A Novel Design of Optimization Algorithm Based on Optimization Problems and its Application on Real Life Problems

Optimization: Algorithms and Applications present a variety of solution techniques for optimization functions, emphasizing concepts rather than rigorous mathematical details. The book covers the stochastic method as a solution method for optimization functions. It discusses hypercube evaluation as optimization algorithm. The author shows how to solve benchmark functions, unimodal functions, simple multimodal functions, hybrid functions, and composition functions using the basic of hypercube optimization search algorithm (HOS) and simple modifications of the basic HOS+ code. The book also introduces a novel method using random perturbation (RP) into two control parameters to solve a wide variety of optimization functions?one of the first optimization book to do so?and develops software code for the proposed algorithm. In addition, it examines neural network models, educational institution problems and warehouse assignments. The author follows a step-by-step approach to developing the MATLAB codes from the proposed algorithm. The author then applies the codes to solve optimization functions taken from the literature and real-world applications, including portfolio optimization functions, medical data classification problems, timetabling problems, and storage location assignments over time to sustain a high level of picking (access) performance?stocking products in an optimized the assignment of products to storage locations. This hands-on approach improves your understanding and confidence in handling different solution methods.