Titlebook: Intelligent Optimization; Principles, Algorith Changhe Li,Shoufei Han,Shengxiang Yang Textbook 2024 China University of Geosciences Press 2

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Changhe Li,Shoufei Han,Shengxiang YangFundamental and typical intelligent optimization algorithms for a wide range of learners.Practical significance of cutting-edge applications for a diverse range of optimization problems in real-world

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for a diverse range of optimization problems in real-world .This textbook comprehensively explores the foundational principles, algorithms, and applications of intelligent optimization, making it an ideal resource for both undergraduate and postgraduate artificial intelligence courses. It remains e

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Textbook 2024urce for both undergraduate and postgraduate artificial intelligence courses. It remains equally valuable for active researchers and individuals engaged in self-study. Serving as a significant reference, it delves into advanced topics within the evolutionary computation field, including multi-object

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Canonical Optimization Algorithms greedy search, A* search, and Monte-Carlo tree search. Several single-solution-based metaheuristic search algorithms are also introduced in this chapter, e.g., hill climbing, simulated annealing, iterated local search, and variable neighborhood search.

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Introductioncategorized into continuous optimization problems and discrete optimization problems. Finally, optimization algorithms are introduced and categorized into deterministic algorithms and probabilistic algorithms, where several terms regarding intelligent optimization are introduced.

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Popular Evolutionary Computation Algorithmsrobability distribution models to generate offsprings. ES, EP, DE, and PSO are mainly used to solve continuous optimization problems, while EDA and ACO are proposed to solve combinatorial optimization problems. However, GA can solve both types of optimization problems by using different solution representations.