Constraint Handling in Cohort Intelligence Algorithm
portes grátis
Constraint Handling in Cohort Intelligence Algorithm
Kulkarni, Anand J.; Kale, Ishaan R.
Taylor & Francis Ltd
12/2021
200
Dura
Inglês
9781032150758
15 a 20 dias
421
Descrição não disponível.
Chapter 1: Introduction to Metaheuristic Algorithms
Chapter 2: Literature Survey on Nature Inspired Optimisation Methodologies and Constraint Handling
Chapter 3: Cohort Intelligence (CI) Using the Static Penalty Function (SPF) Approach
Chapter 4: Constraint Handling Using the Self-Adaptive Penalty Function (SAPF) Approach
Chapter 5: Hybridization of Cohort Intelligence with Colliding Bodies Optimisation
Chapter 6: Validation of CI-SPF, CI-SAPF and CI-SAPF-CBO for Solving Discrete/Integer and Mixed Variable Problems
Chapter 7: Solution to Real-World Applications
Chapter 8: Conclusions and Recommendations
Appendix: Problem Statements for the Truss Structure, Design Engineering, Linear and Nonlinear Programming and Manufacturing Problems
Index
Chapter 2: Literature Survey on Nature Inspired Optimisation Methodologies and Constraint Handling
Chapter 3: Cohort Intelligence (CI) Using the Static Penalty Function (SPF) Approach
Chapter 4: Constraint Handling Using the Self-Adaptive Penalty Function (SAPF) Approach
Chapter 5: Hybridization of Cohort Intelligence with Colliding Bodies Optimisation
Chapter 6: Validation of CI-SPF, CI-SAPF and CI-SAPF-CBO for Solving Discrete/Integer and Mixed Variable Problems
Chapter 7: Solution to Real-World Applications
Chapter 8: Conclusions and Recommendations
Appendix: Problem Statements for the Truss Structure, Design Engineering, Linear and Nonlinear Programming and Manufacturing Problems
Index
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Pseudo-objective Function;Pressure Vessel Design Problem;Mixed Variable Problems;Mixed Design Variables;Constraint Handling Techniques;Penalty Parameter;Design Engineering Domain;Constraint Handling;Average Function Evaluations;Constraint Handling Approach;Dynamic Penalty Function;Roulette Wheel Approach;Cohort Candidates;Penalty Function;Fa;Truss Structure;Convergence Curve;Average Cpu Time;Infeasible Solution;SPF;PSO;Violated;Teaching Learning Based Optimisation;Average Standard Deviation;Penalty Function Approach
Chapter 1: Introduction to Metaheuristic Algorithms
Chapter 2: Literature Survey on Nature Inspired Optimisation Methodologies and Constraint Handling
Chapter 3: Cohort Intelligence (CI) Using the Static Penalty Function (SPF) Approach
Chapter 4: Constraint Handling Using the Self-Adaptive Penalty Function (SAPF) Approach
Chapter 5: Hybridization of Cohort Intelligence with Colliding Bodies Optimisation
Chapter 6: Validation of CI-SPF, CI-SAPF and CI-SAPF-CBO for Solving Discrete/Integer and Mixed Variable Problems
Chapter 7: Solution to Real-World Applications
Chapter 8: Conclusions and Recommendations
Appendix: Problem Statements for the Truss Structure, Design Engineering, Linear and Nonlinear Programming and Manufacturing Problems
Index
Chapter 2: Literature Survey on Nature Inspired Optimisation Methodologies and Constraint Handling
Chapter 3: Cohort Intelligence (CI) Using the Static Penalty Function (SPF) Approach
Chapter 4: Constraint Handling Using the Self-Adaptive Penalty Function (SAPF) Approach
Chapter 5: Hybridization of Cohort Intelligence with Colliding Bodies Optimisation
Chapter 6: Validation of CI-SPF, CI-SAPF and CI-SAPF-CBO for Solving Discrete/Integer and Mixed Variable Problems
Chapter 7: Solution to Real-World Applications
Chapter 8: Conclusions and Recommendations
Appendix: Problem Statements for the Truss Structure, Design Engineering, Linear and Nonlinear Programming and Manufacturing Problems
Index
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Pseudo-objective Function;Pressure Vessel Design Problem;Mixed Variable Problems;Mixed Design Variables;Constraint Handling Techniques;Penalty Parameter;Design Engineering Domain;Constraint Handling;Average Function Evaluations;Constraint Handling Approach;Dynamic Penalty Function;Roulette Wheel Approach;Cohort Candidates;Penalty Function;Fa;Truss Structure;Convergence Curve;Average Cpu Time;Infeasible Solution;SPF;PSO;Violated;Teaching Learning Based Optimisation;Average Standard Deviation;Penalty Function Approach
