽  
Process Planning ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Genetic Operators ◽  
Swarm Optimization ◽  
Hybrid Particle ◽  
Hierarchical Coding ◽  
Hybrid Particle Swarm Optimization

Process planning is a typical combinatorial optimization problem. When the scale of the problem increases, combinatorial explosion occurs, which makes it difficult for traditional precise algorithms to solve the problem. A hybrid particle swarm optimization (HPSO) algorithm is proposed in this paper to solve problems of process planning. A hierarchical coding method including operation layer, machine layer and logic layer is designed in this algorithm. Each layer of coding corresponds to the decision of a sub-problem of process planning. Several genetic operators of the genetic algorithm are designed to replace the update formula of particle position and velocity in the particle swarm optimization algorithm. The results of the benchmark example in case study show that the algorithm proposed in this paper has better performance.

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