MOSOA: A New Multi-objective Seagull Optimization Algorithm

Dhiman, Gaurav and Singh, Krishna Kant and Soni, Mukesh and Nagar, Atulya K. and Dehghani, Mohammad and Slowik, Adam and Kaur, Amandeep and Sharma, Ashutosh and Houssein, Essam H. and Cengiz, Korhan (2020) MOSOA: A New Multi-objective Seagull Optimization Algorithm. Expert Systems With Applications. ISSN 0957-4174

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Abstract

This study introduces the extension of currently developed Seagull Optimization Algorithm (SOA) in terms of multi-objective problems, which is entitled as Multi-objective Seagull Optimization Algorithm (MOSOA). In this algorithm, a concept of dynamic archive is introduced, which has the feature to cache the non-dominated Pareto optimal solutions. The roulette wheel selection approach is utilized to choose the effective archived solutions by simulating the migration and attacking behaviors of seagulls. The proposed algorithm is approved by testing it with twenty-four benchmark test functions, and its performance is compared with existing metaheuristic algorithms. The developed algorithm is analyzed on six constrained problems of engineering design to assess its appropriateness for finding the solutions of real-world problems. The outcomes from the empirical analyzes depict that the proposed algorithm is better than other existing algorithms. The proposed algorithm also considers those Pareto optimal solutions, which demonstrate high convergence.

Item Type: Article
Additional Information and Comments: NOTICE: this is the author’s version of a work that was accepted for publication in Expert Systems With Applications. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Expert Systems With Applications, November 2020, available online:https://www.sciencedirect.com/science/article/abs/pii/S0957417420308940
Keywords: Convergence; Diversity; Pareto Solutions; Multi-objective Optimization; Seagull Optimization Algorithm; Engineering Design Problems.
Faculty / Department: Faculty of Science > Mathematics and Computer Science
Depositing User: Atulya Nagar
Date Deposited: 12 Nov 2020 12:26
Last Modified: 12 Nov 2020 12:26
URI: https://hira.hope.ac.uk/id/eprint/3179

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