An improved heterogeneous multi-swarm PSO algorithm to generate an optimal T-S fuzzy model of a hydraulic process

2017 ◽  
Vol 40 (6) ◽  
pp. 2039-2053 ◽  
Author(s):  
Jaouher Chrouta ◽  
Abderrahmen Zaafouri ◽  
Mohamed Jemli
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Fuzzy Model ◽  
Processing Unit ◽  
Search Performance ◽  
Swarm Optimization ◽  
Generalization Ability ◽  
Central Processing ◽  
Inertia Weight

In this paper, a new methodology to develop an Optimal Fuzzy model (OptiFel) using an improved Multi-swarm Particle Swarm Optimization (MsPSO) algorithm is proposed with a new adaptive inertia weight based on Grey relational analysis. Since the classical MsPSO suffers from premature convergence and can be trapped into local optima, which significantly affects the model accuracy, a modified MsPSO algorithm is presented here. The most important advantage of the proposed algorithm is the adjustment of fewer parameters in which the main parameter is the inertia weight. In fact, the control of this parameter could facilitate the convergence and prevent an explosion of the swarm. The performance of the proposed algorithm is evaluated by adopting standard tests and indicators which are reported in the specialized literature. The proposed Grey MsPSO is first applied to solve the optimization problems of six benchmark functions and then, compared with the other nine variants of particle swarm optimization. In order to demonstrate the higher search performance of the proposed algorithm, the comparison is then made via two performance tests such as the standard deviation and central processing unit time. To further validate the generalization ability of the Improved OptiFel approach, the proposed algorithm is secondly applied on the Box–Jenkins Gas Furnace system and on a irrigation station prototype. A comparative study based on Mean Square Error is then performed between the proposed approach and other existing methods. As a result, the improved Grey MsPSO is well adopted to find an optimal model for the real processes with high accuracy and strong generalization ability.

scholarly journals Real-Time Particle Swarm Optimization on FPGA for the Optimal Message-Chain Structure

Electronics ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 274 ◽  
Author(s):  
Heoncheol Lee ◽  
Kipyo Kim ◽  
Yongsung Kwon ◽  
Eonpyo Hong
Keyword(s):  
Particle Swarm Optimization ◽  
Real Time ◽  
Particle Swarm ◽  
Chain Structure ◽  
Processing Unit ◽  
Swarm Optimization ◽  
Central Processing ◽  
Time Optimization ◽  
New Variant ◽  
Real Time Optimization

This paper addresses the real-time optimization problem of the message-chain structure to maximize the throughput in data communications based on half-duplex command-response protocols. This paper proposes a new variant of the particle swarm optimization (PSO) algorithm to resolve real-time optimization, which is implemented on field programmable gate arrays (FPGA) to be performed faster in parallel and to avoid the delays caused by other tasks on a central processing unit. The proposed method was verified by finding the optimal message-chain structure much faster than the original PSO, as well as reliably with different system and algorithm parameters.

scholarly journals On the Performance of Linear Decreasing Inertia Weight Particle Swarm Optimization for Global Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Martins Akugbe Arasomwan ◽  
Aderemi Oluyinka Adewumi
Keyword(s):  
Global Optimization ◽  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Search Space ◽  
Terminal Point ◽  
Simulation Experiments ◽  
Swarm Optimization ◽  
Inertia Weight

Linear decreasing inertia weight (LDIW) strategy was introduced to improve on the performance of the original particle swarm optimization (PSO). However, linear decreasing inertia weight PSO (LDIW-PSO) algorithm is known to have the shortcoming of premature convergence in solving complex (multipeak) optimization problems due to lack of enough momentum for particles to do exploitation as the algorithm approaches its terminal point. Researchers have tried to address this shortcoming by modifying LDIW-PSO or proposing new PSO variants. Some of these variants have been claimed to outperform LDIW-PSO. The major goal of this paper is to experimentally establish the fact that LDIW-PSO is very much efficient if its parameters are properly set. First, an experiment was conducted to acquire a percentage value of the search space limits to compute the particle velocity limits in LDIW-PSO based on commonly used benchmark global optimization problems. Second, using the experimentally obtained values, five well-known benchmark optimization problems were used to show the outstanding performance of LDIW-PSO over some of its competitors which have in the past claimed superiority over it. Two other recent PSO variants with different inertia weight strategies were also compared with LDIW-PSO with the latter outperforming both in the simulation experiments conducted.

scholarly journals Enriched Particle Swarm Optimization Created By Varying Parameters for Optimization

2019 ◽  
Vol 8 (3) ◽  
pp. 8259-8265
Keyword(s):  
Particle Swarm Optimization ◽  
Swarm Intelligence ◽  
Optimization Problems ◽  
Real Life ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Slow Convergence ◽  
Inertia Weight ◽  
Life Problems ◽  
Fixed Constant

Particle swarm optimization (PSO) is one of the most capable algorithms that reside to the swarm intelligence (SI) systems. Recently, it becomes very popular and renowned because of the easy implementation in complex/real life optimization problems. However, PSO has some observable drawbacks such as diversity maintenance, pre convergence and/or slow convergence speed. The ultimate success of PSO depends on the velocity update of the particles. Velocity has a significant dependence on its multiplied coefficient like inertia weight and acceleration factors. To increase the ability of PSO, this paper introduced an enriched PSO (namely ePSO), to solve hard optimization problems more precisely, efficiently and reliably. In ePSO novel gradually decreased inertia weight (as an alternative of a fixed constant value) and new gradually decreased and/or increased acceleration factors (meant for cognitive and social modules) is introduced. Proposed ePSO is used to solve four well known typical unconstrained benchmark functions and four complex unconstrained real life problems. The overall observation shows that proposed new algorithm ePSO is fitter than the compared algorithms significantly and statistically. Moreover, the convergence accuracy and speed of ePSO are also improved effectively

scholarly journals Adaptive Multiswarm Comprehensive Learning Particle Swarm Optimization

Information ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 173 ◽  
Author(s):  
Xiang Yu ◽  
Claudio Estevez
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Velocity ◽  
Pareto Front ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Search Performance ◽  
Swarm Optimization ◽  
Nondominated Solutions ◽  
The Difference ◽  
Update Strategy

Multiswarm comprehensive learning particle swarm optimization (MSCLPSO) is a multiobjective metaheuristic recently proposed by the authors. MSCLPSO uses multiple swarms of particles and externally stores elitists that are nondominated solutions found so far. MSCLPSO can approximate the true Pareto front in one single run; however, it requires a large number of generations to converge, because each swarm only optimizes the associated objective and does not learn from any search experience outside the swarm. In this paper, we propose an adaptive particle velocity update strategy for MSCLPSO to improve the search efficiency. Based on whether the elitists are indifferent or complex on each dimension, each particle adaptively determines whether to just learn from some particle in the same swarm, or additionally from the difference of some pair of elitists for the velocity update on that dimension, trying to achieve a tradeoff between optimizing the associated objective and exploring diverse regions of the Pareto set. Experimental results on various two-objective and three-objective benchmark optimization problems with different dimensional complexity characteristics demonstrate that the adaptive particle velocity update strategy improves the search performance of MSCLPSO significantly and is able to help MSCLPSO locate the true Pareto front more quickly and obtain better distributed nondominated solutions over the entire Pareto front.

scholarly journals Improved Particle Swarm Optimization Algorithm Based on Last-Eliminated Principle and Enhanced Information Sharing

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Xueying Lv ◽  
Yitian Wang ◽  
Junyi Deng ◽  
Guanyu Zhang ◽  
Liu Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Information Sharing ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Population Diversity ◽  
Search Performance ◽  
Information Communication ◽  
Swarm Optimization ◽  
Classical Optimization

In this study, an improved eliminate particle swarm optimization (IEPSO) is proposed on the basis of the last-eliminated principle to solve optimization problems in engineering design. During optimization, the IEPSO enhances information communication among populations and maintains population diversity to overcome the limitations of classical optimization algorithms in solving multiparameter, strong coupling, and nonlinear engineering optimization problems. These limitations include advanced convergence and the tendency to easily fall into local optimization. The parameters involved in the imported “local-global information sharing” term are analyzed, and the principle of parameter selection for performance is determined. The performances of the IEPSO and classical optimization algorithms are then tested by using multiple sets of classical functions to verify the global search performance of the IEPSO. The simulation test results and those of the improved classical optimization algorithms are compared and analyzed to verify the advanced performance of the IEPSO algorithm.

Comparative Study of Path Planning by Particle Swarm Optimization and Genetic Algorithm

2014 ◽  
Vol 687-691 ◽  
pp. 1420-1424
Author(s):  
Hai Tao Han ◽  
Wan Feng Ji ◽  
Yao Qing Zhang ◽  
De Peng Sha
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Path Planning ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Search Performance ◽  
Operational Environment ◽  
Swarm Optimization ◽  
Vehicle Path ◽  
Swarm Intelligence Algorithm

Two main requirements of the optimization problems are included: one is finding the global minimum, the other is obtaining fast convergence speed. As heuristic algorithm and swarm intelligence algorithm, both particle swarm optimization and genetic algorithm are widely used in vehicle path planning because of their favorable search performance. This paper analyzes the characteristics and the same and different points of two algorithms as well as making simulation experiment under the same operational environment and threat states space. The result shows that particle swarm optimization is superior to genetic algorithm in searching speed and convergence.

scholarly journals A Hybrid Dynamic Probability Mutation Particle Swarm Optimization for Engineering Structure Design

2021 ◽  
Vol 2021 ◽  
pp. 1-32
Author(s):  
Qiuyu Li ◽  
Zhiteng Ma
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Particle Swarm ◽  
Structure Design ◽  
Local Optimum ◽  
Engineering Optimization ◽  
Swarm Optimization ◽  
Engineering Structure ◽  
Inertia Weight

Particle swarm optimization (PSO) is a common metaheuristic algorithm. However, when dealing with practical engineering structure optimization problems, it is prone to premature convergence during the search process and falls into a local optimum. To strengthen its performance, combining several ideas of the differential evolution algorithm (DE), a dynamic probability mutation particle swarm optimization with chaotic inertia weight (CWDEPSO) is proposed. The main improvements are achieved by improving the parameters and algorithm mechanism in this paper. The former proposes a novel inverse tangent chaotic inertia weight and sine learning factors. Besides, the scaling factor and crossover probability are improved by random distributions, respectively. The latter introduces a monitoring mechanism. By monitoring the convergence of PSO, a developed mutation operator with a more reliable local search capability is adopted and increases population diversity to help PSO escape from the local optimum effectively. To evaluate the effectiveness of the CWDEPSO algorithm, 24 benchmark functions and two groups of engineering optimization experiments are used for numerical and engineering optimization, respectively. The results indicate CWDEPSO offers better convergence accuracy and speed compared with some well-known metaheuristic algorithms.

scholarly journals Improved Opposition-Based Particle Swarm Optimization Algorithm for Global Optimization

Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2280
Author(s):  
Nafees Ul Hassan ◽  
Waqas Haider Bangyal ◽  
M. Sadiq Ali Khan ◽  
Kashif Nisar ◽  
Ag. Asri Ag. Ibrahim ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Statistical Tests ◽  
Random Sequence ◽  
Particle Swarm ◽  
Population Diversity ◽  
Bench Mark ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Simulation Results

Particle Swarm Optimization (PSO) has been widely used to solve various types of optimization problems. An efficient algorithm must have symmetry of information between participating entities. Enhancing algorithm efficiency relative to the symmetric concept is a critical challenge in the field of information security. PSO also becomes trapped into local optima similarly to other nature-inspired algorithms. The literature depicts that in order to solve pre-mature convergence for PSO algorithms, researchers have adopted various parameters such as population initialization and inertia weight that can provide excellent results with respect to real world problems. This study proposed two newly improved variants of PSO termed Threefry with opposition-based PSO ranked inertia weight (ORIW-PSO-TF) and Philox with opposition-based PSO ranked inertia weight (ORIW-PSO-P) (ORIW-PSO-P). In the proposed variants, we incorporated three novel modifications: (1) pseudo-random sequence Threefry and Philox utilization for the initialization of population; (2) increased population diversity opposition-based learning is used; and (3) a novel introduction of opposition-based rank-based inertia weight to amplify the execution of standard PSO for the acceleration of the convergence speed. The proposed variants are examined on sixteen bench mark test functions and compared with conventional approaches. Similarly, statistical tests are also applied on the simulation results in order to obtain an accurate level of significance. Both proposed variants show highest performance on the stated benchmark functions over the standard approaches. In addition to this, the proposed variants ORIW-PSO-P and ORIW-PSO-P have been examined with respect to training of the artificial neural network (ANN). We have performed experiments using fifteen benchmark datasets obtained and applied from the repository of UCI. Simulation results have shown that the training of an ANN with ORIW-PSO-P and ORIW-PSO-P algorithms provides the best results than compared to traditional methodologies. All the observations from our simulations conclude that the proposed ASOA is superior to conventional optimizers. In addition, the results of our study predict how the proposed opposition-based method profoundly impacts diversity and convergence.

scholarly journals Hybrid particle swarm optimization with spiral-shaped mechanism for solving high-dimension problems

2020 ◽  
Vol 1 (1) ◽  
pp. 1-6
Author(s):  
Humberto Martins Mendonça Duarte ◽  
Rafael Lima de Carvalho
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Logistic Map ◽  
Particle Swarm ◽  
Dynamic Correction ◽  
Swarm Optimization ◽  
Local Optima ◽  
Inertia Weight ◽  
Hybrid Particle Swarm Optimization ◽  
Global Optima

Particle swarm optimization (PSO) is a well-known metaheuristic, whose performance for solving global optimization problems has been thoroughly explored. It has been established that without proper manipulation of the inertia weight parameter, the search for a global optima may fail. In order to handle this problem, we investigate the experimental performance of a PSO-based metaheuristic known as HPSO-SSM, which uses a logistic map sequence to control the inertia weight to enhance the diversity in the search process, and a spiral-shaped mechanism as a local search operator, as well as two dynamic correction factors to the position formula. Thus, we present an application of this variant for solving high-dimensional optimization problems, and evaluate its effectiveness against 24 benchmark functions. A comparison between both methods showed that the proposed variant can escape from local optima, and demonstrates faster convergence for almost every evaluated function.

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