Computational Intelligence for Solving Complex Optimization Problems

¹ College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432 Saudi Arabia. Department of Computer Science and Engineering, University Centre for Research and Development, Chandigarh University, Mohali 140413, India
² Associate Professor Department of Artificial intelligence and Data Science SSM Institute of Engineering and Technology
³ Assistant Professor, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai – 127
⁴ College of technical engineering, The Islamic university, Najaf, Iraq
⁵ Tashkent State Pedagogical University, Tashkent, Uzbekistan
⁶ Professor, Department of mechanical Engineering, K. Ramakrishnan college of technology, Tiruchirappalli

^* Correspondingauthor: sgkhan@imamu.edu.sa
vijayaraj.t_mech@psvpec.in
laith.h.alzubaidi@gmail.com
barnoabdullaeva1983@gmail.com
vijayan.me@gmail.com

Abstract

Complex optimization issues may now be solved using computational intelligence (CI), which has shown to be a powerful and diverse discipline. Traditional optimization approaches frequently struggle to offer efficient and effective solutions because real-world situations are becoming more complicated. Evolutionary algorithms, neural networks, fuzzy systems, and swarm intelligence are just a few examples of the many methods that fall under the umbrella of computational intelligence and are inspired by both natural and artificial intelligence. This abstract examines how computational intelligence techniques are used to solve complicated optimization issues, highlighting their benefits, drawbacks, and most recent developments. In this, computational intelligence techniques provide a potent and adaptable solution for resolving challenging optimization issues. They are highly adapted for dealing with the non-linear connections, uncertainties, and multi-objective situations that arise in real-world problems. The limits of computational intelligence have recently been pushed by recent developments in hybrid techniques and metaheuristics, even if obstacles in algorithm design and parameter tuning still exist. Computational intelligence is anticipated to play an increasingly significant role in tackling complicated optimization issues and fostering innovation across a variety of disciplines as technology continues to advance.