Journal of Autonomous Intelligence

Computational Intelligence Algorithms for Engineering Design Problems: Theory, Practices and Applications

Submission deadline: 2024-02-29
Special Issue Editors

Special Issue Information

Dear Colleagues,

Computational Intelligence (CI) can be framed as a heterogeneous domain that harmonized and coordinated several technologies, such as probabilistic reasoning, artificial life, multi-agent systems, neuro-computing, fuzzy systems, and evolutionary algorithms. Integrating several disciplines, such as machine learning, artificial intelligence, metaheuristics, decision support systems, and database management systems increases the CI power and impact in several engineering applications. This special issue provides a well-standing forum to discuss the characteristics of CI systems in real-world engineering. It emphasizes on the development of CI techniques and their role as well as the state-of-the- art solutions in different real world engineering applications. The special issue is proposed for researchers, academics, scientists, engineers and professionals who are involved in the new techniques of CI. CI techniques including artificial fuzzy logic and neural networks are presented for biomedical image processing, healthcare, power electronics, control systems, power systems, other engineering fields, medicine, bioinformatics, telecommunication, logistics, agriculture, etc. Hot topics we would like to cover include large-scale search spaces, Big Data applications, combination of metaheuristics and machine learning, and dealing with fitness functions that are costly to compute. Use cases describing successful applications of metaheuristics in complex scenarios are welcome. This special issue intends to capture recent contributions of high-quality papers focusing on interdisciplinary research on the computational intelligence algorithm for engineering applications using modern computational intelligence theories, approaches, and experiments.

Prof. Dr. M. Premkumar,

Dr. Pradeep Jangir

Prof. Sowmya R

Guest Editors


Metaheuristics and Machine Learning; Big Data Applications; Hybrid Metaheuristics; Experiences in Adopting Metaheuristics in Difficult Real Scenarios; Optimization Algorithms; Engineering Design Problems; Constraint Handling; Complicated Optimization Problems; Industrial Problems.

Published Paper

In the modern era, time holds immense value, and individuals strive to avoid delays in their daily responsibilities. These fuel stations are time-consuming and rely on human labour for efficient operation. With each passing day, the number of vehicles and devices in our technologically advanced world continues to grow rapidly. As a result, customers wait in queues at fuel stations, fuelling their desire to transition to an automated fuel dispensing system and eliminate the manual fuel distribution process from their daily routines. This research paper introduces an innovative smart fuel dis-penser system that leverages RFID technology and IoT-based monitoring to enhance automotive fuelling processes. By addressing the limitations of conventional fuelling systems, this proposed system provides a superior solution that is more efficient and effective. Notably, it offers numerous benefits, such as improved accuracy, efficiency, safety, and sustaina-bility, thereby presenting potential cost savings for fuel station owners and operators. The ongoing project is focused on automating fuel dispensing stations using RFID technology as a highly efficient tool. This approach aims to reduce the traffic congestion typically seen in front of fuel stations by shortening the time required for fuel dispensing compared to traditional manual operations. To enhance control and monitoring capabilities, an Android application has been created. This app allows for the tracking of fuel transactions and transaction history for both customers and fuel station dealers. The system utilizes NodeMCU and the Android app as an Internet-of-Things platform for seamless communication be-tween the system, customers, and dealers. This study presents concrete evidence that supports the viability and potential advantages of the proposed system, emphasizing its capacity to revolutionize the fuelling industry and mitigate carbon emissions. The findings derived from the implemented system have been thoroughly examined, offering an intelligent solution for a sustainable future.