Multi/Many-Objective Particle Swarm Optimization Algorithm Based on Competition Mechanism

Yang, Wusi; Chen, Li; Wang, Yi; Zhang, Maosheng

doi:10.1155/2020/5132803

Cited by 11 publications

(8 citation statements)

References 44 publications

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“…Moreover, particle swarm optimization can fall into a local optimum (needs a reasonable balance between convergence and diversity) [29]. Those results are similar to filter and wrapper methods [34] (more details about Filter and wrapper methods can be found in [31,34]).…”

Section: Introductionmentioning

confidence: 56%

“…In contrast to the feature selection field, few evolutionary algorithms are proposed in the literature [25,27]. Indeed, evolutionary feature selection algorithms have the dis-advantage of high computational cost [28] while convergence (close to the true Pareto front) and diversity of solutions (set of solutions as diverse as possible) are still two major difficulties [29].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, Zhou et al 2021 [31] noted that particle swarm optimisation may find a local optimum with high dimensional data. Some variants are suggested such as competitive swarm optimization operator [32] and multiswarm comprehensive learning particle swarm optimization [33], but tackling many-objective optimization is still a challenge [29].…”

Section: Introductionmentioning

confidence: 99%

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A Many-Objective Simultaneous Feature Selection and Discretization for LCS-Based Gesture Recognition

Otis

Vandewynckel

2021

Applied Sciences

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Discretization and feature selection are two relevant techniques for dimensionality reduction. The first one aims to transform a set of continuous attributes into discrete ones, and the second removes the irrelevant and redundant features; these two methods often lead to be more specific and concise data. In this paper, we propose to simultaneously deal with optimal feature subset selection, discretization, and classifier parameter tuning. As an illustration, the proposed problem formulation has been addressed using a constrained many-objective optimization algorithm based on dominance and decomposition (C-MOEA/DD) and a limited-memory implementation of the warping longest common subsequence algorithm (WarpingLCSS). In addition, the discretization sub-problem has been addressed using a variable-length representation, along with a variable-length crossover, to overcome the need of specifying the number of elements defining the discretization scheme in advance. We conduct experiments on a real-world benchmark dataset; compare two discretization criteria as discretization objective, namely Ameva and ur-CAIM; and analyze recognition performance and reduction capabilities. Our results show that our approach outperforms previous reported results by up to 11% and achieves an average feature reduction rate of 80%.

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Section: Introductionmentioning

confidence: 56%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Many-Objective Simultaneous Feature Selection and Discretization for LCS-Based Gesture Recognition

Otis

Vandewynckel

2021

Applied Sciences

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“…It randomly selects two particles in a non-dominated set to compete, and only the winning particles can be used to guide the particles to be optimized. CMaPSO [37] is proposed on the basis of CMOPSO, which adopts a new environment selection strategy relative to CMOPSO. In MCMOPSO [57], a new leader particle selection strategy is adopted, and an external archive set is used.…”

Section: Competitive Swarm Optimizermentioning

confidence: 99%

“…In LMOCSO [36], an accelerated speed update formula is used. In CMaPSO [37], a new environment selection mechanism different from CMOPSO is adopted. The above algorithm is single in the offspring generation strategy.…”

Section: Introductionmentioning

confidence: 99%

A Competitive Particle Swarm Algorithm Based on Vector Angles for Multi-Objective Optimization

2021

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Recently, the particle swarm algorithm (PSO) has demonstrated its effectiveness in solving multi-objective optimization problems. However, the performance of most existing multi-objective particle swarm algorithms depends largely on the global or individual best particles. Moreover, due to the rapid convergence of PSO in single objective optimization problems, PSO is prone to poorly distributed indicators when dealing with multi-objective optimization problems. To solve the above problems, we propose a multi-objective competitive particle swarm algorithm based on vector angles (VaCSO). Firstly, in order to remove the influence of global best particles or individual best particles on the algorithm, the competition mechanism is used. Secondly, in order to increase the diversity of solutions while maintaining the convergence of the algorithm, the population is clustered into two populations. Population 1 mainly considers the convergence of the solution in the offspring generation strategy. As a supplement, population 2 adds a new offspring generation strategy to maintain the distribution of the solution, and we innovatively proposed a three-particle competition to improve the distribution and diversity of particle swarms. Finally, based on vector angle information, we consider auxiliary learning to optimize the population gap, so as to improve the distribution of the algorithm. We have established two sets of comparative experiments to test the performance of VaCSO. We compared VaCSO with the currently popular multi-objective particle swarm optimizers and multi-objective evolutionary algorithms. Experimental results show that VaCSO has an excellent performance in convergence and distribution, and has a significant effect in optimizing quality.INDEX TERMS multi-objective optimization, competition particle swarm, competition mechanism, threeparticle competition, vector angle information, auxiliary learning.

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Preference-inspired coevolutionary algorithm with sparse autoencoder for many-objective optimization

Wang,

Zhang,

Song

et al. 2023

Soft Comput

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Multi/Many-Objective Particle Swarm Optimization Algorithm Based on Competition Mechanism

Cited by 11 publications

References 44 publications

A Many-Objective Simultaneous Feature Selection and Discretization for LCS-Based Gesture Recognition

A Many-Objective Simultaneous Feature Selection and Discretization for LCS-Based Gesture Recognition

A Competitive Particle Swarm Algorithm Based on Vector Angles for Multi-Objective Optimization

Preference-inspired coevolutionary algorithm with sparse autoencoder for many-objective optimization

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