Particle swarm optimization with dual-level task allocation

Lim, Wei Hong; Isa, Nor Ashidi Mat

doi:10.1016/j.engappai.2014.10.022

Cited by 34 publications

(10 citation statements)

References 73 publications

(97 reference statements)

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“…Any particles that have successfully achieved fitness improvement during the teaching or peer-learning phases are allowed to share the useful information obtained to further enhance the quality of global best particle. A PSO with duallevel task allocation (PSO-DLTA) was proposed in [57] by assuming that task allocation of exploration and exploitation should not be restricted on both population and individual levels, but it also can be performed on dimensional levels. The search strategies with different exploration and exploitation strengths can be assigned to different dimensional components of each PSO-DLTA particle based on their distance from the global best position.…”

Section: ) Modificaiton In Learning Strategymentioning

confidence: 99%

“…Two real-world applications known as gear train design [57] and spread spectrum radar polyphase code design [77] are considered in this subsection to evaluate the feasibility of MPSOEG to handle the engineering problems with different complexity levels. The first engineering application aims to optimize the gear ratios of a compound gear train system consisting of three gears.…”

Section: ) Comparitive Studies Using Real-world Engineering Applicatmentioning

confidence: 99%

“…This design problem has no polynomial time solution and it is defined as follows [77]: All six PSO variants that were employed in the previous simulation studies are applied to solve these two engineering design problems. For gear train design problem, the population size and maximum fitness evaluation numbers of all algorithms are S = 10 and FEmax = 2.00E+04, respectively based on [57]. The population size and maximum fitness evaluation numbers used by all PSO variants to solve the spread spectrum radar polyphase code design problem are S = 100 and FEmax = 1.00E+06, respectively based on [79].…”

Section: ) Comparitive Studies Using Real-world Engineering Applicatmentioning

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: ) Comparitive Studies Using Real-world Engineering Applicatmentioning

confidence: 99%

Section: ) Comparitive Studies Using Real-world Engineering Applicatmentioning

confidence: 99%

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Modified Particle Swarm Optimization With Effective Guides

2020

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“…In addition to abovementioned algorithms, some other improved PSO algorithms include adaptive PSO based on clustering [28], a memetic PSO for dynamic multi-modal optimization [29], developmental swarm intelligence in PSO [30], parasitic behavior integrated PSO [31], genetic learning embedded PSO [32], diversity purposed neighborhood search enforced PSO [33], biogeography learning based PSO [34], a generalized theoretical deterministic model based PSO [35], parallel implementation based multi-swarm PSO [36], adaptive time-varying topology connectivity based PSO [37], jumping time-varying acceleration coefficients incorporated PSO [38], the global best-guided PSO [39], the inter swarm interactive learning strategy PSO [40], and the PSO with dual-level task allocation [41]. Although these variants of PSO algorithms improve the performance, they also suffer from the burden of the multi-parameter settings.…”

Section: A Some Pso Variantsmentioning

confidence: 99%

A Molecular Interactions-Based Social Learning Particle Swarm Optimization Algorithm

et al. 2020

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Social learning particle swarm optimization (SL-PSO) allows individuals to learn from others to improve the scalability with easy parameter settings. However, it still suffers from the poor convergence for those multi-modal problems due to the loss of swarm diversity. To improve both the diversity and the convergence, this paper proposes a novel algorithm to apply the mechanism of molecular interactions to SL-PSO, in which the molecular attraction aims to improve the convergence, and the molecular repulsion intends to enhance the diversity. In the experiments, we compare our algorithm with the SL-PSO algorithm and other representative PSO and evolutionary algorithms on 49 benchmark functions. The results show the performance of the proposed algorithm is better than that of the SL-PSO algorithm and other representative PSO and evolutionary algorithms on average. This work builds the solid foundation for the integration of the molecular interaction mechanism with PSO and other optimization algorithms.

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“…One of the main advantages of PSO is its easy implementation [ 12 ]. A large number of numerical experiments also prove that PSO has high convergence accuracy and a fast convergence speed [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

UCPSO: A Uniform Initialized Particle Swarm Optimization Algorithm with Cosine Inertia Weight

Zhang

Sheng

et al. 2021

Computational Intelligence and Neuroscience

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The particle swarm optimization algorithm (PSO) is a meta-heuristic algorithm with swarm intelligence. It has the advantages of easy implementation, high convergence accuracy, and fast convergence speed. However, PSO suffers from falling into a local optimum or premature convergence, and a better performance of PSO is desired. Some methods adopt improvements in PSO parameters, particle initialization, or topological structure to enhance the global search ability and performance of PSO. These methods contribute to solving the problems above. Inspired by them, this paper proposes a variant of PSO with competitive performance called UCPSO. UCPSO combines three effective improvements: a cosine inertia weight, uniform initialization, and a rank-based strategy. The cosine inertia weight is an inertia weight in the form of a variable-period cosine function. It adopts a multistage strategy to balance exploration and exploitation. Uniform initialization can prevent the aggregation of initial particles. It distributes initial particles uniformly to avoid being trapped in a local optimum. A rank-based strategy is employed to adjust an individual particle’s inertia weight. It enhances the swarm’s capabilities of exploration and exploitation at the same time. Comparative experiments are conducted to validate the effectiveness of the three improvements. Experiments show that the UCPSO improvements can effectively improve global search ability and performance.

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Particle swarm optimization with dual-level task allocation

Cited by 34 publications

References 73 publications

Modified Particle Swarm Optimization With Effective Guides

Modified Particle Swarm Optimization With Effective Guides

A Molecular Interactions-Based Social Learning Particle Swarm Optimization Algorithm

UCPSO: A Uniform Initialized Particle Swarm Optimization Algorithm with Cosine Inertia Weight

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