An Elite Pool-Based Big Bang-Big Crunch Metaheuristic for Data Clustering

Almarashdeh, Ibrahim; Jaradat, Ghaith M.; Ayob, Masri; Abu-Al-Aish, Ahmad; Alsmadi, Mutasem K.

doi:10.3844/jcssp.2018.1611.1626

Cited by 20 publications

(1 citation statement)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It proved that it is effective and consistent in solving these problems to optimality in most cases. The BBBC has been applied twice for the data clustering problem by [58,59]. It showed good performance as well as generated good quality results.…”

Section: Bbbc Descriptionmentioning

confidence: 99%

Applying the big bang-big crunch metaheuristic to large-sized operational problems

Qawqzeh

Jaradat

Al-Yousef

et al. 2020

IJECE

Self Cite

View full text Add to dashboard Cite

In this study, we present an investigation of comparing the capability of a big bang-big crunch metaheuristic (BBBC) for managing operational problems including combinatorial optimization problems. The BBBC is a product of the evolution theory of the universe in physics and astronomy. Two main phases of BBBC are the big bang and the big crunch. The big bang phase involves the creation of a population of random initial solutions, while in the big crunch phase these solutions are shrunk into one elite solution exhibited by a mass center. This study looks into the BBBC’s effectiveness in assignment and scheduling problems. Where it was enhanced by incorporating an elite pool of diverse and high quality solutions; a simple descent heuristic as a local search method; implicit recombination; Euclidean distance; dynamic population size; and elitism strategies. Those strategies provide a balanced search of diverse and good quality population. The investigation is conducted by comparing the proposed BBBC with similar metaheuristics. The BBBC is tested on three different classes of combinatorial optimization problems; namely, quadratic assignment, bin packing, and job shop scheduling problems. Where the incorporated strategies have a greater impact on the BBBC's performance. Experiments showed that the BBBC maintains a good balance between diversity and quality which produces high-quality solutions, and outperforms other identical metaheuristics (e.g. swarm intelligence and evolutionary algorithms) reported in the literature.

show abstract

Section: Bbbc Descriptionmentioning

confidence: 99%

Applying the big bang-big crunch metaheuristic to large-sized operational problems

Qawqzeh

Jaradat

Al-Yousef

et al. 2020

IJECE

Self Cite

View full text Add to dashboard Cite

show abstract

Deep Learning for COVID-19 Cases-Based XCR and Chest CT Images

Abuhamdah

Jaradat

Alsmadi

2021

Advances on Smart and Soft Computing

View full text Add to dashboard Cite

Enhanced adaptive-convergence in Harris’ hawks optimization algorithm

Mao,

Gui

2024

Artif Intell Rev

View full text Add to dashboard Cite

This paper presents a novel enhanced adaptive-convergence in Harris’ hawks optimization algorithm (EAHHO). In EAHHO, considering that Harris’ hawks will adopt different perching strategies and chasing styles according to the value of the escaping energy parameter E, nonlinear adaptive-convergence factor a is designed and adjusted to enhance the convergence and robustness of the algorithm. Moreover, the convergence and stability of EAHHO are proved mathematically by using the Markov chain theory and Lyapunov stability theory respectively. Moreover, numerical simulation results of 14 HHOs with different nonlinear convergence factors on 23 benchmark functions show that the nonlinear convergence factor of EAHHO is applicable to challenging problems with unknown search spaces, and the comparisons with the selected well-established algorithms on 56 test functions demonstrate that EAHHO performs competitively and effectively. Finally, the experiment results show that EAHHO algorithm also has a good performance to solve the optimization problems with relatively high dimensions and graph size of Internet of Vehicles routing problem.

show abstract

An Elite Pool-Based Big Bang-Big Crunch Metaheuristic for Data Clustering

Abstract: access article is distributed under a Creative Commons Attribution (CC-BY) 3.0 license.

Cited by 20 publications

References 46 publications

Applying the big bang-big crunch metaheuristic to large-sized operational problems

Applying the big bang-big crunch metaheuristic to large-sized operational problems

Deep Learning for COVID-19 Cases-Based XCR and Chest CT Images

Enhanced adaptive-convergence in Harris’ hawks optimization algorithm

Contact Info

Product

Resources

About