On the Optimization of Systems Using AI Metaheuristics and Evolutionary Algorithms

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Tkach, Itshak and Blackwell, Tim. 2023. On the Optimization of Systems Using AI Metaheuristics and Evolutionary Algorithms. In: Chin-Yin Huang and Sang Won Yoon, eds. Systems Collaboration and Integeration. Cham, Switzerland: Springer, pp. 253-271. ISBN 9783031443725 [Book Section]

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Official URL: https://link.springer.com/chapter/10.1007/978-3-03...

Abstract or Description

In this chapter, evolutionary computation techniques, algo- rithms and research are presented for the optimization and allocation problems. Several aspects of continuous optimization, systems security and supply networks (SN) are illustrated. The real-life optimization and security problems in systems, automation, SN and law enforcement are NP-hard optimization problems, thus evolutionary algorithms (EA) that employ metaheuristic methods are useful for solving them. EA gain sig- nificant interest in recent years, and this chapter summarizes some of the advances in that field and then summarizes their applications for real-life problems. The rest of this chapter is organized as follows. First, the introduction of the developments of nature-inspired EAs and meta- heuristics is described. Then the working principles of genetic algorithms (GA), swarm intelligence, and other nature-inspired optimization algo- rithms are given. Next, the overview of the various applications that were solved and optimized by EAs is presented. The reader of this chapter will be familiar with the following topics: The state-of-the-art AI algorithms and techniques and their working principle. The way to harness AI for optimization and finding optimal solutions. Controlling and optimizing a collaborative system in real-time while addressing several tasks in a complex environment

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Book Section

Identification Number (DOI):

https://doi.org/10.1007/978-3-031-44373-2_15