Bio-Inspired Metaheuristics Search Algorithms for Optimizing Manufacturing Process
Traditional search methods used to solve manufacture engineering optimization problems fail to find the global optimum when dealing with a complex objective function and a large number of decision variables. Bio-inspired metaheuristics search algorithms shows significant performance in handling multi-model function optimization with many constraints. They can efficiently explore the search space and produce results that are more global in nature. Compared with other metaheuristic algorithm, the CSA has the advantage of less parameter setting and easy implementation. Two cutting tools Cases study of engineering problems are used to demonstrate the CSA. The results of case studies show the CSA perform a promising result in engineering optimization problem. A performance comparison with genetic algorithms, particle swarm optimization and cuckoo search is also presented in the paper and shows the ascendancy with better convergence rate.
Keywords - Crow Search Algorithm, Engineering Optimization Problem, Parameter Optimization, Cutting Tools