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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Khalid Shaker
University of Anbar
Computer Science & IT Electrical & Electronics Engineering

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Applying the big bang-big crunch metaheuristic to large-sized operational problems Yousef K. Qawqzeh; Ghaith Jaradat; Ali Al-Yousef; Anmar Abu-Hamdah; Ibrahim Almarashdeh; Mutasem Alsmadi; Mohammed Tayfour; Khalid Shaker; Firas Haddad
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (14.644 KB) | DOI: 10.11591/ijece.v10i3.pp2484-2502

Abstract

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.
Multi-dimensional cubic symmetric block cipher algorithm for encrypting big data Omar A. Dawood; Othman I. Hammadi; Khalid Shaker; Mohammed Khalaf
Bulletin of Electrical Engineering and Informatics Vol 9, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v9i6.2475

Abstract

The advanced technology in the internet and social media, communication companies, health care records and cloud computing applications made the data around us increase dramatically every minute and continuously. These renewals big data involve sensitive information such as password, PIN number, credential numbers, secret identifications and etc. which require maintaining with some high secret procedures. The present paper involves proposing a secret multi-dimensional symmetric cipher with six dimensions as a cubic algorithm. The proposed algorithm works with the substitution permutation network (SPN) structure and supports a high processing data rate in six directions. The introduced algorithm includes six symmetry rounds transformations for encryption the plaintext, where each dimension represents an independent algorithm for big data manipulation. The proposed cipher deals with parallel encryption structures of the 128-bit data block for each dimension in order to handle large volumes of data. The submitted cipher compensates for six algorithms working simultaneously each with 128-bit according to various irreducible polynomials of order eight. The round transformation includes four main encryption stages where each stage with a cubic form of six dimensions.
Co-Authors Ali Al-Yousef Anmar Abu-Hamdah Firas Haddad Ghaith Jaradat Ibrahim Almarashdeh Mohammed Khalaf Mohammed Tayfour Mutasem Alsmadi Omar A. Dawood Othman I. Hammadi Yousef K. Qawqzeh