A new hierarchical multi group particle swarm optimization with different task allocations inspired by holonic multi agent systems

Roshanzamir, Mahdi; Balafar, Mohammad Ali; Razavi, Seyed Naser

doi:10.1016/j.eswa.2020.113292

Cited by 27 publications

(17 citation statements)

References 88 publications

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“…Additionally, a mutation scheme was also introduced in MPCSPSO to assist the stagnated particles in escaping from local optima regions. A holonic PSO with different task allocations (HPSO-DTA) was designed in [3]. All particles were assigned into different groups to perform exploration or exploitation searches based on the characteristics of exemplars created for each group.…”

Section: ) Modificaiton In Learning Strategymentioning

confidence: 99%

“…The rapid technological advancement in Industry Revolution 4.0 has led to the deployment of various engineering systems that can be described as the complicated optimization models with non-differentiable, non-linear, discontinuous and multimodal characteristics. Traditional mathematical programming approaches such as linear programming [1], quadratic programming [2], Newton's method [3] and etc. are not able to solve these challenging optimization problems effectively due to their drawbacks of limited global search, strong dependency on gradient information and poor guessing of initial solutions [3].…”

Section: Introductionmentioning

confidence: 99%

“…Traditional mathematical programming approaches such as linear programming [1], quadratic programming [2], Newton's method [3] and etc. are not able to solve these challenging optimization problems effectively due to their drawbacks of limited global search, strong dependency on gradient information and poor guessing of initial solutions [3]. Metaheuristic search algorithms emerge as promising candidate optimizers to solve these challenging modern engineering optimization problems by leveraging the inspiration from different behaviors of living being or physical phenomena.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Modified Particle Swarm Optimization With Effective Guides

2020

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Gene selection for high dimensional biological datasets using hybrid island binary artificial bee colony with chaos game optimization

Nssibi,

Manita,

Chhabra

et al. 2024

Artif Intell Rev

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Microarray technology, as applied to the fields of bioinformatics, biotechnology, and bioengineering, has made remarkable progress in both the treatment and prediction of many biological problems. However, this technology presents a critical challenge due to the size of the numerous genes present in the high-dimensional biological datasets associated with an experiment, which leads to a curse of dimensionality on biological data. Such high dimensionality of real biological data sets not only increases memory requirements and training costs, but also reduces the ability of learning algorithms to generalise. Consequently, multiple feature selection (FS) methods have been proposed by researchers to choose the most significant and precise subset of classified genes from gene expression datasets while maintaining high classification accuracy. In this research work, a novel binary method called iBABC-CGO based on the island model of the artificial bee colony algorithm, combined with the chaos game optimization algorithm and SVM classifier, is suggested for FS problems using gene expression data. Due to the binary nature of FS problems, two distinct transfer functions are employed for converting the continuous search space into a binary one, thus improving the efficiency of the exploration and exploitation phases. The suggested strategy is tested on a variety of biological datasets with different scales and compared to popular metaheuristic-based, filter-based, and hybrid FS methods. Experimental results supplemented with the statistical measures, box plots, Wilcoxon tests, Friedman tests, and radar plots demonstrate that compared to prior methods, the proposed iBABC-CGO exhibit competitive performance in terms of classification accuracy, selection of the most relevant subset of genes, data variability, and convergence rate. The suggested method is also proven to identify unique sets of informative, relevant genes successfully with the highest overall average accuracy in 15 tested biological datasets. Additionally, the biological interpretations of the selected genes by the proposed method are also provided in our research work.

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