An ameliorated particle swarm optimizer for solving numerical optimization problems

Chen, Ke; Zhou, Fengyu; Wang, Yugang; Yin, Lei

doi:10.1016/j.asoc.2018.09.007

Cited by 51 publications

(30 citation statements)

References 46 publications

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“…e traditional PSO algorithm uses fixed parameter settings during the evolution process. However, many pieces of literatures indicate that PSO is very sensitive to parameter settings [30,[58][59][60][61]. Among them, Zhan et al [61] proposed APSO, which is a very famous PSO variant that well deals with the parameters.…”

Section: Improvement Of Pso Parametersmentioning

confidence: 99%

“…Correspondingly, a larger inertial weight is helpful for global exploration, and with small inertia weight, it is conducive to local exploration. Afterwards, after many numerous studies [53,58,59], it was found that the nonlinear ω is more helpful for algorithm enhancement. In addition, in this paper, we choose to use nonlinear ω and perform chaos treatment, aiming to enhance the disorder during iteration to enhance the population diversity of the algorithm in the late iteration.…”

Section: Improvement Of Pso Parametersmentioning

confidence: 99%

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

2021

Mobile Information Systems

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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.

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Section: Improvement Of Pso Parametersmentioning

confidence: 99%

Section: Improvement Of Pso Parametersmentioning

confidence: 99%

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

2021

Mobile Information Systems

View full text Add to dashboard Cite

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“…PSO is a population-based stochastic optimization algorithm that has been successfully used to solve many optimization problems [39]. Though the PSO has been widely utilized to address various complicated engineering problems, it is likely to suffer lack of diversity and ineffectiveness of balance between the global search ability and the local search ability in the search process [40]. The flowchart of PSO is shown in Fig 11. [41]…”

Section: Numerical Studiesmentioning

confidence: 99%

Optimal Design of Steel-Concrete Composite I-girder Bridges Using Three Meta-Heuristic Algorithms

Kaveh¹,

Zarandi²

2018

Period. Polytech. Civil Eng.

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Bridges are among very important structures in engineering, due to their rather high cost, and this is why optimization of these structures is a challenging problem. In this paper, optimal design of steel-concrete composite I-girder bridges is performed. Three recently developed meta-heuristic algorithms consisting of Colliding Bodies Optimization (CBO), Enhanced Colliding Bodies Optimization (ECBO) and Vibration Particle System (VPS) are utilized for the first time in the optimal design of steel-concrete I-girder bridges. Both continuous and discrete variables are utilized in the process of optimization. Performance and the convergence histories of these algorithms are compared. In order to have a suitable comparison between these algorithms with previous algorithms, PSO is used and results are displayed. This paper focuses on cost optimization the bridges. Furthermore constraints include all of requirements of the code of practice for design. The comparative study has shown that VPS algorithm has better performance than CBO and ECBO. However, all three algorithms act in a way that the final optimized design does not need the addition of the longitudinal stiffener.

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“…Ratnaweera et al [19] proposed a self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (TVAC). To improve the solution quality and accelerate the convergence, Chen et al [20], [21] proposed sine cosine acceleration coefficients (SCAC) and nonlinear dynamic acceleration coefficients (NDAC). Similarly, Tian et al [22] proposed sigmoid-based acceleration coefficients (SBAC) to balance the global search ability in the initial iterative stage and the local convergence ability in the latter stage.…”

Section: Introductionmentioning

confidence: 99%

Improved Simplified Particle Swarm Optimization Based on Piecewise Nonlinear Acceleration Coefficients and Mean Differential Mutation Strategy

Lin

Wang

et al. 2020

IEEE Access

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Particle swarm optimization (PSO) has been widely used in various optimization fields because of its easy implementation and high efficiency. However, it suffers from some limitations like slow convergence and premature convergence when solving high-dimensional optimization problems. This paper attempts to address these open issues. Firstly, a new method of parameter adjustment named piecewise nonlinear acceleration coefficients is introduced to the simplified particle swarm optimization algorithm (SPSO), and an improved algorithm called piecewise-nonlinear-acceleration-coefficients-based SPSO (P-SPSO) is proposed. Then, a mean differential mutation strategy is developed for the update mechanism of P-SPSO, and another improved algorithm named mean-differential-mutation-strategy embedded P-SPSO (MP-SPSO) is proposed. To validate the performance of the proposed algorithms, four different sets of experiments are carried out in this paper. The results show that, 1) the proposed P-SPSO can get better solutions than other four classic improved SPSO with different acceleration coefficients, 2) the proposed MP-SPSO algorithm shows better optimization performance than P-SPSO and mean-differential-mutationstrategy-based SPSO (M-SPSO), 3) the proposed MP-SPSO is clearly seen to be more successful than other eight well-known PSO variants, 4) compared to other nine intelligent optimization algorithms, MP-SPSO achieves better performance in terms of solution quality and robustness. Moreover, the proposed MP-SPSO algorithm is successfully applied to a real constrained engineering problem and provides better solutions than other methods.INDEX TERMS Simplified particle swarm optimization, piecewise nonlinear acceleration coefficients, mean differential mutation strategy, real engineering problem.

show abstract

An ameliorated particle swarm optimizer for solving numerical optimization problems

Cited by 51 publications

References 46 publications

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

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

Optimal Design of Steel-Concrete Composite I-girder Bridges Using Three Meta-Heuristic Algorithms

Improved Simplified Particle Swarm Optimization Based on Piecewise Nonlinear Acceleration Coefficients and Mean Differential Mutation Strategy

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