Multipopulation cooperative particle swarm optimization with a mixed mutation strategy

Li, Wei; Meng, Xiang; Huang, Ying; Fu, Zhang-Hua

doi:10.1016/j.ins.2020.02.034

Cited by 93 publications

(46 citation statements)

References 41 publications

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“…Apart from being able to adaptively divide the swarm into the exploitation and exploration sections, an elitist learning strategy was introduced to specifically evolve the global best particle further in order to further enhance the convergence speed of ATLPSO-ELS. A multipopulation cooperative PSO (MPCPSO) was developed in [39], where fitness criterion was utilized to divide the main population into an elitist population and a general population. An unique exemplar was then generated in each population in order to guide the search processes of their members.…”

Section: ) Modificaiton In Learning Strategymentioning

confidence: 99%

“…There are higher chance for the Pg particle to be trapped at local optima of complex search environment during the early stage of optimization and the remaining population members can be misled towards these inferior solution regions. In [39,49], it was advocated that the negative influences of Pg can be suppressed by leveraging useful information of other nonfittest particles to formulate the unique exemplar for each particle in order to adjust its search trajectory. The simulation results of [51] also revealed that the directional information carried by other non-fittest population members cannot be underestimated in addressing the deficiency of BPSO.…”

Section: A Derivation Of Global Exemplarmentioning

confidence: 99%

“…This paper aims to advocate another viable approach to produce PSO variants with multiple learning strategies by replacing the self-cognitive and social components of all particles through creation of multiple exemplars in guiding the search process of population [3,39]. Under this design methodology, each dimension of these exemplars can be contributed by different non-fittest population members in order to suppress the potential negative influences of global best particle, i.e.…”

Section: Introductionmentioning

confidence: 99%

“…Different exemplars can also be generated to guide the search process of each particle with different exploration and exploitation strengths without having to introduce new search operators into PSO that can incur extra computational complexities. Although some of the PSO variants with multiple exemplars scheme were developed such as those in [3,39], the robust mechanisms required to design effective exemplars for all particles remain as an open-ended challenges. First, it is nontrivial to decide the non-fittest particles that are allowed to donate its directional information in constructing effective exemplars that can guide all particles converging towards the promising solutions regions without compromising the swarm diversity.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: ) Modificaiton In Learning Strategymentioning

confidence: 99%

Section: A Derivation Of Global Exemplarmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modified Particle Swarm Optimization With Effective Guides

2020

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“…The multiphysics analysis method was adopted to compare the conventional SRM and DSSRM (dual stator SRM), 30 which shows that the DSSRM not only reduces the radial force amplitude but also distributes the radial force more uniformly and decreases the vibration. Some structural parameters of the motor are optimized by the genetic and multiobjective algorithm 31‐33 . The reduction of the radial force is realized by reducing the first harmonic of the radial force.…”

Section: Introductionmentioning

confidence: 99%

The effect of a single‐sided pole shoe and slot on reducing torque ripple in a switched reluctance motor

Huang

Liu

et al. 2020

Concurrency and Computation

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Summary The switched reluctance motor (SRM) has the high level of vibration and noise, whose application is greatly limited in many fields. In this article, a novel stator for a SRM is designed to reduce torque ripple and is suitable for one‐directional or low‐speed reverse rotation. On the one hand, a shoe is added to the stator pole side in the rotating forward direction from the completely unaligned to the aligned position. On the other hand, a slot is set on the top of stator tooth. First, causes of vibration and torque ripple are analyzed. Second, it is indicated by the equivalent magnetic circuit (EMC) method that the air gap permeability of the model with the single‐sided pole shoe is larger than that of the conventional model in the nonoverlapping region between the stator and rotor. The influences of the angle and height of shoe on the air gap magnetic density, radial force, and torque ripple are analyzed. Third, the results from EMC method show that the radial force can be reduced by opening a slot at the top of stator pole arc. The width and depth of slot which has effect on the radial force and torque ripple are analyzed. Finally, finite element analysis results show that the decreases of 34.52% for the radial force wave peak and 43.39% for the torque ripple are achieved at the cost of a decrease of 8.54% for the average torque of the single‐sided pole and the slot model of the stator pole compared with the conventional model. Some experiments for the two prototypes verify that the torque ripple and radial force can be reduced by adopting the new model.

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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.

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Multipopulation cooperative particle swarm optimization with a mixed mutation strategy

Cited by 93 publications

References 41 publications

Modified Particle Swarm Optimization With Effective Guides

Modified Particle Swarm Optimization With Effective Guides

The effect of a single‐sided pole shoe and slot on reducing torque ripple in a switched reluctance motor

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

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