MOSOA: A new multi-objective seagull optimization algorithm

•A novel Multi-objective Seagull Optimization Algorithm is proposed.•The algorithm is tested on 24 real challenging benchmark test function.•The results show the superior convergence behaviour of proposed algorithm.•The results on engineering design problems prove its efficiency and applicability. T...

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Bibliographic Details
Published in:Expert systems with applications Vol. 167; p. 114150
Main Authors: Dhiman, Gaurav, Singh, Krishna Kant, Soni, Mukesh, Nagar, Atulya, Dehghani, Mohammad, Slowik, Adam, Kaur, Amandeep, Sharma, Ashutosh, Houssein, Essam H., Cengiz, Korhan
Format: Journal Article
Language:English
Published: New York Elsevier Ltd 01.04.2021
Elsevier BV
Subjects:
Algorithms
Convergence
Design engineering
Diversity
Empirical analysis
Engineering Design Problems
Heuristic methods
Multi-objective Optimization
Multiple objective analysis
Optimization
Optimization algorithms
Pareto optimum
Pareto Solutions
Seagull Optimization Algorithm
Pareto Solutions
Multi-objective Optimization
Seagull Optimization Algorithm
Engineering Design Problems
Diversity
Convergence
ISSN:0957-4174, 1873-6793
Online Access:Get full text
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Summary:•A novel Multi-objective Seagull Optimization Algorithm is proposed.•The algorithm is tested on 24 real challenging benchmark test function.•The results show the superior convergence behaviour of proposed algorithm.•The results on engineering design problems prove its efficiency and applicability. 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.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2020.114150