Adaptive comprehensive learning particle swarm optimization with cooperative archive

Lin, Anping; Sun, Wei; Huang, Yu; Wu, Guohua; Tang, Hongwei

doi:10.1016/j.asoc.2019.01.047

Cited by 44 publications

(25 citation statements)

References 49 publications

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“…The eMuPSO algorithm was first implemented together with the LLS for optimizing the observation matrix and dynamic parameter estimation of all the six links of the 6DOF robot manipulator. The ideal parameters of the manipulator are given in (25)(26)(27)(28)(29)(30)(31)(32). Then the proposed algorithm was finally implemented in evaluating thirty-six benchmark functions including twenty-four variable-dimension benchmark functions and twelve constantdimension benchmark functions.…”

Section: Resultsmentioning

confidence: 99%

“…In [30] an external archive was employed to preserve the non-dominated solutions visited by the particles to enable evolutionary search strategies to exchange useful information among them, and [31] used a gridbased approach for the archiving process and ε-dominance method to update the archive, which helps the algorithm to increase the diversity of solutions. Reference [32] used the cooperative archive to exploit the valuable information of the current swarm and archive. Information about the elite particles from dynamic sub swarms was used in [33] to improve the following sub-swarm, while [32] introduced a new velocity updating technique that explores the external archive of non-dominated solutions in the current swarm.…”

Section: Elite Archivementioning

confidence: 99%

“…Reference [32] used the cooperative archive to exploit the valuable information of the current swarm and archive. Information about the elite particles from dynamic sub swarms was used in [33] to improve the following sub-swarm, while [32] introduced a new velocity updating technique that explores the external archive of non-dominated solutions in the current swarm. In this article, an elite archive learning is used to refine the solution in the final stages of the algorithm.…”

Section: Elite Archivementioning

confidence: 99%

See 2 more Smart Citations

Parameter Estimation for Industrial Robot Manipulators Using an Improved Particle Swarm Optimization Algorithm with Gaussian Mutation and Archived Elite Learning

Umar¹,

Shi²,

Zheng³

et al. 2020

Adv. sci. technol. eng. syst. j.

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Section: Resultsmentioning

confidence: 99%

Section: Elite Archivementioning

confidence: 99%

Section: Elite Archivementioning

confidence: 99%

See 1 more Smart Citation

Parameter Estimation for Industrial Robot Manipulators Using an Improved Particle Swarm Optimization Algorithm with Gaussian Mutation and Archived Elite Learning

Umar¹,

Shi²,

Zheng³

et al. 2020

Adv. sci. technol. eng. syst. j.

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“…A comprehensive learning PSO (CLPSO) was proposed in [52] by leveraging the useful information contained in personal best positions of other non-fittest swarm members in order to update their current velocity and position. Inspired by CLPSO, an adaptive comprehensive learning PSO with cooperative archive (ACLPSO-CA) was proposed in [36]. An adaptive mechanism was designed to dynamically adjust the comprehensive learning probability of each ACLPSO-CA particle during the search process by referring to its current search performance.…”

Section: ) Modificaiton In Learning Strategymentioning

confidence: 99%

“…Different mechanisms were advocated to balance the exploration and exploitation strengths of PSO optimally in order to improve its overall performance in solving various types of complex optimization problems [36,37]. Parameter adaptation is a commonly used mechanism to adjust the exploration and exploitation strengths of PSO through the incorporation of various new algorithm-specific parameters.…”

Section: Introductionmentioning

confidence: 99%

Modified Particle Swarm Optimization With Effective Guides

2020

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Despite of its simplicity, the conventional learning strategy of canonical particle swarm optimization (PSO) is inefficient to handle complex optimization problems due to its tendency of overemphasizing the fitness information of global best position without considering the diversity information of swarm. In this paper, a modified particle swarm optimization with effective guides (MPSOEG) is proposed, aiming to improve the algorithm's search performances in handling the optimization problems with different characteristics. Depending on the search performance of algorithm, two types of exemplars can be generated by an optimal guide creation (OGC) module incorporated into MPSOEG by referring to the particles with valuable directional information. Particularly, a global exemplar is generated by OCG module to guide the swarm converging towards the promising solution regions of search space, whereas a unique local exemplar can be customized for each particle to enable it escaping from local or non-optimal solution regions. In contrary to global best particle, the exemplars generated by OGC module are able to guide all MPSOEG particles more effectively by considering both fitness and diversity information of swarm, hence can achieve better balancing of algorithm's exploration and exploitation searches. Another notable contribution of MPSOEG is the simplicity of its learning framework through the elimination of both inertia weight and acceleration coefficients parameters. Comprehensive simulation studies are conducted with 25 benchmark functions and the proposed MPSOEG is reported to outperform its six peer algorithms in terms of search accuracy, search reliability and search efficiency in most tested problems.

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A multiion particle swarm optimization algorithm based on repellent and attraction forces

Zhu

Zhou

Shang

et al. 2020

Concurrency and Computation

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Summary Particle swarm optimization (PSO) is an iterative computational methods which is used for obtaining the solutions of practical optimization problems. PSO is however, prone to be ended up obtaining a local optimum. Various strategies are proposed in the related literature to address this issue. Such strategies, however, often reduce the convergence speed of the algorithms. This article proposes a multiion particle swarm optimization (MION‐PSO) algorithm which incorporates three strategies to balance the exploration and exploitation abilities of the algorithm. To improve the exploration abilities, the particles are regarded as ions with repellent/attraction forces among them. The second strategy is a multiion strategy (MIS) in which the population is divided into many subswarms, namely, particle group. Using MIS, the optimal solution within each group is then used to purposefully guide the updates of other individuals. To delete the useless particles without the capability of updating historical best location for a specific period and have a misleading impact on others, without we employ a particle elimination strategy. Twenty‐three benchmark functions and eight baseline algorithms are employed to test the performance of MION‐PSO method. The experimental results show that MION‐PSO method significantly outperforms the baseline algorithms in terms of convergence, and further exhibits high capability in finding the optimal solutions especially on unimodal functions.

show abstract

Adaptive comprehensive learning particle swarm optimization with cooperative archive

Cited by 44 publications

References 49 publications

Parameter Estimation for Industrial Robot Manipulators Using an Improved Particle Swarm Optimization Algorithm with Gaussian Mutation and Archived Elite Learning

Parameter Estimation for Industrial Robot Manipulators Using an Improved Particle Swarm Optimization Algorithm with Gaussian Mutation and Archived Elite Learning

Modified Particle Swarm Optimization With Effective Guides

A multiion particle swarm optimization algorithm based on repellent and attraction forces

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