Taguchi-Particle Swarm Optimization for Numerical Optimization

2010 ◽  
Vol 1 (2) ◽  
pp. 18-33
Author(s):  
T. O. Ting ◽  
H. C. Ting ◽  
T. S. Lee
Keyword(s):  
Particle Swarm Optimization ◽  
Taguchi Method ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Computational Cost ◽  
Particle Swarm ◽  
Benchmark Problems ◽  
Discrete Variables ◽  
Swarm Optimization ◽  
Continuous And Discrete Variables

In this work, a hybrid Taguchi-Particle Swarm Optimization (TPSO) is proposed to solve global numerical optimization problems with continuous and discrete variables. This hybrid algorithm combines the well-known Particle Swarm Optimization Algorithm with the established Taguchi method, which has been an important tool for robust design. This paper presents the improvements obtained despite the simplicity of the hybridization process. The Taguchi method is run only once in every PSO iteration and therefore does not give significant impact in terms of computational cost. The method creates a more diversified population, which also contributes to the success of avoiding premature convergence. The proposed method is effectively applied to solve 13 benchmark problems. This study’s results show drastic improvements in comparison with the standard PSO algorithm involving continuous and discrete variables on high dimensional benchmark functions.

Taguchi-Particle Swarm Optimization for Numerical Optimization

2012 ◽  
pp. 44-59
Author(s):  
T. O. Ting ◽  
H. C. Ting ◽  
T. S. Lee
Keyword(s):  
Particle Swarm Optimization ◽  
Taguchi Method ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Computational Cost ◽  
Particle Swarm ◽  
Benchmark Problems ◽  
Discrete Variables ◽  
Swarm Optimization ◽  
Continuous And Discrete Variables

In this work, a hybrid Taguchi-Particle Swarm Optimization (TPSO) is proposed to solve global numerical optimization problems with continuous and discrete variables. This hybrid algorithm combines the well-known Particle Swarm Optimization Algorithm with the established Taguchi method, which has been an important tool for robust design. This paper presents the improvements obtained despite the simplicity of the hybridization process. The Taguchi method is run only once in every PSO iteration and therefore does not give significant impact in terms of computational cost. The method creates a more diversified population, which also contributes to the success of avoiding premature convergence. The proposed method is effectively applied to solve 13 benchmark problems. This study’s results show drastic improvements in comparison with the standard PSO algorithm involving continuous and discrete variables on high dimensional benchmark functions.

Optimization of Shell and Tube Heat Exchanger Using Alpha Tuning Elephant Herding Optimization (EHO) Technique

2021 ◽  
Vol 52 ◽  
pp. 92-101
Author(s):  
Jiten Makadia ◽  
C.D. Sankhavara
Keyword(s):  
Particle Swarm Optimization ◽  
Heat Exchanger ◽  
Optimization Problems ◽  
Cost Optimization ◽  
Real Life ◽  
Particle Swarm ◽  
Benchmark Problems ◽  
Swarm Optimization ◽  
Tube Heat Exchanger ◽  
Shell And Tube

Swarm Intelligence algorithms like PSO (Particle Swarm Optimization), ACO (Ant Colony Optimization), ABC (Artificial Bee Colony), Glow-worm swarm Optimization, etc. have been utilized by researchers for solving optimization problems. This work presents the application of a novel modified EHO (Elephant Herding Optimization) for cost optimization of shell and tube heat exchanger. A comparison of the results obtained by EHO in two benchmark problems shows that it is superior to those obtained with genetic algorithm and particle swarm optimization. The overall cost reduction is 13.3 % and 9.68% for both the benchmark problem compared to PSO. Results indicate that EHO can be effectively utilized for solving real-life optimization problems.

scholarly journals Multi/Many-Objective Particle Swarm Optimization Algorithm Based on Competition Mechanism

2020 ◽  
Vol 2020 ◽  
pp. 1-26
Author(s):  
Wusi Yang ◽  
Li Chen ◽  
Yi Wang ◽  
Maosheng Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Benchmark Problems ◽  
Swarm Optimization ◽  
Competition Mechanism ◽  
Multiobjective Particle Swarm Optimization

The recently proposed multiobjective particle swarm optimization algorithm based on competition mechanism algorithm cannot effectively deal with many-objective optimization problems, which is characterized by relatively poor convergence and diversity, and long computing runtime. In this paper, a novel multi/many-objective particle swarm optimization algorithm based on competition mechanism is proposed, which maintains population diversity by the maximum and minimum angle between ordinary and extreme individuals. And the recently proposed θ-dominance is adopted to further enhance the performance of the algorithm. The proposed algorithm is evaluated on the standard benchmark problems DTLZ, WFG, and UF1-9 and compared with the four recently proposed multiobjective particle swarm optimization algorithms and four state-of-the-art many-objective evolutionary optimization algorithms. The experimental results indicate that the proposed algorithm has better convergence and diversity, and its performance is superior to other comparative algorithms on most test instances.

scholarly journals Optimization of Gain, Impedance, and Bandwidth of Yagi-Uda Array Using Particle Swarm Optimization

2008 ◽  
Vol 2008 ◽  
pp. 1-4 ◽  
Author(s):  
Munish Rattan ◽  
Manjeet Singh Patterh ◽  
B. S. Sohi
Keyword(s):  
Particle Swarm Optimization ◽  
Method Of Moments ◽  
High Performance ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Computational Cost ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Evolutionary Technique ◽  
Global Optimization Technique

Particle swarm optimization (PSO) is a new, high-performance evolutionary technique, which has recently been used for optimization problems in antennas and electromagnetics. It is a global optimization technique-like genetic algorithm (GA) but has less computational cost compared to GA. In this paper, PSO has been used to optimize the gain, impedance, and bandwidth of Yagi-Uda array. To evaluate the performance of designs, a method of moments code NEC2 has been used. The results are comparable to those obtained using GA.

scholarly journals Improved Particle Swarm Optimization with a Collective Local Unimodal Search for Continuous Optimization Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-23 ◽  
Author(s):  
Martins Akugbe Arasomwan ◽  
Aderemi Oluyinka Adewumi
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Continuous Optimization ◽  
Search Space ◽  
Benchmark Problems ◽  
Search Technique ◽  
Swarm Optimization ◽  
Particle Position

A new local search technique is proposed and used to improve the performance of particle swarm optimization algorithms by addressing the problem of premature convergence. In the proposed local search technique, a potential particle position in the solution search space is collectively constructed by a number of randomly selected particles in the swarm. The number of times the selection is made varies with the dimension of the optimization problem and each selected particle donates the value in the location of its randomly selected dimension from its personal best. After constructing the potential particle position, some local search is done around its neighbourhood in comparison with the current swarm global best position. It is then used to replace the global best particle position if it is found to be better; otherwise no replacement is made. Using some well-studied benchmark problems with low and high dimensions, numerical simulations were used to validate the performance of the improved algorithms. Comparisons were made with four different PSO variants, two of the variants implement different local search technique while the other two do not. Results show that the improved algorithms could obtain better quality solution while demonstrating better convergence velocity and precision, stability, robustness, and global-local search ability than the competing variants.

scholarly journals Repulsive Self-Adaptive Acceleration Particle Swarm Optimization Approach

2014 ◽  
Vol 4 (3) ◽  
pp. 189-204 ◽  
Author(s):  
Simone A. Ludwig
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Main Idea ◽  
Benchmark Problems ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Wide Range ◽  
Adaptive Pso ◽  
Self Adaptive

Abstract Adaptive Particle Swarm Optimization (PSO) variants have become popular in recent years. The main idea of these adaptive PSO variants is that they adaptively change their search behavior during the optimization process based on information gathered during the run. Adaptive PSO variants have shown to be able to solve a wide range of difficult optimization problems efficiently and effectively. In this paper we propose a Repulsive Self-adaptive Acceleration PSO (RSAPSO) variant that adaptively optimizes the velocity weights of every particle at every iteration. The velocity weights include the acceleration constants as well as the inertia weight that are responsible for the balance between exploration and exploitation. Our proposed RSAPSO variant optimizes the velocity weights that are then used to search for the optimal solution of the problem (e.g., benchmark function). We compare RSAPSO to four known adaptive PSO variants (decreasing weight PSO, time-varying acceleration coefficients PSO, guaranteed convergence PSO, and attractive and repulsive PSO) on twenty benchmark problems. The results show that RSAPSO achives better results compared to the known PSO variants on difficult optimization problems that require large numbers of function evaluations.

scholarly journals Particle Swarm Optimization Algorithm with Multiple Phases for Solving Continuous Optimization Problems

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jing Li ◽  
Yifei Sun ◽  
Sicheng Hou
Keyword(s):  
Experimental Study ◽  
Particle Swarm Optimization ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Continuous Optimization ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Fixed Phase ◽  
Continuous Optimization Problems

An algorithm with different parameter settings often performs differently on the same problem. The parameter settings are difficult to determine before the optimization process. The variants of particle swarm optimization (PSO) algorithms are studied as exemplars of swarm intelligence algorithms. Based on the concept of building block thesis, a PSO algorithm with multiple phases was proposed to analyze the relation between search strategies and the solved problems. Two variants of the PSO algorithm, which were termed as the PSO with fixed phase (PSOFP) algorithm and PSO with dynamic phase (PSODP) algorithm, were compared with six variants of the standard PSO algorithm in the experimental study. The benchmark functions for single-objective numerical optimization, which includes 12 functions in 50 and 100 dimensions, are used in the experimental study, respectively. The experimental results have verified the generalization ability of the proposed PSO variants.

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