scholarly journals A Hybrid Multi-Step Probability Selection Particle Swarm Optimization with Dynamic Chaotic Inertial Weight and Acceleration Coefficients for Numerical Function Optimization

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 922 ◽  
Author(s):  
Yuji Du ◽  
Fanfan Xu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Early Stage ◽  
Particle Swarm ◽  
Function Optimization ◽  
Local Optimum ◽  
Numerical Function ◽  
Swarm Optimization ◽  
Roulette Wheel Selection ◽  
Search Capability

As a meta-heuristic algoriTthm, particle swarm optimization (PSO) has the advantages of having a simple principle, few required parameters, easy realization and strong adaptability. However, it is easy to fall into a local optimum in the early stage of iteration. Aiming at this shortcoming, this paper presents a hybrid multi-step probability selection particle swarm optimization with sine chaotic inertial weight and symmetric tangent chaotic acceleration coefficients (MPSPSO-ST), which can strengthen the overall performance of PSO to a large extent. Firstly, we propose a hybrid multi-step probability selection update mechanism (MPSPSO), which skillfully uses a multi-step process and roulette wheel selection to improve the performance. In order to achieve a good balance between global search capability and local search capability to further enhance the performance of the method, we also design sine chaotic inertial weight and symmetric tangent chaotic acceleration coefficients inspired by chaos mechanism and trigonometric functions, which are integrated into the MPSPSO-ST algorithm. This strategy enables the diversity of the swarm to be preserved to discourage premature convergence. To evaluate the effectiveness of the MPSPSO-ST algorithm, we conducted extensive experiments with 20 classic benchmark functions. The experimental results show that the MPSPSO-ST algorithm has faster convergence speed, higher optimization accuracy and better robustness, which is competitive in solving numerical optimization problems and outperforms a lot of classical PSO variants and well-known optimization algorithms.

scholarly journals Global Particle Swarm Optimization for High Dimension Numerical Functions Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
J. J. Jamian ◽  
M. N. Abdullah ◽  
H. Mokhlis ◽  
M. W. Mustafa ◽  
A. H. A. Bakar
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Function Optimization ◽  
High Dimensional ◽  
Local Optimum ◽  
Mathematical Function ◽  
Numerical Function ◽  
Swarm Optimization ◽  
Particle Position

The Particle Swarm Optimization (PSO) Algorithm is a popular optimization method that is widely used in various applications, due to its simplicity and capability in obtaining optimal results. However, ordinary PSOs may be trapped in the local optimal point, especially in high dimensional problems. To overcome this problem, an efficient Global Particle Swarm Optimization (GPSO) algorithm is proposed in this paper, based on a new updated strategy of the particle position. This is done through sharing information of particle position between the dimensions (variables) at any iteration. The strategy can enhance the exploration capability of the GPSO algorithm to determine the optimum global solution and avoid traps at the local optimum. The proposed GPSO algorithm is validated on a 12-benchmark mathematical function and compared with three different types of PSO techniques. The performance of this algorithm is measured based on the solutions’ quality, convergence characteristics, and their robustness after 50 trials. The simulation results showed that the new updated strategy in GPSO assists in realizing a better optimum solution with the smallest standard deviation value compared to other techniques. It can be concluded that the proposed GPSO method is a superior technique for solving high dimensional numerical function optimization problems.

Improved Particle Swarm Optimization Algorithm and its Application Based on the Aggregation Degree

2013 ◽  
Vol 427-429 ◽  
pp. 1934-1938
Author(s):  
Zhong Rong Zhang ◽  
Jin Peng Liu ◽  
Ke De Fei ◽  
Zhao Shan Niu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Population Diversity ◽  
Function Optimization ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

The aim is to improve the convergence of the algorithm, and increase the population diversity. Adaptively particles of groups fallen into local optimum is adjusted in order to realize global optimal. by judging groups spatial location of concentration and fitness variance. At the same time, the global factors are adjusted dynamically with the action of the current particle fitness. Four typical function optimization problems are drawn into simulation experiment. The results show that the improved particle swarm optimization algorithm is convergent, robust and accurate.

scholarly journals Improved Chaotic Particle Swarm Optimization Algorithm with More Symmetric Distribution for Numerical Function Optimization

Symmetry ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 876 ◽  
Author(s):  
Ma ◽  
Yuan ◽  
Han ◽  
Sun ◽  
Ma
Keyword(s):  
Particle Swarm Optimization ◽  
Numerical Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Function Optimization ◽  
Numerical Function ◽  
Swarm Optimization ◽  
Chaotic Particle Swarm Optimization ◽  
Chaotic Pso

As a global-optimized and naturally inspired algorithm, particle swarm optimization (PSO) is characterized by its high quality and easy application in practical optimization problems. However, PSO has some obvious drawbacks, such as early convergence and slow convergence speed. Therefore, we introduced some appropriate improvements to PSO and proposed a novel chaotic PSO variant with arctangent acceleration coefficient (CPSO-AT). A total of 10 numerical optimization functions were employed to test the performance of the proposed CPSO-AT algorithm. Extensive contrast experiments were conducted to verify the effectiveness of the proposed methodology. The experimental results showed that the proposed CPSO-AT algorithm converges quickly and has better stability in numerical optimization problems compared with other PSO variants and other kinds of well-known optimal algorithms.

scholarly journals Particle Swarm and Bacterial Foraging Inspired Hybrid Artificial Bee Colony Algorithm for Numerical Function Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Li Mao ◽  
Yu Mao ◽  
Changxi Zhou ◽  
Chaofeng Li ◽  
Xiao Wei ◽  
...  
Keyword(s):  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Function Optimization ◽  
Local Optimum ◽  
Search Range ◽  
Bacterial Foraging Optimization ◽  
Numerical Function ◽  
Bacterial Foraging ◽  
Bee Colony

Artificial bee colony (ABC) algorithm has good performance in discovering the optimal solutions to difficult optimization problems, but it has weak local search ability and easily plunges into local optimum. In this paper, we introduce the chemotactic behavior of Bacterial Foraging Optimization into employed bees and adopt the principle of moving the particles toward the best solutions in the particle swarm optimization to improve the global search ability of onlooker bees and gain a hybrid artificial bee colony (HABC) algorithm. To obtain a global optimal solution efficiently, we make HABC algorithm converge rapidly in the early stages of the search process, and the search range contracts dynamically during the late stages. Our experimental results on 16 benchmark functions of CEC 2014 show that HABC achieves significant improvement at accuracy and convergence rate, compared with the standard ABC, best-so-far ABC, directed ABC, Gaussian ABC, improved ABC, and memetic ABC algorithms.

scholarly journals A Novel Hybrid Particle Swarm Optimization and Sine Cosine Algorithm for Seismic Optimization of Retaining Structures

2021 ◽  
Author(s):  
Mohammad Khajehzadeh ◽  
Alireza Sobhani ◽  
Seyed Mehdi Seyed Alizadeh ◽  
Mahdiyeh Eslami
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Function Optimization ◽  
Numerical Function ◽  
Swarm Optimization ◽  
Retaining Structures ◽  
Hybrid Particle Swarm Optimization ◽  
Dynamic Loading Condition ◽  
Sine Cosine Algorithm ◽  
Competitive Algorithms

This study introduces an effective hybrid optimization algorithm, namely Particle Swarm Sine Cosine Algorithm (PSSCA) for numerical function optimization and automating optimum design of retaining structures under seismic loads. The new algorithm employs the dynamic behavior of sine and cosine functions in the velocity updating operation of particle swarm optimization (PSO) to achieve faster convergence and better accuracy of final solution without getting trapped in local minima. The proposed algorithm is tested over a set of 16 benchmark functions and the results are compared with other well-known algorithms in the field of optimization. For seismic optimization of retaining structure, Mononobe-Okabe method is employed for dynamic loading condition and total construction cost of the structure is considered as the objective function. Finally, optimization of two retaining structures under static and seismic loading are considered from the literature. As results demonstrate, the PSSCA is superior and it could generate better optimal solutions compared with other competitive algorithms.

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 A PSO based approach: Scout particle swarm algorithm for continuous global optimization problems

2018 ◽  
Vol 6 (2) ◽  
pp. 129-142 ◽  
Author(s):  
Hasan Koyuncu ◽  
Rahime Ceylan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Function Optimization ◽  
Optimal Solutions ◽  
Swarm Optimization ◽  
Hybrid Approaches ◽  
External Intervention ◽  
Abc Methods

Abstract In the literature, most studies focus on designing new methods inspired by biological processes, however hybridization of methods and hybridization way should be examined carefully to generate more suitable optimization methods. In this study, we handle Particle Swarm Optimization (PSO) and an efficient operator of Artificial Bee Colony Optimization (ABC) to design an efficient technique for continuous function optimization. In PSO, velocity and position concepts guide particles to achieve convergence. At this point, variable and stable parameters are ineffective for regenerating awkward particles that cannot improve their personal best position (Pbest). Thus, the need for external intervention is inevitable once a useful particle becomes an awkward one. In ABC, the scout bee phase acts as external intervention by sustaining the resurgence of incapable individuals. With the addition of a scout bee phase to standard PSO, Scout Particle Swarm Optimization (ScPSO) is formed which eliminates the most important handicap of PSO. Consequently, a robust optimization algorithm is obtained. ScPSO is tested on constrained optimization problems and optimum parameter values are obtained for the general use of ScPSO. To evaluate the performance, ScPSO is compared with Genetic Algorithm (GA), with variants of the PSO and ABC methods, and with hybrid approaches based on PSO and ABC algorithms on numerical function optimization. As seen in the results, ScPSO results in better optimal solutions than other approaches. In addition, its convergence is superior to a basic optimization method, to the variants of PSO and ABC algorithms, and to the hybrid approaches on different numerical benchmark functions. According to the results, the Total Statistical Success (TSS) value of ScPSO ranks first (5) in comparison with PSO variants; the second best TSS (2) belongs to CLPSO and SP-PSO techniques. In a comparison with ABC variants, the best TSS value (6) is obtained by ScPSO, while TSS of BitABC is 2. In comparison with hybrid techniques, ScPSO obtains the best Total Average Rank (TAR) as 1.375, and TSS of ScPSO ranks first (6) again. The fitness values obtained by ScPSO are generally more satisfactory than the values obtained by other methods. Consequently, ScPSO achieve promising gains over other optimization methods; in parallel with this result, its usage can be extended to different working disciplines. Highlights PSO parameters are ineffective to regenerate the awkward particle that cannot improve its pbest. An external intervention is inevitable once a particle becomes an awkward one. ScPSO is obtained with the addition of scout bee phase into the PSO. So an evolutionary method eliminating the most important handicap of PSO is gained. ScPSO is compared with the variants and with hybrid versions of PSO and ABC methods. According to the experiments, ScPSO results in better optimal solutions. The fitness values of ScPSO are generally more satisfactory than the others. Consequently, ScPSO achieve promising gains over other optimization methods. In parallel with this, its usage can be extended to different working disciplines.

MEMS Relay Optimization Design Algorithm Based on Particle Swarm Optimization

2013 ◽  
Vol 562-565 ◽  
pp. 155-161
Author(s):  
Han Min Liu ◽  
Qing Hua Wu ◽  
Xue Song Yan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Optimization Design ◽  
Particle Swarm ◽  
Nonlinear Function ◽  
Function Optimization ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Design Algorithm ◽  
Nonlinear Function Optimization

Mathematical model of the MEMS relay volume involves in mechanical, electrical, magnetic, thermal, etc., the MEMS relay optimization design is a constrained nonlinear function optimization problem. In this paper, aim at the disadvantages of standard Particle Swarm Optimization algorithm like being trapped easily into a local optimum, we improves the standard PSO and proposes a new algorithm to solve the overcomes of the standard PSO. The new algorithm keeps not only the fast convergence speed characteristic of PSO, but effectively improves the capability of global searching as well. Experiment results reveal that the proposed algorithm can find better solutions when compared to other heuristic methods and is a powerful optimization algorithm for MEMS relay optimization design.

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