Opposition and dimensional based modified firefly algorithm

Verma, Om Prakash; Aggarwal, Deepti; Patodi, Tejna

doi:10.1016/j.eswa.2015.08.054

Cited by 107 publications

(48 citation statements)

References 14 publications

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“…zero) in the landscapes defined by the test functions. The FA variants implemented for performance comparison include Opposition and Dimensional based modified FA (ODFA) (Verma et al, 2016), CFA (Kazem et al, 2013), LSFA (Alweshah and Abdullah, 2015), SFA (Alweshah and Abdullah, 2015), NaFA (Wang et al, 2017), and HFDE (Dash et al, 2017). The classical search methods included for comparison are FA, SA, Bat Swarm Optimization (BSO), CS, PSO, Dragonfly Algorithm (DA) (Mirjalili, 2016a) and ALO (Mirjalili, 2015a).…”

Section: Evaluation Using Standard Benchmark Functionsmentioning

confidence: 99%

Classifier ensemble reduction using a modified firefly algorithm: An empirical evaluation

Zhang

Srisukkham

Neoh

et al. 2018

Expert Systems with Applications

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In this research, we propose a variant of the firefly algorithm (FA) for classifier ensemble reduction. It incorporates both accelerated attractiveness and evading strategies to overcome the premature convergence problem of the original FA model. The attractiveness strategy takes not only the neighbouring but also global best solutions into account, in order to guide the firefly swarm to reach the optimal regions with fast convergence while the evading action employs both neighbouring and global worst solutions to drive the search out of gloomy regions. The proposed algorithm is subsequently used to conduct discriminant base classifier selection for generating optimized ensemble classifiers without compromising classification accuracy. Evaluated with standard, shifted, and composite test functions, as well as the Black-Box Optimization Benchmarking test suite and several high dimensional UCI data sets, the empirical results indicate that, based on statistical tests, the proposed FA model outperforms other state-of-the-art FA variants and classical metaheuristic search methods in solving diverse complex unimodal and multimodal optimization and ensemble reduction problems. Moreover, the resulting ensemble classifiers show superior performance in comparison with those of the original, full-sized ensemble models.

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Section: Evaluation Using Standard Benchmark Functionsmentioning

confidence: 99%

Classifier ensemble reduction using a modified firefly algorithm: An empirical evaluation

Zhang

Srisukkham

Neoh

et al. 2018

Expert Systems with Applications

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show abstract

“…In 2010, Zhao et al proposed FA to solve optimized problem illuminated by the relationship of firefly population (FP) [26][27][28]. The initial population of N F fireflies is initialized based on Equation 23.…”

Section: Basic Firefly Algorithmmentioning

confidence: 99%

Solving the Multi-Objective Optimal Power Flow Problem Using the Multi-Objective Firefly Algorithm with a Constraints-Prior Pareto-Domination Approach

Chen

Zhang

et al. 2018

Energies

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Known as a multi-objective, large-scale, and complicated optimization problem, the multi-objective optimal power flow (MOOPF) problem tends to be introduced with many constraints. In this paper, compared with the frequently-used penalty function-based method (PFA), a novel constraint processing approach named the constraints-prior Pareto-domination approach (CPA) is proposed for ensuring non-violation of various inequality constraints on dependent variables by introducing the Pareto-domination principle based on the sum of constraint violations. Moreover, for solving the constrained MOOPF problem, the multi-objective firefly algorithm with CPA (MOFA-CPA) is proposed and some optimization strategies, such as the crowding distance calculation and non-dominated sorting based on the presented CPA, are utilized to sustain well-distributed Pareto front (PF). Finally, in order to demonstrate the feasible and effective improvement of MOFA-CPA, a comparison study between MOFA-CPA and MOFA-PFA is performed on two test systems, including three bi-objective optimization cases and three tri-objective optimization cases. The simulation results demonstrate the capability of the MOFA-CPA for obtaining PF with good distribution and superiority of the proposed CPA for dealing with inequality constraints on dependent variables. In addition, some quality indicators are used to evaluate the convergence, distribution, and uniformity of the PFs found by the MOFA-CPA and MOFA-PFA.

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“…In this layer, the input weight and deviation will be randomly generated and least square method will be deployed to determine output weight analytically [17], which differentiates this method from traditional methods. In this phase, learning happens followed by finding transformation matrix [93][94][95][96][97][98][99][100][101][102][103]. It is deployed to minimize the sum-of-squares error function.…”

Section: Machine Learning and Classification Methodsmentioning

confidence: 99%

Literature Review on Big Data Analytics Methods

Vanani¹,

Majidian²

2020

Social Media and Machine Learning

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Companies and industries are faced with a huge amount of raw data, which have information and knowledge in their hidden layer. Also, the format, size, variety, and velocity of generated data bring complexity for industries to apply them in an efficient and effective way. So, complexity in data analysis and interpretation incline organizations to deploy advanced tools and techniques to overcome the difficulties of managing raw data. Big data analytics is the advanced method that has the capability for managing data. It deploys machine learning techniques and deep learning methods to benefit from gathered data. In this research, the methods of both ML and DL have been discussed, and an ML/DL deployment model for IOT data has been proposed.

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Opposition and dimensional based modified firefly algorithm

Cited by 107 publications

References 14 publications

Classifier ensemble reduction using a modified firefly algorithm: An empirical evaluation

Classifier ensemble reduction using a modified firefly algorithm: An empirical evaluation

Solving the Multi-Objective Optimal Power Flow Problem Using the Multi-Objective Firefly Algorithm with a Constraints-Prior Pareto-Domination Approach

Literature Review on Big Data Analytics Methods

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