Soft computing techniques for engineering optimization

Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Authors -- Section I: Introduction to Soft Computing Techniques -- 1. Introduction to Optimization and Relevance of Soft Computing towards Optimal Solution -- 1.1 Optimization -- 1.2 Single-Objective Mathematical Programming -- 1.3 Generalized Reduced Gradient Method -- 1.4 Derivative-Based Optimization -- 1.4.1 Descent Methods -- 1.4.2 Gradient-Based Method -- 1.4.3 Steepest Descent Method -- 1.4.4 Newton's Method (Classical Newton's Method) -- 1.5 Soft Computing -- 1.5.1 SC - How Is It Different from Hard Computing? -- 1.6 Relevance of SC towards Optimal Solution -- 2. Various Soft Computing Techniques and Their Description -- 2.1 History of Heuristics/Search Algorithms -- 2.2 Soft Computing Approaches - Derivative-Free Optimisation -- 2.2.1 Genetic Algorithms -- 2.2.1.1 Encoding Schemes -- 2.2.1.2 Fitness Evaluation -- 2.2.1.3 Selection -- 2.2.1.4 Crossover -- 2.2.1.5 Mutation -- 2.2.1.6 Termination -- 2.2.1.7 Elitism -- 2.2.2 Simulated Annealing -- 2.2.2.1 Terminologies of SA -- 2.2.2.2 Annealing Schedule -- 2.2.2.3 Stepwise Procedure of SA -- 2.2.3 Random Search -- 2.2.4 Downhill Simplex Search -- 2.3 Stochastic Optimization - Recent Advancement -- 2.3.1 Particle Swarm Optimization -- 2.3.1.1 Discussion of PSO over GA in Soft Computing Platform -- 2.3.2 Ant Colony Optimization -- 2.3.2.1 ACO - How It Works in Any Optimization Issue? -- 2.3.2.2 ACO - Sequential Steps -- 2.3.2.3 ACO - Meta-Heuristics Approach -- 2.4 Hybridization -- Section II: Various Case Studies Comprising Industrial Problems and Their Optimal Solutions Using Different Techniques -- 3. Single-Objective Optimization Using Response Surface Methodology -- 3.1 Introduction -- 3.2 Experimentation, Result and Discussion.