MOSOA: A new multi-objective seagull optimization algorithm. (1st April 2021)

• Record Type: Journal Article
• Authors: Dhiman, Gaurav; Singh, Krishna Kant; Soni, Mukesh; Nagar, Atulya; Dehghani, Mohammad; Slowik, Adam; Kaur, Amandeep; Sharma, Ashutosh; Houssein, Essam H.; Cengiz, Korhan
• Is Part Of: Expert systems with applications. Volume 167(2021)
• Publication Date: 2021-04-01
• Subjects: Convergence -- Diversity -- Pareto Solutions -- Multi-objective Optimization -- Seagull Optimization Algorithm -- Engineering Design Problems; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2020.114150
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: 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. 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.