04044nam a22004695i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050000900153072001600162072002300178072001500201100007500216245018300291250001800474264006900492300006400561336002600625337002600651338003600677347002400713490006200737505073800799520139201537650003202929650002902961650003202990650002903022700007703051700009203128700007803220710003403298773002603332776003603358776003603394776003603430830006203466856004603528978-3-031-63053-8DE-He21320251030112334.0cr nn 008mamaa240626s2024    sz |    s    |||| 0|eng d  a97830316305387 a10.1007/978-3-031-63053-82doi 4aQ342 7aUYQ2bicssc 7aCOM0040002bisacsh 7aUYQ2thema1 aCuevas, Erik.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aMetaheuristic Algorithms: New Methods, Evaluation, and Performance Analysish[electronic resource] /cby Erik Cuevas, Alberto Luque, Bernardo Morales Castañeda, Beatriz Rivera.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXV, 297 p. 83 illus., 39 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aStudies in Computational Intelligence,x1860-9503 ;v11630 a -- 1. Introduction to Metaheuristic methods.  -- 2. A novel method for initializing populations using the Metropolis-Hastings (MH) technique.  -- 3. A measure of diversity for metaheuristic algorithms employing population-based approaches.  -- 4. Population Control in Metaheuristic Algorithms: Can Fewer Be Better?.  -- 5. Exploration Paths Derived from Trajectories Extracted from Second-Order System Responses.  -- 6. Utilizing the Moth Swarm Algorithm to Improve Image Contrast.  -- 7. Enhancing Anisotropic Diffusion Filtering via Multi-Objective Optimization.  -- 8. Fractional Fuzzy Controller Calibration Using metaheuristic Techniques.  -- 9. Striving for Optimal Equilibrium in Metaheuristic Algorithms: Is It Attainable?.  aThis book encompasses three distinct yet interconnected objectives. Firstly, it aims to present and elucidate novel metaheuristic algorithms that feature innovative search mechanisms, setting them apart from conventional metaheuristic methods. Secondly, this book endeavors to systematically assess the performance of well-established algorithms across a spectrum of intricate and real-world problems. Finally, this book serves as a vital resource for the analysis and evaluation of metaheuristic algorithms. It provides a foundational framework for assessing their performance, particularly in terms of the balance between exploration and exploitation, as well as their capacity to obtain optimal solutions. Collectively, these objectives contribute to advancing our understanding of metaheuristic methods and their applicability in addressing diverse and demanding optimization tasks. The materials were compiled from a teaching perspective. For this reason, the book is primarily intended for undergraduate and postgraduate students of Science, Electrical Engineering, or Computational Mathematics. Additionally, engineering practitioners who are not familiar with metaheuristic computation concepts will appreciate that the techniques discussed are beyond simple theoretical tools because they have been adapted to solve significant problems that commonly arise in engineering areas. 0aComputational intelligence. 0aArtificial intelligence.14aComputational Intelligence.24aArtificial Intelligence.1 aLuque, Alberto.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut1 aMorales Castañeda, Bernardo.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut1 aRivera, Beatriz.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303163052108iPrinted edition:z978303163054508iPrinted edition:z9783031630552 0aStudies in Computational Intelligence,x1860-9503 ;v116340uhttps://doi.org/10.1007/978-3-031-63053-8