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{ "item_title" : "Handbook of Whale Optimization Algorithm", "item_author" : [" Seyedali Mirjalili "], "item_description" : "Handbook of Whale Optimization Algorithm: Variants, Hybrids, Improvements, and Applications provides the most in-depth look at an emerging meta-heuristic that has been widely used in both science and industry. Whale Optimization Algorithm has been cited more than 5000 times in Google Scholar, thus solving optimization problems using this algorithm requires addressing a number of challenges including multiple objectives, constraints, binary decision variables, large-scale search space, dynamic objective function, and noisy parameters to name a few. This handbook provides readers with in-depth analysis of this algorithm and existing methods in the literature to cope with such challenges. The authors and editors also propose several improvements, variants and hybrids of this algorithm. Several applications are also covered to demonstrate the applicability of methods in this book.", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/0/32/395/365/0323953654_b.jpg", "price_data" : { "retail_price" : "180.00", "online_price" : "180.00", "our_price" : "180.00", "club_price" : "180.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Handbook of Whale Optimization Algorithm|Seyedali Mirjalili

Handbook of Whale Optimization Algorithm : Variants, Hybrids, Improvements, and Applications

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Overview

Handbook of Whale Optimization Algorithm: Variants, Hybrids, Improvements, and Applications provides the most in-depth look at an emerging meta-heuristic that has been widely used in both science and industry. Whale Optimization Algorithm has been cited more than 5000 times in Google Scholar, thus solving optimization problems using this algorithm requires addressing a number of challenges including multiple objectives, constraints, binary decision variables, large-scale search space, dynamic objective function, and noisy parameters to name a few. This handbook provides readers with in-depth analysis of this algorithm and existing methods in the literature to cope with such challenges.

The authors and editors also propose several improvements, variants and hybrids of this algorithm. Several applications are also covered to demonstrate the applicability of methods in this book.

This item is Non-Returnable

Details

  • ISBN-13: 9780323953658
  • ISBN-10: 0323953654
  • Publisher: Academic Press
  • Publish Date: November 2023
  • Dimensions: 11 x 8.5 x 1.38 inches
  • Shipping Weight: 3.45 pounds
  • Page Count: 686

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