Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

**Integrated particle swarm and evolutionary algorithm approaches to the quadratic assignment problem.** / Helal, Ayah M.; Jawdat, Enas; Elnabarawy, Islam; Wunsch, Donald C.; Abdelbar, Ashraf M.

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Helal, AM, Jawdat, E, Elnabarawy, I, Wunsch, DC & Abdelbar, AM 2018, Integrated particle swarm and evolutionary algorithm approaches to the quadratic assignment problem. in *2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings.* 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-8, 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017, Honolulu, United States, 27/11/2017. https://doi.org/10.1109/SSCI.2017.8280797

Helal, A. M., Jawdat, E., Elnabarawy, I., Wunsch, D. C., & Abdelbar, A. M. (2018). Integrated particle swarm and evolutionary algorithm approaches to the quadratic assignment problem. In *2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings *(pp. 1-8). (2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI.2017.8280797

Helal AM, Jawdat E, Elnabarawy I, Wunsch DC, Abdelbar AM. Integrated particle swarm and evolutionary algorithm approaches to the quadratic assignment problem. In 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1-8. (2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings). https://doi.org/10.1109/SSCI.2017.8280797

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title = "Integrated particle swarm and evolutionary algorithm approaches to the quadratic assignment problem",

abstract = "This paper introduces three integrated hybrid approaches that apply a combination of Hierarchical Particle Swarm Optimization (HPSO) and Evolutionary Algorithms (EA) to the Quadratic Assignment Problem (QAP). The approaches maintain a single population. In the first approach, Alternating HPSO-EA (AHE), the population alternates between applying HPSO and EA in successive generations. In the second, more integrated approach, Integrated HPSO-EA (IHE), each population element chooses to apply one of the two algorithms in each generation with some probability. An element applying HPSO in a given generation can be influenced by an element applying EA in that generation, and vice versa. Thus, within the same generation, some elements act as HPSO particles and others as EA population members, and yet the entire population still cooperates. In the third approach, we present a Social Evolutionary Algorithm (SEA), in which the population applies EA, and each population element can choose to apply the PSO-style social mutation operator in each generation with some probability. The three approaches are compared to HPSO and EA using 31 instances of varying size from the QAP instance library.",

author = "Helal, {Ayah M.} and Enas Jawdat and Islam Elnabarawy and Wunsch, {Donald C.} and Abdelbar, {Ashraf M.}",

year = "2018",

month = feb,

day = "2",

doi = "10.1109/SSCI.2017.8280797",

language = "English",

series = "2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1--8",

booktitle = "2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings",

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note = "2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 ; Conference date: 27-11-2017 Through 01-12-2017",

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AU - Jawdat, Enas

AU - Elnabarawy, Islam

AU - Wunsch, Donald C.

AU - Abdelbar, Ashraf M.

PY - 2018/2/2

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N2 - This paper introduces three integrated hybrid approaches that apply a combination of Hierarchical Particle Swarm Optimization (HPSO) and Evolutionary Algorithms (EA) to the Quadratic Assignment Problem (QAP). The approaches maintain a single population. In the first approach, Alternating HPSO-EA (AHE), the population alternates between applying HPSO and EA in successive generations. In the second, more integrated approach, Integrated HPSO-EA (IHE), each population element chooses to apply one of the two algorithms in each generation with some probability. An element applying HPSO in a given generation can be influenced by an element applying EA in that generation, and vice versa. Thus, within the same generation, some elements act as HPSO particles and others as EA population members, and yet the entire population still cooperates. In the third approach, we present a Social Evolutionary Algorithm (SEA), in which the population applies EA, and each population element can choose to apply the PSO-style social mutation operator in each generation with some probability. The three approaches are compared to HPSO and EA using 31 instances of varying size from the QAP instance library.

AB - This paper introduces three integrated hybrid approaches that apply a combination of Hierarchical Particle Swarm Optimization (HPSO) and Evolutionary Algorithms (EA) to the Quadratic Assignment Problem (QAP). The approaches maintain a single population. In the first approach, Alternating HPSO-EA (AHE), the population alternates between applying HPSO and EA in successive generations. In the second, more integrated approach, Integrated HPSO-EA (IHE), each population element chooses to apply one of the two algorithms in each generation with some probability. An element applying HPSO in a given generation can be influenced by an element applying EA in that generation, and vice versa. Thus, within the same generation, some elements act as HPSO particles and others as EA population members, and yet the entire population still cooperates. In the third approach, we present a Social Evolutionary Algorithm (SEA), in which the population applies EA, and each population element can choose to apply the PSO-style social mutation operator in each generation with some probability. The three approaches are compared to HPSO and EA using 31 instances of varying size from the QAP instance library.

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T3 - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings

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BT - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

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