Optimization Using Evolutionary Algorithms and Metaheuristics: Applications in Engineering (Science, Technology, and Management)

Description

Metaheuristic optimization is a higher-level procedure or heuristic designed to find, generate, or select a heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information or limited computation capacity. This is usually applied when two or more objectives are to be optimized simultaneously. This book is presented with two major objectives. Firstly, it features chapters by eminent researchers in the field providing the readers about the current status of the subject. Secondly, algorithm-based optimization or advanced optimization techniques, which are applied to mostly non-engineering problems, are applied to engineering problems. This book will also serve as an aid to both research and industry. Usage of these methodologies would enable the improvement in engineering and manufacturing technology and support an organization in this era of low product life cycle. Features: Covers the application of recent and new algorithms Focuses on the development aspects such as including surrogate modeling, parallelization, game theory, and hybridization Presents the advances of engineering applications for both single-objective and multi-objective optimization problems Offers recent developments from a variety of engineering fields Discusses Optimization using Evolutionary Algorithms and Metaheuristics applications in engineering   Cover Half Title Series Title Copyright Contents Preface Editor Biography Section I State of the Art 1 Some Metaheuristic Optimization Schemes in Design Engineering Applications Section II Application to Design and Manufacturing 2 AGV Routing via Ant Colony Optimization Using C# 3 Data Envelopment Analysis: Applications to the Manufacturing Sector 4 Optimization of Process Parameters for Electrical Discharge Machining of Al7075-B4C and TiC Hybrid Composite Using ELECTRE Method 5 Selection of Laser Micro-drilling Process Parameters Using Novel Bat Algorithm and Bird Swarm Algorithm Section III Application to Energy Systems 6 Energy Demand Management of a Residential Community through Velocity-Based Artificial Colony Bee Algorithm 7 Adaptive Neuro-fuzzy Inference System (ANFIS) Modelling in Energy System and Water Resources Index