How to Design Optimization Algorithms by Applying Natural Behavioral Patterns

CONTENTS
Cover
Title
Copyright
End User License Agreement
Contents
Foreword
Preface
Dedication
Introduction to Optimization
Nature and Optimization Algorithms
How to Formulate Natural Ideas in Several Algorithms
	3.1. SSPCO ALGORITHM (SEE-SEE PARTRIDGE)
	3.2. SIMULATION RESULTS
	3.3. CHAOTIC SSPCO ALGORITHM
	3.4. THE FIRST METHOD: CHAOTIC SSPCO ALGORITHM
	3.5. THE SECOND EQUATION: CHAOTIC SSPCO
	3.6. SIMULATION AND RESULTS
	3.7. DATA CLUSTERING USING BY CHAOTIC SSPCO ALGORITHM
	3.8. FITNESS FUNCTION
	3.9. EXPERIMENTAL STUDY
	3.10. RESULTS AND DISCUSSIONS
	3.11. T-TEST
	3.12. FRIEDMAN TEST
	3.13. A CLUSTERING APPROACH BY SSPCO OPTIMIZATION ALGORITHM BASED ON CHAOTIC INITIAL POPULATION
	3.14. FITNESS FUNCTION
	3.15. EXPERIMENTAL STUDY
	3.16. RESULTS AND DISCUSSIONS
	3.17. T-TEST
	3.18. TRAVELING SALESMAN PROBLEM
	3.19. SIMULATION AND RESULTS
	3.20. ESCAPE FROM HUNTER PARTICLE SWARM OPTIMIZATION
	3.21. EXPERIMENTAL RESULT
	3.22. PROVIDING A BIRD ALGORITHM BASED ON CLASSICAL CONDITIONING LEARNING BEHAVIOUR
	3.23. SIMULATION AND RESULTS
How to Transform the Behaviour in Nature into the Algorithm
	4.1. CHAOTIC INTIAL POPULATION
	4.2. CHAOTIC CREATION OF FOOD SOURCES
Conclusion
References
	REFERENCES
Subject Index
Back Cover