Initialization and Diversity in Optimization AlgorithmsAuthor(s): Mario A. Navarro Velzquez, Bernardo Morales-Castaeda, Itzel Aranguren, Diego Oliva, Marco Perez-Cisneros\nFormat: Hardback\nPublisher: Taylor & Francis Ltd, United Kingdom\nImprint: CRC Press\nISBN-13: 9781032695815, 978-1032695815\nSynopsis\nDesigning new algorithms in swarm intelligence is a complex undertaking. Two critical factors have been seen to have a direct correlation with positive results. First is initialization, which serves as the initial step for all swarm intelligence techniques. Candidate solutions are generated to form the initial population, which are subsequently modified during the iterative process. A well-initialized population increases the algorithm's chances of avoiding local optima and finding the global optimum in fewer iterations. Although random distributions are commonly used for initialization, there are various ways to initialize the population elements.\n\nMaintaining diversity amo.
Initialization and Diversity in Optimization AlgorithmsAuthor(s): Mario A. Navarro Velzquez, Bernardo Morales-Castaeda, Itzel Aranguren, Diego Oliva, Marco Perez-Cisneros\nFormat: Hardback\nPublisher: Taylor & Francis Ltd, United Kingdom\nImprint: CRC Press\nISBN-13: 9781032695815, 978-1032695815\nSynopsis\nDesigning new algorithms in swarm intelligence is a complex undertaking. Two critical factors have been seen to have a direct correlation with positive results. First is initialization, which serves as the initial step for all swarm intelligence techniques. Candidate solutions are generated to form the initial population, which are subsequently modified during the iterative process. A well-initialized population increases the algorithm's chances of avoiding local optima and finding the global optimum in fewer iterations. Although random distributions are commonly used for initialization, there are various ways to initialize the population elements.\n\nMaintaining diversity amo.
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