Book Description: 'Computational Intelligence-Based Optimization Algorithms from Theory to Practice' provides a comprehensive overview of optimization algorithms based on computational intelligence, including genetic algorithms, particle swarm optimization, ant colony optimization, and simulated annealing. This book covers the theoretical foundations of these algorithms, their practical applications, and case studies demonstrating their effectiveness in solving real-world problems. The authors emphasize the importance of understanding the underlying principles of these algorithms to apply them effectively in various fields such as engineering, finance, and biology. They also discuss the challenges and limitations of these algorithms and suggest future research directions in this rapidly evolving field. Long Detailed Description of the Plot: In the fast-paced world of technology, it is essential to understand the process of technological evolution and its impact on humanity. As we progress towards a more interconnected and automated society, it becomes increasingly important to develop a personal paradigm for perceiving the technological process of developing modern knowledge. This paradigm can serve as the basis for the survival of humanity and the unification of people in a warring state. In 'Computational Intelligence-Based Optimization Algorithms from Theory to Practice', we explore the need and possibility of developing such a paradigm.

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