Advances in Metaheuristics Algorithms: Methods and Applications

1. Front Matter

   Pages i-xiv

   PDF

2. Introduction

   • Erik Cuevas, Daniel Zaldívar, Marco Pérez-Cisneros

   Pages 1-8

3. The Metaheuristic Algorithm of the Social-Spider

   • Erik Cuevas, Daniel Zaldívar, Marco Pérez-Cisneros

   Pages 9-33

4. Calibration of Fractional Fuzzy Controllers by Using the Social-Spider Method

   • Erik Cuevas, Daniel Zaldívar, Marco Pérez-Cisneros

   Pages 35-55

5. The Metaheuristic Algorithm of the Locust-Search

   • Erik Cuevas, Daniel Zaldívar, Marco Pérez-Cisneros

   Pages 57-76

6. Identification of Fractional Chaotic Systems by Using the Locust Search Algorithm

   • Erik Cuevas, Daniel Zaldívar, Marco Pérez-Cisneros

   Pages 77-92

7. The States of Matter Search (SMS)

   • Erik Cuevas, Daniel Zaldívar, Marco Pérez-Cisneros

   Pages 93-118

8. Multimodal States of Matter Search

   • Erik Cuevas, Daniel Zaldívar, Marco Pérez-Cisneros

   Pages 119-165

9. Metaheuristic Algorithms Based on Fuzzy Logic

   • Erik Cuevas, Daniel Zaldívar, Marco Pérez-Cisneros

   Pages 167-218

This book explores new alternative metaheuristic developments that have proved to be effective in their application to several complex problems. Though most of the new metaheuristic algorithms considered offer promising results, they are nevertheless still in their infancy. To grow and attain their full potential, new metaheuristic methods must be applied in a great variety of problems and contexts, so that they not only perform well in their reported sets of optimization problems, but also in new complex formulations. The only way to accomplish this is to disseminate these methods in various technical areas as optimization tools. In general, once a scientist, engineer or practitioner recognizes a problem as a particular instance of a more generic class, he/she can select one of several metaheuristic algorithms that guarantee an expected optimization performance. Unfortunately, the set of options are concentrated on algorithms whose popularity and high proliferation outstrip thoseof the new developments. This structure is important, because the authors recognize this methodology as the best way to help researchers, lecturers, engineers and practitioners solve their own optimization problems.

• Evolutionary Computation
• Swarm Algorithms
• Metaheuristics
• Metaheuristic Methods
• Social Spider Optimization

• CUCEI, Universidad de Guadalajara, Guadalajara, Mexico

  Erik Cuevas, Daniel Zaldívar, Marco Pérez-Cisneros

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