A Novel Many-Objective Sine–Cosine Algorithm (MaOSCA) for Engineering Applications

Rama Chandran Narayanan, Narayanan Ganesh, Robert Čep (), Pradeep Jangir, Jasgurpreet Singh Chohan and Kanak Kalita ()
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Rama Chandran Narayanan: Department of Computer Science and Engineering, Sona College of Technology, Salem 636005, India
Narayanan Ganesh: School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, India
Robert Čep: Department of Machining, Assembly and Engineering Metrology, Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, 70800 Ostrava, Czech Republic
Pradeep Jangir: Rajasthan Rajya Vidyut Prasaran Nigam, Losal, Jaipur 302006, India
Jasgurpreet Singh Chohan: Department of Mechanical Engineering, University Centre for Research & Development, Chandigarh University, Mohali 140413, India
Kanak Kalita: Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi 600062, India

Mathematics, 2023, vol. 11, issue 10, 1-28

Abstract: In recent times, numerous innovative and specialized algorithms have emerged to tackle two and three multi-objective types of problems. However, their effectiveness on many-objective challenges remains uncertain. This paper introduces a new Many-objective Sine–Cosine Algorithm (MaOSCA), which employs a reference point mechanism and information feedback principle to achieve efficient, effective, productive, and robust performance. The MaOSCA algorithm’s capabilities are enhanced by incorporating multiple features that balance exploration and exploitation, direct the search towards promising areas, and prevent search stagnation. The MaOSCA’s performance is evaluated against popular algorithms such as the Non-dominated sorting genetic algorithm-III (NSGA-III), the Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) integrated with Differential Evolution (MOEADDE), the Many-objective Particle Swarm Optimizer (MaOPSO), and the Many-objective JAYA Algorithm (MaOJAYA) across various test suites, including DTLZ1-DTLZ7 with 5, 9, and 15 objectives and car cab design, water resources management, car side impact, marine design, and 10-bar truss engineering design problems. The performance evaluation is carried out using various performance metrics. The MaOSCA demonstrates its ability to achieve well-converged and diversified solutions for most problems. The success of the MaOSCA can be attributed to the multiple features of the SCA optimizer integrated into the algorithm.

Keywords: many-objective optimization; Sine–Cosine Algorithm; reference point mechanism; information feedback model; MaOSCA (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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