Nature-Inspired Algorithms for Engineering Design Optimization

Submission Deadline: 31 December 2023 (closed) View: 131

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Summary

Researchers from many research communities find inspiration and interest in developing a wide range of nature-inspired optimization algorithms from the nature that emerged billions of years ago. Optimization problems with significant nonlinear constraints are present in the majority of real-world applications. Some of the earlier proposed conventional optimization approaches, including gradient-based methods, have failed to tackle these real-world problems, and the majority of solutions have been found using hit and trail methods. Therefore, nature-inspired optimization methods are used to address the highly nonlinear problems. The adaptability and capacity of these nature-inspired algorithms to solve NP-hard problems is primarily responsible for their success rate. Many algorithms have been created by drawing inspiration from the astonishing natural species like lions, fish, ants, insects, and birds etc. For challenging optimization problems and a variety of engineering applications, nature-inspired optimization techniques offer adaptable computational tools. Many academics around the world have been fascinated by the development of some advanced intelligent algorithms and its usefulness in tackling a wide variety of complicated engineering problems efficiently because nature-inspired algorithms are simple and adaptable in nature.

The aim of this Special Issue is to create a platform for researchers and academics working on traditional and new nature-inspired algorithms. The focus is on discussing recent parametric adaptations in these algorithms and their application to engineering design problems. As nature-inspired algorithms continue to evolve and find applications in various domains, there is a need to explore their capabilities, limitations, and possible enhancements. By addressing these aspects, the Special Issue aims to contribute to the advancement of nature-inspired algorithms and their effective utilization in solving complex engineering design problems.

Original contributions in the adaptive properties of nature-inspired algorithms are welcome. The topics of interest include, but are not limited to:

• Swarm Intelligence (SI)-based algorithms

• Bio-inspired (not SI-based) algorithms

• Physics and Chemistry based algorithms

• Adapting the parameters of various algorithms

• New self-adaptive operators in algorithms

• Hybrid algorithms

• Multi-algorithm strategies

• Theoretical analysis with respect to the self-adaptive operators

• Application of new algorithms to engineering design problems

• Application of new algorithms to real-world problems such as time series forecasting, wireless sensor networks, cognitive radios, image processing, antenna array design, and others.

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