Multi-Swarm Whale Optimization Algorithm for Data Clustering Problems using Multiple Cooperative Strategies

Saidala, Ravi Kumar; Devarakonda, Nagaraju

doi:10.5815/ijisa.2018.08.04

Cited by 10 publications

(7 citation statements)

References 25 publications

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“…Swarm-inspired meta-heuristics include algorithms that replicate the social and biological characteristics of organisms, such as mating, labor division, foraging, navigation, and self-organization. Examples of social network optimizers include the multi-swarm whale optimization algorithm [32], genetic algorithm [33], multitracker optimization algorithm [34], and parallel multiobjective evolutionary algorithm [35].…”

Section: Introductionmentioning

confidence: 99%

A Feature Selection Based on Improved Artificial Hummingbird Algorithm Using Random Opposition-Based Learning for Solving Waste Classification Problem

Ali

AbdElminaam

2022

Mathematics

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Recycling tasks are the most effective method for reducing waste generation, protecting the environment, and boosting the overall national economy. The productivity and effectiveness of the recycling process are strongly dependent on the cleanliness and precision of processed primary sources. However, recycling operations are often labor intensive, and computer vision and deep learning (DL) techniques aid in automatically detecting and classifying trash types during recycling chores. Due to the dimensional challenge posed by pre-trained CNN networks, the scientific community has developed numerous techniques inspired by biology, swarm intelligence theory, physics, and mathematical rules. This research applies a new meta-heuristic algorithm called the artificial hummingbird algorithm (AHA) to solving the waste classification problem based on feature selection. However, the performance of the AHA is barely satisfactory; it may be stuck in optimal local regions or have a slow convergence. To overcome these limitations, this paper develops two improved versions of the AHA called the AHA-ROBL and the AHA-OBL. These two versions enhance the exploitation stage by using random opposition-based learning (ROBL) and opposition-based learning (OBL) to prevent local optima and accelerate the convergence. The main purpose of this paper is to apply the AHA-ROBL and AHA-OBL to select the relevant deep features provided by two pre-trained models of CNN (VGG19 & ResNet20) to recognize a waste classification. The TrashNet dataset is used to verify the performance of the two proposed approaches (the AHA-ROBL and AHA-OBL). The effectiveness of the suggested methods (the AHA-ROBL and AHA-OBL) is compared with that of 12 modern and competitive optimizers, namely the artificial hummingbird algorithm (AHA), Harris hawks optimizer (HHO), Salp swarm algorithm (SSA), aquila optimizer (AO), Henry gas solubility optimizer (HGSO), particle swarm optimizer (PSO), grey wolf optimizer (GWO), Archimedes optimization algorithm (AOA), manta ray foraging optimizer (MRFO), sine cosine algorithm (SCA), marine predators algorithm (MPA), and rescue optimization algorithm (SAR). A fair evaluation of the proposed algorithms’ performance is achieved using the same dataset. The performance analysis of the two proposed algorithms is applied in terms of different measures. The experimental results confirm the two proposed algorithms’ superiority over other comparative algorithms. The AHA-ROBL and AHA-OBL produce the optimal number of selected features with the highest degree of precision.

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Section: Introductionmentioning

confidence: 99%

A Feature Selection Based on Improved Artificial Hummingbird Algorithm Using Random Opposition-Based Learning for Solving Waste Classification Problem

Ali

AbdElminaam

2022

Mathematics

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“…Computational intelligence (CI) is growing its importance in many fields, like performing effective forecasts exploiting surrogate models, improving risk management, 4 performing effective data mining, 5 clustering, 6 developing effective controllers,? and many other applications.…”

Section: Introductionmentioning

confidence: 99%

Optimization of electric vehicles charging station deployment by means of evolutionary algorithms

Niccolai

Bettini

Zich

2021

Int J of Intelligent Sys

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Due to the growing importance of electric vehicles, charging stations (CS) deployment is becoming an important issue in many cities. The aim of this paper is to introduce a novel evolutionary‐based approach for solving the CS deployment problem. This study investigates many aspects of the formulation of this approach, such as the design variables selection and the definition of a feasibility function, to improve both effectiveness and flexibility. In particular, the latter is a key factor compared to many other state‐of‐the‐art approaches: in fact, it can be used with most of the available Evolutionary Algorithms (EAs) and can manage different quality‐of‐service performance parameters. The proposed approach is successfully compared with a greedy optimization on the case study of the City of Milan (Italy) using four different EAs. Two different performance parameters have been defined and used to prove the flexibility of the proposed approach. The results show its very good convergence rate and the quality of the obtained solutions.

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“…The first approach, i.e. partitionbased clustering approach, the dataset is partitioned into homogeneous groups using some proximity function (Saidala, R. K., & Devarakonda, N., 2018a;Xu, R., & Wunsch, D., 2005).In case of hierarchic clustering approach the data is grouped over a variety of scales by generating a cluster tree or dendogram (Murtagh, F., 1983;Saidala, R. K., & Devarakonda, N. R., 2017c). Density-based clustering approach, considers local density conditions instead of proximity between objects.…”

Section: Introductionmentioning

confidence: 99%

“…Density-based clustering approach, considers local density conditions instead of proximity between objects. (Kriegel, H. P., Et al., 2011&Saidala, R. K., & Devarakonda, N. R., 2017c). The last approach, i.e.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Between TOA and Lévy Flight Trajectory for Solving Different Cluster Problems

Devarakonda

Saidala

Kamarajugadda³

2021

International Journal of Cognitive Informatics and Natural Intelligence

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In data analysis applications for extraction of useful knowledge, clustering plays an important role. The major shortcoming of traditional clustering algorithms is exhibiting poor performance in solving complex data cluster problems. This research paper introduces a novel hybrid optimization technique based clustering approach. This paper is designed with two main objectives: designing efficient function optimization algorithm and developing advanced data clustering approach. In achieving the first objective, the standard TOA is first enhanced by hybridizing with Lévy flight trajectory and benchmarked on 23 functions. A new clustering approach is developed by conjoining k-means algorithm and Lévy flight TOA. Tested the numerical complexity of the proposed novel clustering approach on 10 UCI clustering datasets and 4 web document cluster problems. Conducted several simulation experiments and done an analysis of the results. The obtained graphical and statistical analysis reveals that the proposed novel clustering approach yields better quality clusters.

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Multi-Swarm Whale Optimization Algorithm for Data Clustering Problems using Multiple Cooperative Strategies

Cited by 10 publications

References 25 publications

A Feature Selection Based on Improved Artificial Hummingbird Algorithm Using Random Opposition-Based Learning for Solving Waste Classification Problem

A Feature Selection Based on Improved Artificial Hummingbird Algorithm Using Random Opposition-Based Learning for Solving Waste Classification Problem

Optimization of electric vehicles charging station deployment by means of evolutionary algorithms

A Hybrid Between TOA and Lévy Flight Trajectory for Solving Different Cluster Problems

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