Multi swarm bare bones particle swarm optimization with distribution adaption

Vafashoar, Reza; Meybodi, Mohammad Reza

doi:10.1016/j.asoc.2016.06.028

Cited by 31 publications

(4 citation statements)

References 46 publications

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“…Moreover, a new approach for adaptive determination of covariance matrices, which are used in the updating distributions, has been proposed in the research. As the result that the searching ability of CLA-BBPSO has been confirmed by experiments, to improve the convergence speed can be the main future work 20 .…”

Section: Bare Bones Particle Swarm Optimizationmentioning

confidence: 92%

A Pair-wise Bare Bones Particle Swarm Optimization Algorithm for Nonlinear Functions

Guo¹,

Sato²

2017

IJNDC

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Bare bones particle swarm optimization is a parameter-free swarm intelligence algorithm which is famous for easy applying. It has aroused wide concern of academic circle on its principles and applications in recent years. However, losing the diversity quickly still causes the premature convergence in the iteration process. Hence, a pair-wise bare bones particle swarm optimization algorithm is proposed in this paper to balance the exploration and exploitation. Moreover, a separate iteration strategy is used in pair-wise operator to enhance the diversity of the swarm. A pair of particles will be placed in different groups and will be applied with different evolutionary strategies. Also, to verify the performance of the proposed algorithm, a set of well-known nonlinear benchmark functions are used in the experiment. Furthermore, several evolutionary algorithms are also evaluated on the same functions as the control group. Finally, the experiment results and statistical analysis confirm the performance of PBBPSO with nonlinear functions.

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Section: Bare Bones Particle Swarm Optimizationmentioning

confidence: 92%

A Pair-wise Bare Bones Particle Swarm Optimization Algorithm for Nonlinear Functions

Guo¹,

Sato²

2017

IJNDC

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“…Nowadays, using only conventional or standard PSO to solve most of the real-world constraint optimisation problems is a very difficult task [ 17 ]. Conventional PSO often suffers from low exploration ability and premature convergence, especially for very high complex multimodal problems [ 34 ]. The hybrid strategies have been widely accepted to increase the performance of algorithms for obtaining better solutions [ 35 ].…”

Section: Pso Variants and Literature Reviewmentioning

confidence: 99%

Particle Swarm Optimisation Variants and Its Hybridisation Ratios for Generating Cost-Effective Educational Course Timetables

2021

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Due to the COVID-19 pandemic, many universities across the globe are unexpectedly accelerated to face another major financial crisis. An effective university course timetabling has a direct effect on the utilisation of the university resources and its operating costs. The university course timetabling is classified to be a Non-deterministic Polynomial (NP)-hard problem. Constructing the optimal timetables without an intelligence timetabling tool is extremely difficult task and very time-consuming. A Hybrid Particle Swarm Optimisation-based Timetabling (HPSOT) tool has been developed for optimising the academic operating costs. In the present study, two variants of Particle Swarm Optimisation (PSO) including Standard PSO (SPSO) and Maurice Clerc PSO (MCPSO) were embedded in the HPSOT program. Five combinations of Insertion Operator (IO) and Exchange Operator (EO) were also proposed and integrated within the HPSOT program aimed at improving the performance of the proposed PSO variants. The statistical design and analysis indicated that five combination results of IO and EO for hybrid SPSO and MCPSO were significantly better than those obtained from the original PSO variants for all eleven problem instances. The average computational times taken by the proposed hybrid methods were also faster than the conventional SPSO and MCPSO for all cases.

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“…The inertia weight, which determines the particle's convergence rate, is represented by w. When w decreases with the increase of the iterations number, the changing w is conducive to improving the performance of the algorithm, and the equation is as follows (Vafashoar and Meybodi et al, 2016) w = w max À iter 3…”

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Route planning algorithm for autonomous underwater vehicles based on the hybrid of particle swarm optimization algorithm and radial basis function

Zhou¹,

Xing²,

Bai

et al. 2018

Transactions of the Institute of Measurement and Control

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The mission route plays an essential role for the mission security and reliability of an unmanned system. This paper gives a route planning method for autonomous underwater vehicles (AUVs) based on the hybrid of particle swarm optimization (PSO) algorithm and radial basis function (RBF). In the improved PSO algorithm, metropolis criterion is used to prevent the improved PSO algorithm from falling into local optimum and RBF is used to smooth the path planned by PSO algorithm. Compared with classic PSO algorithm, the hybrid algorithm of PSO and RBF can avoid falling into the local optimum effectively and plan an anti-collision route. Moreover, based on the simulation results, it can be seen that the approach presented here is more efficient in convergence performance, and the planned route requires lower performance of AUVs.

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Multi swarm bare bones particle swarm optimization with distribution adaption

Cited by 31 publications

References 46 publications

A Pair-wise Bare Bones Particle Swarm Optimization Algorithm for Nonlinear Functions

A Pair-wise Bare Bones Particle Swarm Optimization Algorithm for Nonlinear Functions

Particle Swarm Optimisation Variants and Its Hybridisation Ratios for Generating Cost-Effective Educational Course Timetables

Route planning algorithm for autonomous underwater vehicles based on the hybrid of particle swarm optimization algorithm and radial basis function

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