Recent Advances and Application of Metaheuristic Algorithms: A Survey (2014–2020)

Khanduja, Neha; Bhushan, Bharat

doi:10.1007/978-981-15-7571-6_10

Cited by 35 publications

(13 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The mathematical transcription of this multi-objective optimization problem is as follows [28,[78][79][80][81][82]: The solution 𝐵 𝑟 to this multi-objective optimization problem is generally not unique and not optimal because they represent a compromise on the two objectives to be achieved. Indeed, the excessive reduction of the number of features could decrease the accuracy.…”

Section: Mathematical Modeling Of the Wrapper Feature Selection In A ...mentioning

confidence: 99%

BHHO-EAS metaheuristic applied to the NP-Hard wrapper feature selection multi-objective optimization problem

SASSI,

CHELOUAH

2024

Preprint

View full text Add to dashboard Cite

Faced with the increase in high-dimensional Big Data creating more volume and complexity, the feature selection process became an essential phase in the preprocessing workflow upstream of the design of systems based on deep learning. This paper is a concrete and first application of the new metaheuristic Harris Hawk Optimization Encirclement-Attack-Synergy (HHO-EAS) in solving the NP-Hard wrapper feature selection multi-objective optimization problem. This problem combines two contradictory objectives: maximizing the accuracy of a classifier while minimizing the number of the most relevant and non-redundant selected features. To do this we hybridized HHO-EAS to create the new metaheuristic Binary HHO-EAS (BHHO-EAS). We combined HHO-EAS to the sixteen transfer functions most used in the literature structured in a balanced way among the four main categories including S-Shaped, V-Shaped, Q-Shaped and U-Shaped. This wide range of transfer function allows us to analyze the evolution of BHHO-EAS’s skills according to the assigned transfer function and to determine which of them offer the best performances. We applied wrapper feature selection to the well-known NSL-KDD dataset with the deep learning Multi Layer Perceptron (MLP) classifier. We put BHHO-EAS in competition with three other well-known population based binary metaheuristics, BPSO, BBA and BHHO. The analysis of the experimental results, compared to the three other binary metaheuristics, demonstrated that BHHO-EAS obtained the best performance on 100% of the transfer functions. This is more particularly highlighted by the U-Shaped transfer functions, which give an acceptable compromise for the two objectives of the problem with an average accuracy of 96,4% and an average size of selected features of 20.

show abstract

Section: Mathematical Modeling Of the Wrapper Feature Selection In A ...mentioning

confidence: 99%

BHHO-EAS metaheuristic applied to the NP-Hard wrapper feature selection multi-objective optimization problem

SASSI,

CHELOUAH

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Eventually, researchers developed a list of modern techniques to overcome the difficulties faced in estimating the unknown parameter value of nonlinear regression models. These alternative methods named meta-heuristic algorithms employ a higher level procedure to provide a sufficiently good solution close enough to the exact solutions with limited assumptions and computation resources (Khanduja and Bhushan, 2021). Most of these algorithms are designed to mimic natural, social or biological activities of living things in order to find best points for an objective function.…”

Section: Introductionmentioning

confidence: 99%

Estimating Nonlinear Regression Parameters using Particle Swarm Optimization and Genetic Algorithm

Emmanuel

Okoye

Ezenweke

et al. 2023

FJS

View full text Add to dashboard Cite

Obtaining parameter estimates for nonlinear regression model using gauss-newton and gradient-based methods present some complex analytical challenges. In this paper we investigated the effectiveness and simplicity of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) on five nonlinear regression models with varying level of complexities. We developed function in R-programming for each models and performed 30 independent runs for at least 100 iterations for both PSO and GA. We evaluated PSO and GA performance in view of computation time, residual error produced and compared our results with values published online. Based on the results obtained, PSO significantly outperform GA in view of computation time and quality of parameter estimates. Even so, GA required fewer iterations and produced fairly accurate results. Further investigation shows that PSO and GA are both competitive, effective, simple to implement, and can be considered reliable for obtaining the parameter estimates of different nonlinear regression tasks.

show abstract

“…Such complex problems require the algorithm to efficiently and effectively explore their associated search space to find good solutions. Populationbased meta-heuristics have been the dominant methods to find optimal or near-optimal solutions to many optimization problems within a reasonable time [1]. These meta-heuristics derive their inspiration from mimicking intelligent processes arising in nature.…”

Section: Introductionmentioning

confidence: 99%

“…Figure 1 illustrates how the sine and cosine functions affect the movement of search agents with respect to the destination in the range [-2, 2]. As shown in Figure 1, exploration is identified by the regions [-2, -1) and (1,2] while exploitation happens between [-1, 1]. Figure 1 depicts how the position of a solution is updated to the next random location either inward or outward as compared to the destination.…”

mentioning

confidence: 99%

Parallel and Distributed Implementation of Sine Cosine Algorithm on Apache Spark Platform

2021

View full text Add to dashboard Cite

The Sine Cosine Algorithm (SCA) has experienced wide spread use in solving optimization problems in many disciplines mainly due to its simplicity and efficiency. However, like many other metaheuristics, SCA requires considerable amount of compute time when solving large size optimization problems. Therefore, in order to tackle such challenging problems efficiently, this work proposes Spark-SCA, a scalable and parallel implementation of SCA algorithm on Apache Spark distributed framework. Spark-SCA exploits Spark platform native support for iterative algorithms through in-memory computing to speed-up computations when handling large optimization problems. Both the design and implementation details of Spark-SCA are presented herein. The performance of Spark-SCA was compared to standard SCA on different benchmark functions with up to 1,000-dimension as well as three practical engineering design problems. Simulation experiments conducted on Amazon Web Services (AWS) public cloud demonstrated how Spark-SCA outperforms the standard version in terms of solution quality and run time as well as it competitiveness in exploring solution space of complex optimization problems.

show abstract

Recent Advances and Application of Metaheuristic Algorithms: A Survey (2014–2020)

Cited by 35 publications

References 34 publications

BHHO-EAS metaheuristic applied to the NP-Hard wrapper feature selection multi-objective optimization problem

BHHO-EAS metaheuristic applied to the NP-Hard wrapper feature selection multi-objective optimization problem

Estimating Nonlinear Regression Parameters using Particle Swarm Optimization and Genetic Algorithm

Parallel and Distributed Implementation of Sine Cosine Algorithm on Apache Spark Platform

Contact Info

Product

Resources

About